Intelligent joint prosthesis

Abstract

The invention relates to a smart joint prosthesis. Medical devices coupled to sensors, and systems including such devices, may generate data and analytics based on this data, which may be used to identify and / or address issues associated with implanting a medical device, including incorrect placement of the device, accidental degradation of the device, and undesired movement of the device. Also provided are medical devices coupled to sensors, as well as devices and methods of addressing problems identified by implanting a medical device.

Description

Cross - Reference to Related Applications

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 858,277, filed Jun. 6, 2019, under 35 U.S.C. § 119(e), the entire disclosure of which is incorporated herein by reference for all purposes. Technical Field

[0002] The present invention generally relates to sensor - containing medical devices, systems including such devices, methods of using such devices and systems, and data generated thereby, and to devices and methods for addressing problems associated with sensor - containing implantable medical devices. Background Art

[0003] Medical devices and implants are common in modern medicine. Generally, medical devices and implants are manufactured to replace, support, or augment an anatomical or biological structure. When a medical device is located on the surface of a patient's body, the device is readily visible to the patient and the attending healthcare professional. However, when a medical device is designed to be implanted within a patient, i.e., an implantable medical device or medical implant, it is generally not readily visible.

[0004] Examples of medical implants include orthopedic implants such as hip, shoulder, and knee prostheses; spinal implants (spinal cages and artificial discs) and spinal hardware (screws, plates, pins, rods); intrauterine devices; orthopedic hardware for repairing fractures and soft tissue injuries (casts, braces, tensor bandages, plates, screws, wires, dynamic hip screws, pins, and plates); cochlear implants; aesthetic implants (breast implants, fillers); and dental implants.

[0005] Using the knee as a specific example, current prosthetic systems for total knee arthroplasty (TKA) typically consist of up to five components: a femoral component, a tibial component, a tibial insert, a tibial stem extension, and a patellar component, which five components may be collectively referred to as a total knee implant (TKI). These components are designed to work together as a functional unit to replace and provide the functions of the natural knee joint. The femoral component attaches to the femoral head of the knee joint and forms the upper articular surface. The tibial insert (also called a spacer) is typically composed of a polymer and forms the lower articular surface with the metal femoral head. The tibial component consists of a tibial stem that is inserted into the tibial medullary cavity and a baseplate, which is sometimes referred to as a tibial plate, tibial tray, or tibial baseplate that contacts / fixes the tibial insert. Optionally and particularly in cases where the proximal tibial bone quality and / or bone mass is compromised, a tibial stem extension may be added to the tibial stem of the tibial component, where the tibial stem extension serves as a keel to prevent the tibial component from tilting and to increase stability. Commercial examples of TKA products include Persona, all provided by Zimmer Biomet Inc. (Warsaw, Indiana, USA)TM Knee system (I113369) and related tapered tibial stem extension (K133737). The surgery to implant these four components into a patient is also known as total knee replacement (TKR). Similar prosthetic devices can be used for other joints, such as total hip arthroplasty (THA) and total shoulder arthroplasty (TSA), where one joint surface is metallic and the opposing surface is polymeric. In general, these devices and surgeries (TKA, THA, and TSA) are commonly referred to as total joint arthroplasty (TJA).

[0006] For TKA, the tibial component and the femoral component are typically inserted into the tibia and femur respectively and cemented in place within the tibia and femur. In some cases, the components are not cemented in place, such as in non-cemented knees. Whether or not they are cemented in place, once placed and integrated into the surrounding bone (a process called osseointegration), they are not easily removed. Therefore, proper placement of these components during implantation is very important for a successful surgical outcome, and surgeons are very careful when implanting and fixing these components.

[0007] Current commercially available TKA systems have a long history of clinical use, with implant durations typically exceeding 10 years and some reports supporting an 87% survival rate at 25 years. Currently, clinicians use a series of physical examinations at 2 - 3 weeks, 6 - 8 weeks, 3 months, 6 months, 12 months, and annually thereafter to monitor the progress of TKA patients after implantation.

[0008] After implanting a TKI and the patient begins walking with the knee prosthesis, problems may arise and are sometimes difficult to identify. The ability of clinical examinations to detect prosthesis failure is usually limited; therefore, additional monitoring such as CT scans, MRI scans, or even nuclear scans are often required. Given the continuous care requirements throughout the implant's life cycle, patients are encouraged to see their clinicians annually to check their health, monitor other joints, and evaluate the function of the TKA implant. While current standards of care enable clinicians and the healthcare system to evaluate the TKA function of patients during a 90-day period of care, the measurements are usually subjective and lack the temporal resolution to describe small changes in function, which may be precursors to larger mobility problems. Long-term (>1 year) follow-up of TKA patients also presents a problem in that patients do not see their clinicians annually. Instead, they typically only seek additional consultation when pain or other symptoms arise.

[0009] Currently, without clinical access and the hands and visual observation of an experienced healthcare provider, there is no mechanism for reliably detecting malposition, instability, or misalignment in a TKA. Even so, early identification of subclinical problems or conditions is difficult or impossible because they are typically too subtle to be detected during a physical examination or demonstrated by radiological studies. Additionally, if detection is possible, corrective action will be hindered by the fact that specific amounts of movement and / or degrees of improper alignment cannot be accurately measured or quantified, precluding targeted and successful intervention. Existing external monitoring devices do not provide the fidelity required to detect instability because these devices are separated from the TKA by skin, muscle, and fat - each of which masks the mechanical signature of instability and introduces anomalies such as bending, tissue-propagated acoustic noise, inconsistent sensor placement on the surface, and inconsistent positioning of the external sensor relative to the TKA.

[0010] Implants other than TKA implants can also be associated with various complications, either during implantation or after surgery. Generally, correctly placing a medical implant can be challenging for a surgeon, and various complications can occur during the insertion of any medical implant, whether by open surgery or minimally invasive surgery. For example, a surgeon may wish to confirm the correct anatomical alignment and placement of the implant within the surrounding tissues and structures. However, this can be difficult to do during the surgery itself, making intraoperative corrective adjustments difficult.

[0011] In addition, a patient may experience many complications after surgery. Such complications include neurological symptoms, pain, failure of the implant (blockage, loosening, etc.) and / or wear, movement or breakage of the implant, inflammation, and / or infection. While some of these problems can be addressed with medications and / or additional surgery, they are difficult to predict and prevent; generally, early identification of complications and side effects, while desired, is difficult or impossible.

[0012] The present disclosure aims to identify, locate, and / or quantify these problems, particularly in the early stages, and provide methods and devices to remedy these problems.

[0013] All topics discussed in the background section are not necessarily prior art and should not be assumed to be prior art merely because of their discussion in the background section. Along these lines, unless explicitly stated as prior art, any recognition of problems in the prior art discussed in the background section or related to such topics should not be regarded as prior art. On the contrary, the discussion of any topic in the background section should be considered part of the inventors' approach to solving a particular problem, which may itself be creative. Summary of the Invention

[0014] In short, the present disclosure relates to intelligent implants, systems including intelligent implants, methods for using the implants / systems to detect, locate, quantify, and / or characterize at least one of problems related to the implants, and methods and devices for solving the identified problems. As provided in more detail below, the present disclosure provides medical devices coupled to sensors, and systems including such devices, which can generate data and analyses based on the data, which can be used to identify and / or solve problems related to implanted medical devices. In one embodiment, the medical device is a total joint arthroplasty (TJA) and the data is kinematic data reflecting the movement of the TJA. Problems that may be identified include misplacement of the TJA device, incorrect alignment of the device, unexpected deterioration or wear of the device, instability of the device (and associated joint), and unwanted movement of the device. Also provided are medical devices coupled to sensors, and devices and methods for solving the identified problems of implanted medical devices.

[0015] A medical device coupled to a sensor can be referred to as an intelligent implant, where the intelligent implant will include sensors that can detect and / or measure the function of the implant and / or the immediate environment around the implant and / or the activity / movement of the implant as well as the activity and movement of the patient. The implant can alternatively be referred to herein as a prosthesis, where intelligent implant and intelligent prosthesis have the same meaning. In one embodiment, the sensor is coupled to the medical device, such as a prosthesis / implant, such that the sensor is entirely located within the medical device, such that the sensor is completely surrounded by the outer surface of the medical device, and thus no part of the sensor makes physical contact with any tissue of the patient in whom the medical device is implanted. In embodiments of the present disclosure, a reference herein to a medical device or implant or prosthesis can be understood to refer to an intelligent medical device or implant / prosthesis having a sensor entirely located within the medical device or implant / prosthesis as disclosed herein. In embodiments of the present disclosure, a reference herein to a medical device or implant or prosthesis having a sensor should be understood to refer to an intelligent medical device or implant / prosthesis, where the sensor is entirely located within the medical device or implant / prosthesis. In embodiments of the present disclosure, a reference herein to a medical device or implant / prosthesis having a sensor should be understood to refer to an intelligent medical device or implant / prosthesis, where the sensor is one accelerometer or more than one accelerometer (e.g., two, three, four, five, six, seven, etc. accelerometers) that is entirely located within the medical device or implant / prosthesis. In embodiments of the present disclosure, a reference herein to a medical device or implant / prosthesis having a sensor should be understood to refer to an intelligent medical device or implant / prosthesis, where the sensor is one or more accelerometers (e.g., two, three, four, five, six, seven, etc. accelerometers) that are entirely located within the medical device, implant / prosthesis, such that the medical device or implant / prosthesis is a component such as a TKA.

[0016] The system will include an intelligent implant and one or more of the following: a memory storing data from such detections and / or measurements, an antenna transmitting such data; a base station that receives data generated by the sensors and may transmit the data and / or the analyzed data to a cloud-based location; a cloud-based location where data may be stored and analyzed, and where the analyzed data may be stored and / or further analyzed; and a receiving station receiving outputs from the cloud-based location, where the receiving station may be accessed by, such as, a healthcare professional or an insurance company or a manufacturer of the implant, and the outputs may identify the status of the implant and / or the functionality of the implant and / or the status of the patient receiving the implant, and may also provide recommendations for solving any problems arising from the analysis of the raw data.

[0017] For example, instability in total joint arthroplasty (such as TKA, THA, and TSA) hardware can lead to bone erosion and accelerated fatigue of the implant components. If untreated or uncorrected, bone erosion and accelerated fatigue typically result in pain and inflammation. By the time pain and inflammation prompt a total joint arthroplasty (TJA) patient to seek medical care, the extent of bone erosion and TJA fatigue may leave healthcare professionals with only one option: highly invasive and expensive surgery, and a reduced probability of a "successful" outcome. The present disclosure provides devices, systems, and methods that are capable of detecting instability in TJA hardware early, before bone erosion and implant fatigue damage occur. Such instability can be detected, quantified, and characterized, and the results communicated to healthcare providers to allow early treatment and / or more effective treatment of the problem, i.e., the healthcare providers can utilize less invasive, less expensive, and more likely successful corrective treatments. The present disclosure also provides devices and / or methods for solving instability problems.

[0018] The present disclosure relates to TJA (total joint arthroplasty), the term including reference to the surgery and associated implant hardware such as the TJA prosthesis of the present disclosure. The features of the methods, devices, and systems of the present disclosure may be illustrated herein by reference to a particular intelligent TJA prosthesis, however, the present disclosure should be understood to be applicable to any one or more TJA prostheses, including TKA (total knee arthroscopy) prostheses, such as TKI (total knee implant), which may also be referred to as a TKA system; TSA (total shoulder arthroscopy) prostheses, such as TSI (total shoulder implant), which may also be referred to as a TSI system; and THA (total hip arthroscopy) prostheses, such as THI (total hip implant), which may also be referred to as a THA system. In one embodiment, the TJA prosthesis is an intelligent TJA, also referred to as an intelligent TJA prosthesis, which has at least one sensor as disclosed herein.

[0019] This brief overview is provided to introduce certain concepts in a simplified form that are further described in detail below. Unless otherwise expressly stated, this brief overview is not intended to identify key or essential features of the claimed subject matter nor is it intended to limit the scope of the claimed subject matter.

[0020] Some exemplary numbered embodiments of the present disclosure are as follows: 1. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the medial side of the implant compared to the lateral side. 2. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the lateral side of the implant compared to the medial side. 3. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the anterior side of the implant compared to the posterior side. 4. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the posterior side of the implant compared to the anterior side. 5. A tibial insert / articular spacer / for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thick on the medial, lateral, anterior, and / or posterior side of the implant. 6. The tibial insert according to any one of embodiments 1 - 5, wherein the tibial insert is made of polyethylene or polyetheretherketone (PEEK). 7. The tibial insert according to any one of embodiments 1 - 6, wherein the tibial insert is customized for a patient. 8. The tibial insert according to any one of embodiments 1 to 7, wherein the insert is manufactured by 3 - D printing or by molding. 9. An implantable medical device, comprising: a circuit configured to be fixedly attached to an implantable prosthesis device; a power component; and a device configured to decouple the circuit from the power component. 10. An implantable medical device, comprising: a circuit configured to be fixedly attached to an implantable prosthesis device; a battery; and a fuse coupled between the circuit and the battery. 11. A method, comprising electrically disconnecting a fuse disposed between a circuit and a battery, wherein at least the fuse and the circuit are disposed on an implanted prosthesis device. 12. An implantable medical device, comprising: at least one sensor configured to generate a sensor signal; and a control circuit configured to cause the at least one sensor to generate the sensor signal at a frequency associated with a telemedicine code. 13. An implantable medical device, comprising: at least one sensor configured to generate a sensor signal; and a control circuit configured to cause the at least one sensor to generate the sensor signal at a frequency that allows a doctor to be eligible for payment according to a telemedicine insurance code. 14. An implantable medical device, comprising: at least one sensor configured to generate a sensor signal; and a control circuit configured to cause the at least one sensor to generate the sensor signal at a frequency that allows a doctor to be eligible for full payment according to a telemedicine insurance code. 15. A method, comprising generating a sensor signal associated with an implantable medical device at a frequency that allows a doctor to be eligible for payment according to a telemedicine insurance code. 16. A method, comprising generating a sensor signal associated with an implantable medical device at a frequency that allows a doctor to be eligible for full payment according to a telemedicine insurance code. 17. An implantable prosthesis, comprising: a housing; and an implantable circuit disposed in the housing and configured to generate at least one first signal representative of movement; determine whether the signal meets at least one first criterion; and in response to determining that the signal meets at least one first criterion, send the signal to a remote location. 18. A base station, comprising: a housing; and a base station circuit disposed in the housing and configured to receive at least a first signal representative of movement from an implantable prosthesis; send at least one first signal to a destination; receive at least one second signal from a source; and send at least one second signal to the implantable prosthesis. 19. A method, comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to a current through the fuse exceeding an overcurrent threshold. 20. A method, comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to a current through the fuse exceeding an overcurrent threshold for at least a threshold time. 21. A method includes disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to a voltage across the fuse exceeding an overvoltage threshold. 22. A method includes disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to a voltage across the fuse exceeding an overvoltage threshold for at least a threshold time. 23. A method includes disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to a temperature exceeding an overtemperature threshold. 24. A method includes disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to a temperature exceeding an overtemperature threshold for at least a threshold duration. 25. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; and transmitting the sensor signal to a remote location. 26. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; sampling the sensor signal; and transmitting the sample to a remote location. 27. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; determining whether the sensor signal represents a qualified event; and transmitting the signal to a remote location in response to determining that the sensor signal represents a qualified event. 28. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; receiving a polling signal from a remote location; and transmitting the sensor signal to a remote location in response to the polling signal. 29. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; generating a message including the sensor signal or data representative of the sensor signal; and transmitting the message to a remote location. 30. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; generating a data packet including the sensor signal or data representative of the sensor signal; and transmitting the data packet to a remote location. 31. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; encrypting at least a portion of the sensor signal or data representative of the sensor signal; and transmitting the encrypted sensor signal to a remote location. 32. A method includes: generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; encoding at least a portion of the sensor signal or data representative of the sensor signal; and transmitting the encoded sensor signal to a remote location. 33. A method comprising: generating a sensor signal in response to movement of a subject in which a prosthesis is implanted; transmitting the sensor signal to a remote location; and after transmitting the sensor signal, placing an implantable circuit associated with the prosthesis into a low power mode. 34. A method comprising: generating a first sensor signal in response to movement of a subject in which a prosthesis is implanted; transmitting the first sensor signal to a remote location; after transmitting the sensor signal, placing at least one component of an implantable circuit associated with the prosthesis into a low power mode; and after a period of time in the low power mode of the implantable circuit has elapsed, generating a second sensor signal in response to movement of the subject. 35. A method comprising: receiving a sensor signal from a prosthesis implanted in a subject; and transmitting the received sensor signal to a destination. 36. A method comprising: sending a request to a prosthesis implanted in a subject, receiving a sensor signal from the prosthesis after sending the request; and transmitting the received sensor signal to a destination. 37. A method comprising: receiving a sensor signal and at least one identifier from a prosthesis implanted in a subject; determining whether the identifier is correct; and in response to determining that the identifier is correct, transmitting the received sensor signal to a destination. 38. A method comprising: receiving a message comprising a sensor signal from a prosthesis implanted in a subject; decrypting at least a portion of the message; and transmitting the decrypted message to a destination. 39. A method comprising: receiving a message comprising a sensor signal from a prosthesis implanted in a subject; decoding at least a portion of the message; and transmitting the decoded message to a destination. 40. A method comprising: receiving a message comprising a sensor signal from a prosthesis implanted in a subject; encoding at least a portion of the message; and transmitting the encoded message to a destination. 41. A method comprising: receiving a message comprising a sensor signal from a prosthesis implanted in a subject; encrypting at least a portion of the message; and transmitting the encrypted message to a destination. 42. A method comprising: receiving a data packet comprising a sensor signal from a prosthesis implanted in a subject; decrypting at least a portion of the data packet; and transmitting the decrypted data packet to a destination. 43. A method comprising: receiving a data packet comprising a sensor signal from a prosthesis implanted in a subject; decoding at least a portion of the data packet; and transmitting the decoded data packet to a destination. 44. A method comprising: receiving a data packet comprising a sensor signal from a prosthesis implanted in a subject; encoding at least a portion of the data packet; and transmitting the encoded data packet to a destination. 45. A method comprising: receiving a data packet including a sensor signal from a prosthesis implanted in a subject; encrypting at least a portion of the data packet; and transmitting the encrypted data packet to a destination. 46. A method comprising: receiving a sensor signal from a prosthesis implanted in a subject; decrypting at least a portion of the sensor signal; and transmitting the decrypted sensor signal to a destination. 47. A method comprising: receiving a sensor signal from a prosthesis implanted in a subject; decoding at least a portion of the sensor signal; and transmitting the decoded sensor signal to a destination. 48. A method comprising: receiving a sensor signal from a prosthesis implanted in a subject; encoding at least a portion of the sensor signal; and transmitting the encoded sensor signal to a destination. 49. A method comprising: receiving a sensor signal from a prosthesis implanted in a subject; encrypting at least a portion of the sensor signal; and transmitting the encrypted sensor signal to a destination. 50. An implantable circuit for an implantable prosthesis. 51. An implantable prosthesis comprising an implantable circuit. 52. An implantable prosthesis comprising a fuse. 53. A base station for communicating with an implantable prosthesis. 54. A monitoring - session - data collection, analysis, and status - reporting system implemented as components of one or more computer systems, each computer system having one or more processors, one or more memories, one or more network connections, and access to one or more mass storage devices, the one or more monitoring - session - data collection, data - analysis, and status - reporting systems comprising: A monitoring - session - data - receiving component that receives monitoring - session - data from an external monitoring - session - data source, including acceleration data generated by sensors internal or proximal to a prosthesis attached or implanted within a patient, and stores the received monitoring - session - data in one or more of one or more memories and one or more mass storage devices; A monitoring - session - data - processing component that prepares the monitoring - session - data for processing, determines component trajectories representing motion patterns and additional metric values from the monitoring - session - data; and A monitoring - session - data - analysis component that determines prosthesis status and patient status from the motion patterns and additional metric values, distributes the determined prosthesis status and patient status to a target computer system via a network connection, and When indicated by the determined prosthesis state and patient state, distribute one or more alerts and events to a target computer system via a network connection. 55. The monitoring-session-data collection, analysis, and status-reporting system as described in embodiment 54, wherein the monitoring-session data includes: a patient identifier; a device identifier; a timestamp; device-configuration data; and an ordered data set. 56. The monitoring-session-data collection, analysis, and status-reporting system as described in embodiment 55, wherein the ordered data set includes one of the following: A time series of data vectors, each data vector including linear-acceleration-related numerical values with respect to three coordinate axes of an internal device coordinate system; and A time series of data vectors, each data vector including linear-acceleration-related numerical values with respect to three coordinate axes of a first internal device coordinate system and including angular-velocity-related numerical values with respect to the first internal device coordinate system or a second internal device coordinate system. 57. The monitoring-session-data collection, analysis, and status-reporting system as described in embodiment 54, wherein the monitoring-session-data processing component prepares the monitoring-session data for processing by: Receiving a time series of data vectors, each data vector including three linear-acceleration-related numerical values in the directions of three coordinate axes of a first internal device coordinate system and including three angular-velocity-related numerical values about each axis of the first or second internal device coordinate system; Rescaling the numerical values of the data vectors when rescaling of the data-vector sequence is needed; Normalizing the numerical values of the data vectors when normalization of the data-vector sequence is needed; Converting one or more of the linear-acceleration-related numerical values and angular-velocity-related numerical values to associate the linear-acceleration-related numerical values and angular-velocity-related numerical values to a common internal coordinate system when conversion of one or more of the linear-acceleration-related numerical values and angular-velocity-related numerical values is needed to associate them to a common internal coordinate system; and Synchronizing the data vectors with respect to a fixed-interval time series when the time series of data vectors needs to be synchronized with respect to the fixed-interval time series. 58. The monitoring-session-data collection, analysis, and status-reporting system as described in embodiment 54, wherein the monitoring-session-data processing component determines component trajectories representing motion patterns and additional metric values from the monitoring-session data by: Orientation-prepared monitoring-session-data, which includes data vectors, each data vector including three linear-acceleration-related numerical values in the directions of the three coordinate axes of the internal device coordinate system relative to the natural coordinate system and numerical values related to the angular velocity around each axis of the internal device coordinate system; Band-pass filter the orientation data vectors to obtain a set of data vectors for each of a plurality of frequencies, including normal-motion frequencies; Determine the spatial amplitude on each of the coordinate-axis directions of the natural coordinate system from the data vectors of each non-normal-motion frequency; Determine the spatial amplitude on each coordinate-axis direction of the natural coordinate system from the patient's basic trajectory and the data vectors of normal-motion frequencies; and Determine the current normal-motion characteristics from the patient's basic trajectory and the data vectors of normal-motion frequencies. 59. The monitoring-session-data collection, analysis, and status-reporting system according to embodiment 58, wherein determining the spatial amplitude on each coordinate-axis direction of the natural coordinate system from the data vectors of the frequencies further includes: generating a spatial trajectory from the data vectors; projecting the spatial frequencies onto each coordinate axis; and determining the protection lengths of the spatial frequencies on each coordinate axis. 60. The monitoring-session-data collection, analysis, and status-reporting system according to embodiment 54, wherein the monitoring-session-data-analysis component determines the prosthesis status and the patient status from the motion pattern and additional metric values by: Submitting the motion pattern and additional metric values to a decision tree that generates a diagnostic-and-recommendation report; and Packaging the diagnostic-and-recommendation report together with the amplitude generated for the motion pattern, the metrics generated from the normal-motion-frequency trajectory and the reference trajectory, and the additional metric values to generate one or both of an output report and output data values characterizing the prosthesis status and the patient status. 61. The monitoring-session-data collection, analysis, and status-reporting system according to embodiment 54, wherein distributing one or more alerts and events to the monitoring-session-data-analysis component of the target computer system includes: An alert notifying a medical practitioner or healthcare institution that the patient requires immediate assistance or intervention; and An event indicating additional services and / or equipment required by the patient, which can be processed by various external computer systems to automatically provide the patient with the additional services and / or equipment or notify the patient of the additional services and / or equipment and provide the patient with information regarding the procurement of the additional services and / or equipment. 62. A method performed by a monitoring - session - data collection, analysis, and status - reporting system implemented as components of one or more computer systems, each computer system having one or more processors, one or more memories, one or more network connections, and access to one or more mass storage devices, the method comprising: Receiving monitoring - session - data from an external monitoring - session - data source, including acceleration data generated by sensors internal or proximal to a prosthesis attached to or implanted within a patient; Storing the received monitoring - session - data in one or more of one or more memories and one or more mass storage devices; Determining prosthesis status and patient status from motion patterns and additional metrics, Distributing the determined prosthesis status and patient status to a target computer system via a network connection, and When indicated by the determined prosthesis status and patient status, distributing one or more alerts and events to a target computer system via a network connection. 63. The method of embodiment 62, wherein determining prosthesis status and patient status from motion patterns and additional metrics further comprises: Preparing the monitoring - session - data for processing, Determining component trajectories representing motion patterns and additional metrics from the monitoring - session - data; Submitting the motion patterns and additional metrics to a decision tree that generates a diagnostic - and - recommendation report; and Packaging the diagnostic - and - recommendation report together with the amplitude generated for the motion pattern, metrics generated from a normal - motion - frequency trajectory and a reference trajectory, and the additional metrics to generate one or both of an output report and output data values characterizing the prosthesis status and patient status. 64. The method of embodiment 62, wherein preparing the monitoring - session - data for processing further comprises: Receiving a time series of data vectors, each data vector including three values related to linear - acceleration in the three axis directions of a first internal device coordinate system, and including three values related to angular velocity about each axis of the first or second internal device coordinate system; Rescaling the values of the data vectors when rescaling of the data - vector sequence is required; Normalizing the values of the data vectors when normalization of the data - vector sequence is required; When one or more of the linear-acceleration related values and the angular-velocity related values need to be transformed to associate the linear-acceleration related values and the angular-velocity related values to a common internal coordinate system, transform one or more of the linear-acceleration values and the angular-velocity related values to associate to a common internal coordinate system; and When a time series of data vectors needs to be synchronized relative to a fixed-interval time series, synchronize the data vectors relative to the fixed-interval time series. 65. The method according to embodiment 62, wherein the component trajectories representing the motion pattern and the additional metric values are determined from the monitoring-session-data by: Orient the prepared monitoring-session-data, which includes data vectors, each data vector including three linear-acceleration related values in the directions of the three coordinate axes of the internal device coordinate system relative to the natural coordinate system and values related to the angular velocity about each axis of the internal device coordinate system; Band-pass filter the oriented data vectors to obtain a set of data vectors for each of a plurality of frequencies, including the normal-motion frequency; Determine the spatial amplitude on each of the coordinate-axis directions of the natural coordinate system from the data vectors of each non-normal-motion frequency; Determine the spatial amplitude on each coordinate-axis direction of the natural coordinate system from the patient's basic trajectory and the data vectors of the normal-motion frequency; and Determine the current normal-motion characteristics from the patient's basic trajectory and the data vectors of the normal-motion frequency. 66. The method according to embodiment 54, wherein determining the spatial amplitude on each coordinate-axis direction of the natural coordinate system from the data vectors of the frequency further includes: Generate a spatial trajectory from the data vectors; Project the spatial frequency onto each coordinate axis; and Determine the guard length of the spatial frequency on each coordinate axis. 67. The method according to embodiment 54, wherein determining the prosthesis state and the patient state from the motion pattern and the additional metric values further includes: Submit the motion pattern and the additional metric values to a decision tree that generates a diagnostic-and-recommendation report; and Package the diagnostic-and-recommendation report together with the amplitude generated for the motion pattern, the metrics generated from the normal-motion-frequency trajectory and the reference trajectory, and the additional metric values to generate one or both of an output report and output data values characterizing the prosthesis state and the patient state. 68. The method according to embodiment 54, wherein one or more alerts and events distributed to the target computer system include: Alerts notifying medical practitioners or healthcare facilities that a patient requires immediate assistance or intervention; and Events indicating additional services and / or equipment needed by a patient, which can be processed by various external computer systems to automatically provide additional services and / or equipment to the patient or notify the patient of additional services and / or equipment and provide the patient with information regarding procurement of additional services and / or equipment. 69. A physical data-storage device encoded with computer instructions that, when executed by one or more processors within one or more computer systems of a monitoring-session-data collection, analysis, and status-reporting system, each computer system having one or more processors, one or more memories, one or more network connections, and access to one or more mass storage devices, controls the monitoring-session-data collection, analysis, and status-reporting system to: Receive monitoring-session data from an external monitoring-session data source, including acceleration data generated by sensors internal or proximal to a prosthesis attached to or implanted within a patient. 70. A method for determining joint loosening in a patient having an implanted artificial joint, the method comprising a) analyzing movement of the implanted artificial joint, and b) comparing the movement to prior / standardized norms. 71. A method for determining loosening of an implanted prosthesis in a patient having an implanted prosthesis, the method comprising: a) Obtaining a standardized movement norm by analyzing movement of the implanted prosthesis during one or more first monitoring sessions, b) Obtaining a current movement description by analyzing movement of the implanted prosthesis during one or more second monitoring sessions occurring after the one or more first monitoring sessions; and c) Comparing the current movement description to the standardized movement norm to identify loosening of the implanted prosthesis in a patient having an implanted prosthesis. 72. A method for identifying a clinical or subclinical condition associated with an implant within a patient, the method comprising: a. Monitoring a first movement of the implant during a first monitoring session using a sensor directly coupled to the implant to provide first monitoring-session data of the first movement; b. Monitoring a second movement of the implant during a second monitoring session using the sensor to provide second monitoring-session data of the second movement; and c. Comparing the first monitoring-session data or a product thereof to the second monitoring-session data or a product thereof to provide a comparison indicative of a clinical or subclinical condition associated with the implant. 73. The method according to embodiment 72, wherein the clinical or subclinical condition is loosening of the implant (movement of the prosthesis within the surrounding bone or cement, e.g., due to periprosthetic lucency or periprosthetic osteolysis, separation of the implant from the host bone). 74. The method according to embodiment 72, wherein the clinical or subclinical condition is malalignment (suboptimal positioning of the prosthesis components) or realignment (change in alignment of the prosthesis components) of the implant. 75. The method according to embodiment 72, wherein the clinical or subclinical condition is deformation (wear) of the implant. 76. The method according to embodiment 72, wherein the patient is asymptomatic for the condition, and comparison of the first and second data or their products indicates that the condition has occurred between the first and second monitoring sessions. 77. The method according to embodiment 72, wherein the patient is asymptomatic for implant loosening, and comparison of the first and second data or their products indicates that the implant has loosened between the first and second monitoring sessions. 78. The method according to embodiment 72, wherein the patient is asymptomatic for implant realignment, and comparison of the first and second data or their products indicates that the implant has changed alignment between the first and second monitoring sessions. 79. The method according to embodiment 72, wherein the patient is asymptomatic for implant deformation, and comparison of the first and second data or their products indicates that the implant has deformed between the first and second monitoring sessions. 80. A method for treating a clinical or subclinical condition associated with an implant in a patient, comprising: a. identifying an implant in the patient, wherein the implant has a clinical or subclinical condition; and b. attaching a corrective external brace to the patient to restore correct alignment and / or enhanced implant stability. 81. The method according to embodiment 80, wherein the corrective external brace is specifically customized for the patient and the subclinical condition. 82. A method for treating a clinical or subclinical condition associated with an implant in a patient, comprising: a. identifying an implant in the patient, wherein the implant has a clinical or subclinical condition; and b. contacting the implant with a fixation system to retard the progression of the subclinical condition. 83. The method according to embodiment 82, wherein the fixation system comprises hardware selected from Kirschner wires, pins, screws, plates, and intramedullary devices. 84. The method as described in embodiment 82, wherein the screw is positioned through the bone accommodating the implant, and the end of the screw abuts against the surface of the implant to retard the movement of the implant, and the screw is selected from one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, and twenty screws. 85. The method as described in embodiment 82, wherein the fixation system includes bone cement. 86. A method for treating a clinical or subclinical condition associated with an implant in a patient, comprising: a. Identifying the implant in the patient, wherein the implant has a clinical or subclinical condition; and b. Contacting the implant with a tamper, wherein the contact changes the position of the implant in the patient; and optionally c. Coating a cement around the implant with the changed position. 87. The method as described in embodiment 86, wherein the subclinical condition is realignment of the implant. 88. A method for treating a clinical or subclinical condition associated with an implant in a patient, comprising: a. Identifying the implant in the patient, wherein the implant has a clinical or subclinical condition; and b. Implanting an insert into a component adjacent to the implant, wherein the insert changes the force acting on the implant component. 89. The method as described in embodiment 88, wherein the insert is a tibial insert. 90. The method as described in embodiment 88, wherein the insert is a tibial insert having: (i) a lateral side with a minimum thickness and (ii) a medial side with a minimum thickness different from the minimum thickness of the lateral side. 91. A method for treating a clinical or subclinical condition associated with an implant in a patient, comprising: a. Identifying the implant in the patient, wherein the implant has a clinical or subclinical condition; and b. Delivering an osteoinductive agent to a location around the implant. 92. The method as described in embodiment 91, wherein the osteoinductive agent is selected from autologous bone grafts, xenogeneic bone grafts, synthetic bone grafts, bone pastes, bone growth factors, and growth factors. 93. A method for treating a clinical or subclinical condition associated with an implant in a patient, comprising: a. Identifying the implant in the patient, wherein the implant has a clinical or subclinical condition; and b. Delivering an antibacterial agent to a location around the implant. 94. The method according to embodiment 93, wherein the antibacterial agent is formulated in a sustained release form. 95. The method according to any one of embodiments 72 - 94, wherein the implant is an intelligent implant. 96. The method according to embodiments 72 - 94, wherein the implant is selected from knee implants, hip implants, and shoulder implants. 97. The method according to any one of embodiments 72 - 94, wherein the product of the monitoring session data includes a motion pattern. 98. The method according to any one of embodiments 72 - 94, wherein the product of the monitoring session data includes a motion pattern, and the status of the implant is determined from the motion pattern. 99. The method according to any one of embodiments 72 - 94, wherein the product of the monitoring session data includes a motion pattern, and the status of the patient is determined from the motion pattern. 100. The method according to any one of embodiments 72 - 94, wherein the implant has been positioned in the patient's body for at least 10 weeks prior to the first monitoring session. 101. The method according to any one of embodiments 72 - 94, wherein the alignment of the implant has changed over a period of at least 2 weeks. 102. The method according to any one of embodiments 72 - 94, wherein the implant has loosened over a period of at least 2 weeks. 103. The method according to any one of embodiments 72 - 94, wherein the implant has deformed over a period of at least 2 weeks. 104. The method according to any one of embodiments 72 - 94, wherein the implant includes a control circuit configured to cause the sensor to generate sensor signals at a frequency related to a telemedicine code for a clinical or sub - clinical condition, and the sensor signals are generated at that frequency. 105. The method according to any one of embodiments 72 - 94, wherein the implant includes a control circuit configured to cause the sensor to generate sensor signals at a frequency that would allow a doctor to be eligible for payment under a telemedicine insurance code, and the sensor signals are generated at that frequency. 106. The method according to any one of embodiments 72 - 94, wherein the implant includes a control circuit configured to cause the sensor to generate sensor signals at a frequency that would allow a doctor to be eligible for full payment under a telemedicine insurance code, and the sensor signals are generated at that frequency. 107. The method according to any one of embodiments 72 - 94, further comprising generating sensor signals associated with the implant at a frequency that allows: (i) the doctor to be eligible for full payment according to a remote health insurance code, or (ii) the doctor to be eligible for payment according to a remote health insurance code. 108. A method, comprising: a. providing an intelligent prosthesis implanted adjacent to the bone of a patient's joint, wherein an accelerometer is included in the intelligent prosthesis and wherein the accelerometer is positioned within the bone; b. moving the implanted intelligent prosthesis relative to the external environment in which the patient is located, wherein the implanted intelligent prosthesis is moved during a first monitoring session; c. performing a first measurement with the accelerometer during the first monitoring session, wherein the first measurement provides first monitoring - session - data or a product thereof that identifies the state of the implanted intelligent prosthesis at the time of the first measurement. 109. The method according to embodiment 108, wherein the accelerometer is a plurality of accelerometers. 110. The method according to embodiment 108, wherein the accelerometer is selected from a 1 - axis accelerometer, a 2 - axis accelerometer, and a 3 - axis accelerometer. 111. The method according to embodiment 108, wherein the accelerometer operates in a broadband mode. 112. The method according to embodiment 108, wherein the bone is the tibia. 113. The method according to embodiment 108, wherein the accelerometer is located in the tibial extension of the intelligent prosthesis. 114. The method according to embodiment 108, wherein during the first monitoring session, the implanted intelligent prosthesis is moved relative to the external environment without applying an impact force to the patient or the intelligent prosthesis. 115. The method according to embodiment 108, wherein the external environment includes the patient's residence. 116. The method according to embodiment 108, wherein the external environment includes an operating room where the intelligent prosthesis has been implanted in the patient. 117. The method according to embodiment 108, wherein the state of the implanted intelligent prosthesis is a characterization of loosening of the implanted intelligent prosthesis within the bone. 118. The method according to embodiment 108, wherein the state of the implanted intelligent prosthesis is a characterization of alignment of the implanted intelligent prosthesis within the bone. 119. The method according to embodiment 108, wherein the state of the implanted intelligent prosthesis is a characterization of wear of the implanted intelligent prosthesis. 120. The method according to embodiment 108, wherein the state of the implanted intelligent prosthesis is a characterization of a bacterial infection in the region within the bone adjacent to the implanted intelligent prosthesis. 121. The method according to embodiment 108, wherein the status of the implanted intelligent prosthesis indicates a subclinical condition. 122. The method according to embodiment 108, wherein step b) is repeated after a waiting period, and the repetition of step b) includes moving the implanted intelligent prosthesis relative to the external environment in which the patient is located, wherein the implanted intelligent prosthesis moves during a second monitoring session, and wherein a second measurement is performed with an accelerometer during the second monitoring session, and the second measurement provides second monitoring - session - data or a product thereof that identifies the implantation status of the implanted intelligent prosthesis at the time of the second measurement. 123. The method according to embodiment 108, wherein step b) is repeated a plurality of times, the plurality of times being separated from each other by the same or different waiting periods, and the repetition of step b) includes moving the implanted intelligent prosthesis relative to the external environment in which the patient is located, wherein the implanted intelligent prosthesis moves during a plurality of monitoring sessions, and wherein a measurement is performed with an accelerometer during each of the plurality of monitoring sessions, and the measurement provides a plurality of monitoring - session - data or a product thereof, each of which identifies the implantation status of the implanted intelligent prosthesis at the time of the measurement. 124. The method according to embodiment 108, wherein step b) is repeated a plurality of times, the plurality of times being separated from each other by the same or different waiting periods, and the repetition of step b) includes moving the implanted intelligent prosthesis relative to the external environment in which the patient is located, wherein the implanted intelligent prosthesis moves during a plurality of monitoring sessions, and wherein a measurement is performed with an accelerometer during each of the plurality of monitoring sessions, and the measurement provides a plurality of monitoring - session - data or a product thereof, each of which identifies the implantation status of the implanted intelligent prosthesis at the time of the measurement; wherein the plurality is optionally selected from 2 to 20 monitoring sessions, and the plurality of obtained monitoring - session data together indicate a change in the status of the implanted intelligent prosthesis during the time period when the plurality of monitoring sessions occur. 125. The method according to embodiment 124, wherein the change in status indicates the healing of the tissue around the implanted intelligent prosthesis. 126. The method according to embodiment 124, wherein the change in status indicates an infection of the tissue around the implanted prosthesis. 127. The method according to embodiment 124, wherein the change in status indicates loosening of the implanted intelligent prosthesis within the bone. 128. The method according to embodiment 124, wherein the change in status indicates wear of the implanted intelligent prosthesis. 129. The method according to embodiment 124, wherein the change in status indicates misalignment of the implanted intelligent prosthesis. 130. The method according to embodiment 124, wherein the change in status indicates a change in the alignment of the implanted intelligent prosthesis.

[0021] Details of one or more embodiments are set forth in the following description. Features described in connection with one exemplary embodiment may be combined with features of other embodiments. Accordingly, any of the various embodiments described herein may be combined to provide further embodiments. Aspects of the embodiments may be modified, if necessary, to employ concepts of various patents, applications and publications as identified herein to provide yet further embodiments. Other features, objects, and advantages will be apparent from the specification, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Exemplary features of the present disclosure, its nature, and various advantages will become apparent from the following detailed description of the drawings and various embodiments. Non-limiting and non-exhaustive embodiments are described with reference to the drawings, in which like reference numerals or reference designators refer to like components throughout the various views unless otherwise indicated. The size and relative positions of the elements in the figures are not necessarily drawn to scale. For example, the shapes of the various elements are selected, enlarged, and positioned to improve the readability of the drawing. Specific shapes have been selected for ease of identification in the drawings. One or more embodiments are described below in connection with the drawings, in which:

[0023] Figure 1 An exemplary smart implant is illustrated.

[0024] Figure 2 An implant including Figure 1 is illustrated as part of an articular prosthesis and the prosthesis is positioned in the tibia.

[0025] Figure 3 is a relationship diagram of a kinematic implantable device environment in a patient's home according to one embodiment.

[0026] Figure 4 is a block diagram of an implantable circuit of an implantable prosthesis (such as an implantable knee prosthesis) according to one embodiment, wherein the circuit includes an implantable reporting processor (IRP).

[0027] Figure 5 is a block diagram of a base station circuit configured to communicate with the implantable circuit and forward data from the implantable circuit to a remote processing server (such as a cloud-based server).

[0028] Figure 6 is according to one embodiment, Figure 4 a perspective view of an inertial measurement unit (IMU) of an implantable circuit and a set of coordinate axes within the reference frame of the IMU.

[0029] Figure 7 is according to one embodiment, in which a standing patient with an implanted knee prosthesis and Figure 6 a front view of two axes of the IMU.

[0030] Figure 8 For one embodiment, a side view of two axes of an Figure 7 IMU for a patient in a supine position and Figure 6

[0031] Fig. 9 For one embodiment, a graph of acceleration measured along the Figure 7 x, y, and z axes of an 8 IMU over time for a patient in Figure 6

[0032] Fig.10 For one embodiment, a graph of acceleration measured along the Figure 7 x, y, and z axes of an 8 IMU over time for a patient in Figure 6

[0033] Fig.11 For one embodiment, a graph of the time-scale magnified portion of a Fig. 9 graph over time

[0034] Fig.12 For one embodiment, a graph of the time-scale magnified portion of a Fig.10 graph over time

[0035] Fig.13 For one embodiment, a graph of acceleration measured along the Figure 7 x, y, and z axes of an 8 IMU over time during heel strike of a patient in Figure 6

[0036] Fig.14 For one embodiment, a graph of the corresponding spectral distribution of each of the Fig.13 x, y, and z accelerations over frequency

[0037] Fig.15 For one embodiment, a graph of the cumulative spectral distribution of the Fig.13 x, y, and z accelerations over frequency

[0038] Fig.16 For one embodiment, a graph of acceleration measured along the Figure 7 x, y, and z axes of an 8 IMU over time during heel strike of a patient in Figure 6 ​​​​​

[0039] Fig.17 is, according to one embodiment, Fig.16 a graph of the corresponding spectral distribution of each of the x, y, and z accelerations of

[0040] Fig.18 is, according to one embodiment, Fig.16 a graph of the cumulative spectral distribution of the x, y, and z accelerations of

[0041] Fig.19 is, according to one embodiment, when a patient walks with a normal gait and a knee prosthesis implanted in the patient exhibits instability and early-stage deterioration Figure 7 and 8 during impact on the heel of the patient Figure 6 a graph of the accelerations measured along the x, y, and z axes of the

[0042] Fig. 20 is, according to one embodiment, Fig.19 a graph of the corresponding spectral distribution of each of the x, y, and z accelerations of

[0043] Fig.21 is, according to one embodiment, Fig.19 a graph of the cumulative spectral distribution of the x, y, and z accelerations of

[0044] Fig. 22 is, according to one embodiment, when a patient walks with a normal gait and a knee prosthesis implanted in the patient exhibits instability and severe deterioration Figure 7 and 8 during impact on the heel of the patient Figure 6 a graph of the accelerations measured along the x, y, and z axes of the

[0045] Fig.23 is, according to one embodiment, Fig. 22 a graph of the corresponding spectral distribution of each of the x, y, and z accelerations of

[0046] Fig.24 is, according to one embodiment, Fig. 22 a graph of the cumulative spectral distribution of the x, y, and z accelerations of

[0047] Fig.25 is, according to one embodiment, Figure 4 a flowchart of the operation of an implantable circuit.

[0048] Fig.26 is, according to one embodiment, Figure 4 Flowchart of the operation of the base station circuit.

[0049] Fig. 27 It is according to one embodiment, Figure 4 Flowchart of the operation of the fuse.

[0050] Fig.28 Illustrates a three-dimensional Cartesian coordinate space and the representation of a point in the space by a vector.

[0051] Fig.29A and Fig.29B Each illustrates the data output by the IMU.

[0052] Fig. 30A 、 Fig. 30B 、 Fig. 30C 、 Fig.30D 、 Fig.30E 、 Fig.30F and Figure 30G Each illustrates a complex space curve representing motion and the decomposition of the complex space curve into component motions.

[0053] Fig.31 Illustrates a method for processing non-periodic motion types.

[0054] Fig.32A 、 Fig.32B 、 Fig.32C 、 Fig.32D 、 Fig.32E and Fig.32F Each illustrates the principal-component-analysis method for rotating the initial coordinate system to a coordinate system whose central axis is aligned with the distribution of points representing experimental observations.

[0055] Fig.33 Illustrates determining the natural coordinate system using principal component analysis based on the raw or filtered IMU output data.

[0056] Fig.34A 、 Fig.34B 、 Fig.34C and Fig.34D Each illustrates forward and inverse Fourier transforms.

[0057] Fig.35 Illustrates using Fourier transform on the data-vector output of the IMU.

[0058] Fig.36A and Fig.36B Each illustrates the data output by the data-processing application as a result of processing and analyzing the raw data obtained during a monitoring session received from the base station.

[0059] Fig.36CIllustrates the final part of the results generated by a data processing application as a result of processing and analyzing the raw data obtained during a monitoring session received from a base station.

[0060] Fig.37A 、 Fig.37B 、 Fig.37C 、 Fig.37D 、 Fig.37E 、 Fig.37F 、 Figure 37G and Fig.37H each provide a control - flow diagram illustrating the current - discussed implementation of a data - processing application for processing patient - monitoring - session data.

[0061] Fig.38A Illustrates representative cloud - based systems and methods for generating and processing data, communication paths, report generation, and revenue generation. Fig.38B Illustrates representative on - premise - based systems and methods for generating and processing data, communication paths, report generation, and revenue generation.

[0062] Fig.39 Illustrates the components of a currently - used total knee arthroscopy system (3010), specifically the femoral component (3012), the tibial insert (3014), and the tibial component (3016), where the tibial component (3016) includes a tibial plate (3018) and a tibial stem (3020).

[0063] Fig.40A 、 Fig.40B 、 Fig.40C and Fig.40D Illustrates an exemplary tibial component.

[0064] Fig.41 Illustrates a tibial insert.

[0065] Fig.42A Illustrates Fig.41 a cross - sectional view of the tibial insert.

[0066] Fig.42B Illustrates Fig.42A the deviation in a cross - sectional view.

[0067] Fig.43 Illustrates a tibial insert.

[0068] Fig.44A Illustrates Fig.43 a cross - sectional view of the tibial insert.

[0069] Fig.44B Illustrates Fig.44A the deviation in a cross - sectional view.

[0070] Fig.45Describes a tibial insert having an angle extending into the femoral component.

[0071] Fig.46 Describes a tibial insert having a spike extending into the femoral component. DETAILED DESCRIPTION OF THE INVENTION

[0072] The present disclosure can be more readily understood by reference to the following detailed description of the preferred embodiments of the present disclosure and the examples of "intelligent prostheses" included herein. The following description, together with the accompanying drawings, sets forth certain specific details in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the disclosed embodiments can be practiced in various combinations without one or more of these specific details, or with other methods, components, devices, materials, etc. In other instances, well-known structures or components associated with the context of the present disclosure, including but not limited to communication systems and networks, have not been shown or described in order to avoid unnecessarily obscuring the description of the embodiments. Additionally, each embodiment can be a method, system, medium, or device. Thus, each embodiment can be a fully hardware embodiment, a fully software embodiment, a fully firmware embodiment, or a combination or sub - combination of software, firmware, and hardware aspects of an embodiment.

[0073] Before elaborating on the present disclosure in more detail, providing definitions of certain terms that will be used herein may aid in its understanding. Additional definitions are set forth throughout the present disclosure. The terms "comprising" and "including" and their derivatives mean including but not limited to. The term "or" is inclusive and means "and / or". The phrases "associated with" and "associated therewith" and their derivatives may mean including, interconnected, containing, included within, connected to or connected with, coupled to or coupled with, capable of communicating with, cooperating with, interlaced, juxtaposed, proximate to, combined with or combined to or with, having, having the characteristics of, etc. The term "controller" means any device, system, or part thereof that controls at least one operation, such device may be implemented in hardware (e.g., an electronic circuit), firmware, software, or some combination of at least two of them. The functions associated with any particular controller may be centralized or distributed, whether local or remote. Other definitions of certain words and phrases may be provided in this patent document. Those of ordinary skill in the art will understand that in many cases, if not most cases, such definitions apply to the prior and future use of such defined words and phrases.

[0074] As used in this disclosure, an "intelligent prosthesis" or "intelligent medical device" is an implantable or implanted medical device that ideally replaces or functionally supplements a subject's natural body part. As used herein, the term "intelligent prosthesis" may be used interchangeably to refer to an "intelligent implant", "smart implant", "smart medical device", "joint implant", "implanted medical device" or another similar term. When an intelligent prosthesis performs kinematic measurements, it may be referred to as a "kinematic medical device" or "kinematic implantable device". In describing embodiments of the present disclosure, reference may be made to kinematic implantable devices, however it should be understood that this is merely an example of an intelligent medical device that may be used in the devices, methods, systems, etc. of the present disclosure. Whether the intelligent prosthesis performs kinematic measurements, or performs other or additional measurements, the prosthesis will include an implantable reporting processor (IRP) or be associated with an implantable reporting processor (IRP). In one embodiment, the intelligent prosthesis is an implantable or implantable medical device having an implantable reporting processor arranged to perform the functions described herein. The intelligent prosthesis may perform one or more of the following exemplary actions to characterize the post-implant state of the intelligent prosthesis: identifying the intelligent prosthesis or a portion thereof, such as by identifying one or more unique identification codes of the intelligent prosthesis or a portion of the intelligent prosthesis; detecting, sensing, and / or measuring parameters, which may collectively be referred to as monitoring parameters, to collect operation, kinematic, or other data regarding the intelligent prosthesis or a portion thereof, and wherein such data may optionally be collected as a function of time; storing the collected data within the intelligent prosthesis or a portion thereof; and communicating the collected data and / or stored data wirelessly from the intelligent prosthesis or a portion thereof to an external computing device. The external computing device may access or otherwise access at least one data storage location, such as a data storage location found on a personal computer, base station, computer network, cloud-based storage system, or another computing device that accesses such storage. Non-limiting and non-exhaustive lists of embodiments of intelligent prostheses include total joint arthroplasties such as total knee arthroplasty (TKA), TKA tibial trays, TKA femoral components, TKA patellar components, tibial extensions, total hip arthroplasty (THA), femoral components for THA, acetabular components for THA, shoulder arthroplasty, breast implants, intramedullary rods for repairing arm or leg fractures, scoliosis rods, dynamic hip screws, spinal intervertebral spacers, spinal artificial discs, annuloplasty rings, heart valves, endovascular stents, vascular grafts, and vascular stent implants.

[0075] As used herein, "kinematic data" individually or collectively includes some or all of the data associated with a particular kinematic implantable device and available for communication external to the particular kinematic implantable device. For example, kinematic data can include raw data from one or more sensors of a kinematic implantable device, where the one or more sensors include, such as, gyroscopes, accelerometers, pedometers, strain gauges, etc., which generate data related to movement, force, tension, velocity, or other mechanical forces. Kinematic data can also include processed data, status data, operational data, control data, fault data, time data, scheduling data, event data, log data, and data associated with a particular kinematic implantable device from one or more sensors. In some cases, high-resolution kinematic data includes kinematic data from one, more, or all sensors of a kinematic implantable device, which is collected in greater amounts, at higher resolution, from more sensors, more frequently, etc.

[0076] In one embodiment, kinematics refers to the measurement of the position, angle, velocity, and acceleration of body segments and joints during movement. To describe the movement of the body, body segments are considered rigid bodies. They include the foot, shank (leg), thigh, pelvis, chest, hand, forearm, upper arm, and head. The joints between adjacent segments include the ankle (talocrural and subtalar joints), knee, hip, wrist, elbow, and shoulder. Position describes the location of a body segment or joint in space, measured in terms of distance, e.g., in meters. A related measurement called displacement refers to the position relative to the starting position. In two dimensions, position is given in Cartesian coordinates, with the horizontal position followed by the vertical position. In one embodiment, a kinematic implant or intelligent kinematic implant obtains kinematic data and optionally only obtains kinematic data.

[0077] "Sensor" refers to a device that can be used to detect, measure, and / or monitor one or more different aspects (anatomical, physiological, metabolic, and / or functional) of body tissue and / or one or more aspects of an orthopedic device or implant. Representative examples of sensors suitable for use within the present disclosure include, for example, fluid pressure sensors, fluid volume sensors, contact sensors, position sensors, pulse pressure sensors, blood volume sensors, blood flow sensors, chemical sensors (e.g., for blood and / or other fluids), metabolic sensors (e.g., for blood and / or other fluids), accelerometers, mechanical stress sensors, and temperature sensors. In some embodiments, the sensor can be a wireless sensor, or in other embodiments, a sensor connected to a wireless microprocessor. In additional embodiments, one or more (including all) sensors can have a unique sensor identification number ("USI") that specifically identifies the sensor. In some embodiments, the sensor is a device that can be used to quantitatively measure one or more different aspects (anatomical, physiological, metabolic, and / or functional) of body tissue and / or one or more aspects of an orthopedic device or implant. In some embodiments, the sensor is an accelerometer that can be used to quantitatively measure one or more different aspects (e.g., function) of body tissue and / or one or more aspects (e.g., alignment in a patient) of an orthopedic device or implant.

[0078] A variety of sensors (also known as microelectromechanical systems or "MEMS", or nanoelectromechanical systems or "NEMS", and BioMEMS or BioNEMS, see generally https: / / en.wikipedia.org / wiki / MEMS) can be used within the present disclosure. Representative patents and patent applications include U.S. Patent Nos. 7,383,071, 7,450,332; 7,463,997, 7,924,267 and 8,634,928, as well as U.S. Publications Nos. 2010 / 0285082 and 2013 / 0215979. Representative publications include Albert Foch's "Introduction to BioMEMS", CRC Press, 2013; Marc J. Madou's "From MEMS to Bio-MEMS and Bio-NEMS: Manufacturing Techniques and Applications", CRC Press 2011; Simona Badilescu's "Bio-MEMS: Science and Engineering Perspectives", CRC Press 2011; Steven S. Saliterman's "Fundamentals of BioMEMS and Medical Microdevices", SPIE - The International Society of Optical Engineering, 2006; "Bio-MEMS: Technologies and Applications", edited by Wanjun Wang and Steven A. Soper, CRC Press, 2012; and Volker Kempe's "Inertial MEMS: Principles and Practice", Cambridge University Press, 2011; Polla, D.L., et al., "Microdevices in Medicine," Ann. Rev. Biomed. Eng. 2000, 02:551 - 576; Yun, K.S., et al., "A Surface-Tension Driven Micropump for Low-voltage and Low-Power Operations," J. Microelectromechanical Sys., 11:5, October 2002, 454 - 461; Yeh, R., et al., “Single Mask, Large Force, and LargeDisplacement Electrostatic Linear Inchworm Motors,” J. Microelectromechanical Sys., 11:4, August 2002, 330-336; and Loh, N.C., et al., “Sub-10cm3 InterferometricAccelerometer with Nano-g Resolution,” J. Microelectromechanical Sys., 11:3, June 2002, 182-187; all of the above publications are incorporated by reference in their entirety.

[0079] To further understand the various aspects of the embodiments of the present disclosure provided herein, the following sections are provided: A. Smart medical devices and implants; B. Systems with smart implants; C. Joint implants and systems with joint implants; D. Computer systems for analysis, information dissemination, ordering, and supply: processing IMU data recorded during patient monitoring; E. Methods and devices for stabilizing artificial joints; F. Methods and devices for adjusting the position of artificial joints; G. Joint inserts and their uses; and H. Clinical solutions and products. A. Smart medical devices and implants

[0080] In one aspect, the present disclosure provides a medical device including a medical device (implant) implantable in a patient that can be used to monitor and report the status and / or activity of the medical device, including postoperative activity and the progress of the patient involved, as well as its characteristics. In one embodiment, the present disclosure provides a smart implant that realizes the benefits of a medical implant, such as those provided by a prosthesis that replaces or supplements the natural function of the patient, while also realizing the benefits of monitoring and reporting, which provides insights into the function and / or condition of the device and / or the patient who has received the implanted device. In one embodiment, the medical device is an implantable device that is an implantable prosthesis in vivo that can be implanted into a living host (also referred to as a patient), for example, to improve the function of the biological structure or organ of the patient's body or to replace the biological structure or organ of the patient's body.

[0081] In one embodiment of the present disclosure, the medical implant is a stent implant and the smart implant is a stent implant coupled to a sensor, as disclosed, for example, in PCT Publication No. WO 2014 / 100795 and U.S. Patent No. 9949692, and PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077.

[0082] In one embodiment of the present disclosure, the medical implant is a stent and the smart implant is a stent coupled to a sensor, such as a stent monitoring assembly, as disclosed in PCT Publication No. WO 2014 / 144070 and U.S. Patent Publication No. 2016 / 0038087, and PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077.

[0083] In one embodiment of the present disclosure, the medical implant is a hip replacement prosthesis, including one or more of a femoral stem, a femoral head, and an acetabular implant, and the smart implant is a sensor coupled to the hip replacement prosthesis or its components, such as a hip replacement, as disclosed in PCT Publication No. WO 2014 / 144107 and U.S. Patent Publication No. 2016 / 0029952, and PCT Publication No. WO2016 / 044651 and U.S. Patent Publication No. 20160310077.

[0084] In one embodiment of the present disclosure, the medical implant is a medical tube and the smart implant is a medical tube coupled to a sensor. A medical tube refers to a generally cylindrical body that can be used in a medical procedure (e.g., the tube is typically sterile, pyrogen-free, and / or suitable for use and / or implantation into the human body). For example, the tube can be used for: 1) bypassing a blockage (e.g., in the case of coronary artery bypass grafting or "CABG" and peripheral bypass grafting) or opening a blockage (balloon dilation catheter, angioplasty balloon); 2) relieving pressure (e.g., shunts, drainage tubes and catheters, urinary catheters); 3) restoring or supporting an anatomical structure (e.g., endotracheal tube, tracheostomy tube, and feeding tube); and 4) providing access (e.g., CVC catheter, peritoneal and hemodialysis catheters). Representative examples of tubes include catheters, auditory tubes or Eustachian tubes, drainage tubes, tracheostomy tubes (e.g., Durham tubes), endotracheal tubes, esophageal tubes, feeding tubes (e.g., nasogastric or NG tubes), gastric tubes, rectal tubes, colostomy tubes, and various grafts (e.g., bypass grafts). See, e.g., PCT Publication No. WO 2015 / 200718 and U.S. Patent Publication No. 2017 / 0196478, and PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077, which disclose medical tubes and sensors attached thereto. In one embodiment, the medical tube is selected from catheters, auditory tubes or Eustachian tubes, drainage tubes, tracheostomy tubes, endotracheal tubes, esophageal tubes, feeding tubes, gastric tubes, rectal tubes, and colostomy tubes.

[0085] In one embodiment of the present disclosure, the medical implant is an aesthetic (cosmetic) implant, and the smart implant is an aesthetic implant coupled to a sensor. An aesthetic implant refers to an artificial or synthetic prosthesis that has been or can be implanted in the body. Implants are typically used to augment or replace structures in the body and have been used in a variety of aesthetic applications, including, for example, facial (such as lip, chin, nose, nasolabial fold, and cheekbone implants), penile, and body contour (such as breast, pectoral muscle, calf, buttock, abdominal, and biceps / triceps) implants. See, e.g., PCT Publication No. WO 2015 / 200704 and U.S. Patent Publication No. 2017 / 0181825, as well as PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077, which disclose aesthetic implants and sensors attached thereto. In one embodiment, the aesthetic implant is a breast implant.

[0086] In one embodiment of the present disclosure, the medical implant is a spinal implant, and the smart implant is a spinal implant coupled to a sensor. Examples of spinal devices and implants include pedicle screws, spinal rods, spinal wires, spinal plates, spinal cages, artificial intervertebral discs, bone cements, and combinations thereof (such as one or more pedicle screws and a spinal rod, one or more pedicle screws and a spinal plate). In addition, a medical delivery device for placing a spinal device and implant along with one or more sensors can also be a smart medical device according to the present disclosure. Examples of medical delivery devices for spinal implants include kyphoplasty balloons, catheters (including thermal catheters and bone tunnel catheters), bone cement injection devices, microdiscectomy tools, and other surgical tools. See, e.g., PCT Publication No. WO 2015 / 200720 and U.S. Patent Publication No. 2017 / 0196508, as well as PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077, which disclose spinal implants and sensors attached thereto, and delivery devices with sensors attached thereto for placing spinal devices, any of which can be a smart medical device or smart implant according to the present disclosure.

[0087] In one embodiment of the present disclosure, the medical device is a piece of orthopedic hardware, which may or may not be implantable, and the smart medical device is a sensor coupled to a piece of orthopedic hardware, which may or may not be implantable orthopedic hardware. Examples of orthopedic devices and implants include external hardware (e.g., casts, braces, external fixation devices, tensioners, slings, and supports) and internal hardware (e.g., Kirschner wires, pins, screws, plates, and intramedullary devices (e.g., rods and nails)). In addition, a medical delivery device for placing an orthopedic device and implant together with one or more sensors may also be a smart medical device of the present disclosure. Examples of medical delivery devices for orthopedic hardware include drills, drill guides, hammers, guide wires, catheters, bone tunnel catheters, microsurgical tools, and general surgical tools. See, e.g., PCT Publication No. WO 2015 / 200722 and U.S. Patent Publication No. 2017 / 0196499, as well as PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077, which disclose orthopedic hardware and sensors attached thereto, and delivery devices with sensors for placing orthopedic hardware, all of which may be smart medical devices and smart implants of the present disclosure.

[0088] In one embodiment of the present disclosure, the medical device is a medical polymer for a medical procedure. A variety of polymers can be used as medical polymers, where an attached sensor can monitor the integrity and effectiveness of the polymer (whether used alone or as part of or in combination with other medical devices or implants). The medical polymers of the present disclosure can be formed into a wide range of shapes and sizes suitable for medical applications. Representative examples of polymer forms include solid forms such as films, sheets, molded, cast or cut shapes. Other solid forms include extruded forms that can be made into tubes (such as shunts, drainage tubes and catheters), and fibers that can be woven into meshes or used to make sutures. Liquid forms of the polymer include gels, dispersions, colloidal suspensions, etc. Particularly preferred polymers used in the present disclosure are medical polymers that are provided in a sterile and / or pyrogen-free form and are suitable for humans. Representative examples of polymers include polyesters, polyurethanes, silicones, epoxies, melamine formaldehyde resins, acetals, polyethylene terephthalate, polysulfones, polystyrenes, polyvinyl chlorides, polyamides, polyolefins, polycarbonates, polyethylenes, polyamides, polyimides, polypropylenes, polytetrafluoroethylenes, ethylene propylene diene rubbers, styrenes (such as styrene butadiene rubber), nitriles (such as nitrile rubber), hypalon, polysulfides, butyl rubbers, silicone rubbers, celluloses, chitosans, fibrinogens, collagens, hyaluronic acids, PEEK, PTFE, PLA, PLGA, PCL and PMMA. See, e.g., PCT Publication No. WO 2015 / 200723 and U.S. Patent Publication No. 2017 / 0189553, as well as PCT Publication No. WO2016 / 044651 and U.S. Patent Publication No. 20160310077, which disclose polymers and sensors attached thereto, all of which can be the intelligent medical devices and intelligent implants of the present disclosure.

[0089] In one embodiment of the present disclosure, the medical device is a heart valve, and the intelligent medical device is a heart valve coupled to a sensor. A "heart valve" refers to a device implantable within the heart of a patient with valvular disease. There are three main types of heart valves: mechanical, biological, and tissue-engineered (although for the purposes of the present disclosure, tissue-engineered valves will be considered together with other biological valves). Mechanical valves are generally divided into two categories: 1) heart valves for surgical procedures using sternotomy or "open-heart" surgery (such as, 'caged-ball', 'tilting-disc', bileaflet, and tricuspid designs); and 2) percutaneously implantable heart valves (such as, stent frames (self-expanding stents or balloon-expandable stents) or non-stent frame designs), which may typically contain leaflets made from biological sources (bovine or porcine pericardium). Tissue-based or 'biological' valves are generally made from porcine or bovine sources and are typically prepared from the valves of animals (such as porcine valves) or pericardial sac tissue (such as bovine pericardial valves or porcine pericardial valves). Tissue-engineered valves are valves that are artificially manufactured on a scaffold (such as, by growing suitable cells on a tissue scaffold). Tissue-engineered valves have not been commercially adopted. See, e.g., PCT Publication No. WO 2015 / 200707 and U.S. Patent Publication No. 2017 / 0196509, as well as PCT Publication No. WO 2016 / 044651 and U.S. Patent Publication No. 20160310077, which disclose heart valves and sensors attached to heart valves, which may be the medical device and the intelligent medical device of the present disclosure, respectively. In an embodiment, the heart valve is a mechanical heart valve, such as a caged-ball design, a tilting-disc mechanical valve, a bileaflet or tricuspid mechanical valve, a self-expanding percutaneous heart valve, a percutaneous heart valve, or a balloon-expandable percutaneous heart prosthesis. The medical device with a sensor may be a balloon delivery device for a balloon-expandable percutaneous heart valve.

[0090] A medical implant replaces a patient's joint, such as a knee, shoulder, or hip joint, and allows the patient to have the same or nearly the same range of motion as a healthy joint would provide. When a medical implant replaces a joint, in one embodiment, a sensor coupled to the implant may monitor displacement or movement. Generally, there are three types of three-dimensional motion within and around the joint that a sensor can detect: core gait (or limb range of motion in the case of shoulder or elbow arthroplasty), macroinstability, and microinstability. Although these motions will be discussed in relation to TKA implants, it is understood that they are also applicable to total hip, shoulder, elbow, and ankle arthroplasties. See, e.g., PCT Publication No. WO 2014 / 144107, WO2014 / 209916, WO 2016 / 044651, and WO 2017 / 165717, which disclose medical implants for replacing a patient's joint for the present disclosure, and their intelligent forms.

[0091] In one embodiment, the medical implant is a knee implant, and in particular, a total knee implant for total knee arthroscopy. Sensors attached to the total knee implant of the present disclosure can monitor and characterize the movement of the knee implant, where this movement can take the form of, such as core gait, macroscopic instability, and microscopic instability, as discussed below.

[0092] Figure 1 is available for implementing Figure 2 A perspective view of an exemplary embodiment of a reporting processor 10 of an exemplary smart implant depicted in the exemplary embodiment shown. In Figure 1 the embodiment shown, the implantable reporting processor 10 includes an outer cannula 12 that encapsulates a power component (battery) 14, an electronic assembly 16, and an antenna 20. One component of the cannula is an antenna cover 18 that covers and protects the antenna that allows the implantable reporting processor to receive and transmit information. The outer cannula 12 may include fixation-screw engagement holes 22 that can be used to physically attach the reporting processor 10 to a tibial plate 32, as Figure 2 depicted.

[0093] Figure 2 A perspective view of a tibial component 30 that is available for implementing an exemplary embodiment of the present disclosure. For example, Figure 2 the tibial component 302 shown may include an implantable medical device for TKA, such as a tibial extension. Referring to Figure 2 the exemplary embodiment 2 shown, the tibial component 30 includes a tibial plate 32 that is physically attached to the upper surface of the tibia 34. For example, the tibial plate 32 may be a bottom plate section of an artificial knee joint (prosthesis) that can be implanted during a surgical procedure such as TKA. Before or during the surgical procedure, an implantable reporting processor from Figure 1 can be physically attached to the tibial plate 32 and also implanted in the tibia 34. For Figure 2 the exemplary embodiment shown, the tibial component 30 includes a tibial plate 32 and a reporting processor 10 that are surgically implanted to form a tibial extension 36.

[0094] Core gait is described as the movement associated with basic locomotion. It mainly occurs in the sagittal plane and has a frequency range of 0.5 Hz to 5 Hz. Most commonly, this can be considered the basic walking motion: starting from toe-off, the leg swings forward in combination with hip movement, bends at the knee, extends the leg to end with a heel strike, and rolls the foot back to the toe-off position.

[0095] Macrostability is a subset of the motion within the core gait and is related to musculoskeletal instability during joint loading in gait. For example, simply put, this can be considered as uncontrolled medial-lateral and / or anterior-posterior motion when getting out of a chair or going up and down stairs, and the frequency is between 2 Hz and 20 Hz and will cover a motion range of 5 mm to 10 cm.

[0096] Microinstability is another subset of the motion related to the intra-articular motion of the TKA due to misalignment between the femoral component and the tibial plate and its tibial insert. This motion can occur in the anterior-posterior plane and / or the medial-lateral plane, and the frequency is between 5 Hz and 50 Hz, covering a motion range of 0.1 mm to 2 cm in any one or combination of these directions. This motion may be due to incorrect sizing of the implant during the initial surgery, musculoskeletal structural changes related to weight loss and / or further injury, and / or joint wear resulting in changes in the geometry of the polymer puck and its associated fit with the femoral component. In addition, this motion may be due to loosening of the tibial component itself caused by bone subsidence and / or poor bone structure related to osteoporosis or other metabolic conditions affecting bone density. It should also be understood that the microinstability mentioned may be due to a combination of the above-mentioned motion patterns. Both macro- and microinstabilities may be related to pain and a decline in the quality of life indicators of the patient and may require intervention to address.

[0097] Sensor modalities implanted in the bone and integrated into the TKA, or even sensors implanted only in the bone but not necessarily coupled to the implant, address the signal fidelity and compliance limitations of external devices. However, there is still an unmet need to identify sensor data signatures indicative of instability to enable an "early warning system" with sufficient fidelity before bone erosion, TKA hardware degradation, and the onset of pain requiring more invasive / expensive interventions. The present disclosure addresses this need.

[0098] In an embodiment, the present disclosure provides methods and apparatuses that include: an implanted sensor coupled to the TKA hardware and / or coupled to bone, where the sensor has sufficient sensitivity and specificity to detect and identify movement of the tibia (or the tibial component of a TKI) consistent with an instability signature. An "instability signature" is defined as having a characteristic frequency response greater than about 20 Hz, or greater than about 25 Hz, or greater than about 30 Hz and less than about 90 Hz, or less than about 100 Hz, or less than about 110 Hz, and indicates that the TKA hardware is not fixedly engaged with the tibia. Normal kinematic movement during normal human movement is typically less than about 20 Hz, while movement of the device associated with wear, abrasion, and lack of osseointegration (collectively referred to as degradation) is typically associated with movement greater than 100 Hz. Device instability typically provides movement in the range of about 20 Hz to about 100 Hz. The present disclosure provides a sensor coupled to the intelligent implant of the present disclosure that has sufficient dynamic range to detect and distinguish normal kinematic movement (typically less than about 20 Hz), implant instability (typically about 20 - 100 Hz), and implant degradation or lack of osseointegration (typically greater than about 100 Hz). The sensor can have sufficient dynamic range that is high enough to not be saturated by normal kinematic movement but sensitive enough to detect small movements / shocks indicative of an "instability signature". Additionally, the sensor may have sufficient frequency response and sampling rate to distinguish without aliasing; i) normal kinematic movement, ii) instability signature, and iii) degradation signature.

[0099] In an embodiment, the present disclosure provides a medical device coupled to a motion sensor (e.g., one or more sensors selected from an accelerometer and a gyroscope that detect acceleration), and also provides an algorithm that can quantify the degree of instability; i.e., 1 mm movement vs. 2 mm movement or 5 degree movement vs. 10 degree movement, where the range of instability is determined by a defined transient signature that meets temporal and spectral definitions. Based on this information, the degree of instability can be evaluated over time, and an "intervention threshold" can be determined based on; i) clinical data, ii) anatomical thresholds, iii) TKA device design limitations and analysis, and other factors.

[0100] In one aspect, the present disclosure provides a report processor intended for implanting a medical device, such as a prosthesis, where the report processor monitors the status of the device after implantation, typically by obtaining kinematic data in the range of about 10 - 120 Hz. The report processor is also referred to as an implantable report processor or IRP. As discussed herein, the status of the device can include the integrity of the device, the movement of the device, the forces applied to the device, and other information related to the implanted device. The present disclosure also provides a medical device having a structure that can be easily assembled with an IRP. An implantable medical device equipped with an IRP is referred to herein as a smart implant, recognizing that the implant is monitoring its own status or condition to obtain data, where this data is stored in the implant and then, as needed, this data is transmitted to a separate device for viewing by, for example, a doctor.

[0101] For example, the smart implantable device of the present disclosure having suitable internal electronics can be used to monitor and measure the movement of a synthetic joint (prosthesis) implanted in a surgical patient via total knee arthroplasty (TKA), store the measurement data and unique identification information of the prosthetic components, and transmit the data to an external recipient (e.g., doctor, clinician, medical assistant, etc.) as needed. The IRP will include one or more sensors, such as gyroscopes, accelerometers, and temperature and pressure sensors, and these sensors can be located anywhere within the outer casing of the IRP, such as they can all be located on a PC board. In one embodiment, such as when the smart implant is a joint prosthesis, the IRP performs kinematic measurements, and in another embodiment, the IRP only performs kinematic measurements. Thus, a smart joint implant can include sensors for kinematic measurements to determine the movement experienced by the implanted prosthesis.

[0102] The smart medical device IRP of the present disclosure can include components for total or partial joint replacement, such as those that occur during total knee arthroplasty (TKA), where the IRP can be a component of the tibial component, femoral component, and / or tibial extension or attached to the tibial component, femoral component, and / or tibial extension; or such as those that occur during hip replacement, where the IRP can be a component of the femoral component or acetabular component for hip replacement or attached to the femoral component or acetabular component for hip replacement; or such as those that occur during shoulder replacement, where the IRP can be a component of the humeral component for shoulder replacement or attached to the humeral component for shoulder replacement. Other examples of medical devices that can be combined with the IRP to provide a smart implant include breast implants, lumbar interbody cages, artificial spinal discs, dynamic hip screws, and intramedullary rods for the leg.

[0103] The IRP and the medical device are each intended to be implanted in a living subject, such as a mammal, such as a human, horse, dog, etc. Thus, in one embodiment, the IRP is sterile, such as by treatment with sterilizing radiation or with ethylene oxide. In another embodiment, as two examples, the smart implant, including the IRP and the medical device, is sterile, also optionally by treatment with sterilizing radiation or ethylene oxide. To protect against the in vivo environment, in one embodiment, the IRP is hermetically sealed so that fluid cannot enter the IRP. The subject in which the medical device has been implanted may alternatively be referred to herein as a patient. In one embodiment, the subject / patient is human.

[0104] Due to the limited space within the body and / or within the prosthetic implant for placement of such devices, implantable devices need to be rugged and small or space efficient. The challenge for implantable devices with internal electronic components and internal or external transmission antennas to be commercially successful is that the device and / or the transmission antenna should not be unduly large, their power consumption should allow them to operate for a suitably long time, i.e., for an unlimited duration, and they should not be adversely affected by their local biological environment. The IRP of the present disclosure can have a suitable internal or external space-efficient and / or power-efficient antenna.

[0105] The smart implant will optionally have a power source required to operate the electronics that measure, record, and transmit data regarding the status of the implant within the IRP. Some medical implants already have a power supply. An example of an in vivo implantable prosthesis that can improve organ function and has a power supply is an implantable atrial defibrillator that detects when the heart enters an abnormal rhythm commonly referred to as "atrial fibrillation" and generates one or more electrical pulses to return the heart to a normal sinus rhythm. Typically, the power supply is in the form of a battery.

[0106] Because the charge on the battery can last for a relatively short period of time, the prosthesis is typically located in a region of the body from which it is practical to remove the prosthesis to replace the battery or recharge the battery. For example, an atrial defibrillator is typically implanted just under the skin of the patient's chest. To replace the battery, a surgeon makes an incision, removes the old defibrillator, implants a new defibrillator containing a new battery, and closes the incision. Alternatively, the patient or a doctor (such as a cardiologist) recharges the battery by placing a device that inductively (sometimes called magnetically) couples to recharge the battery over the implanted defibrillator without removing the defibrillator from the subject.

[0107] Unfortunately, removing the prosthesis to replace the battery is generally not advisable, at least because it involves invasive surgery that can be relatively expensive and may have adverse side effects such as infection and soreness. Although inductive recharging of an implanted battery is non-invasive, positioning the prosthesis so that the battery can be inductively recharged may be impractical or impossible. In addition, the size of the coil required to transfer power is relatively large with respect to the device, and this creates problems in the limited space available within the body. The recharging time may be unduly long, misalignment of the coil can result in excessive heat generation, which potentially can damage surrounding tissue, and the inductive battery configuration may render the implant incompatible with MRI use. Furthermore, battery chemistries compatible with recharging (i.e., secondary batteries) typically have significantly reduced energy storage capacity compared to similarly sized batteries constructed using non-rechargeable chemistries (i.e., primary batteries).

[0108] An alternative that can overcome the latter problem is to implant the battery at a location remote from the implanted prosthesis where inductive recharging of the battery is feasible. One advantage of implanting the battery remote from the implanted prosthesis is that the battery can be made larger and thus more long-lasting than when it is located inside the prosthesis. However, implanting the battery remote from the implanted prosthesis may have several disadvantages. For example, even if the battery is suitably positioned for inductive recharging, the charging device may be too expensive or complex for home use, the patient may forget to recharge the device, and regular visits to a doctor to recharge the battery may be inconvenient and costly for the patient. In addition, it may be difficult to implant wires for coupling the battery to the remotely (relative to the battery) implanted prosthesis, or if the implantable sensor is powered wirelessly from the rechargeable battery, the measurement capabilities of the sensor may be limited. Furthermore, because the battery is typically implanted just under the skin to improve the inductive-coupling coefficient, it may be visible and thus embarrassing to the patient, and it may make the patient physically uncomfortable.

[0109] Accordingly, an implantable reporting processor (IRP) can include a power source (e.g., a battery) and means for managing the power output of the implanted power source so that the power source will provide power for a sufficient period of time regardless of the location of the power source within the patient. The IRP can include the only power source present in the smart implant.

[0110] Examples of batteries suitable for use with an implantable reporting processor include those sized to fit within a container that can be placed inside the bone of a living patient and having a lifespan, such as several years, sufficient to power the electronic circuitry within the implantable reporting processor for a period of time, suitable for a prosthesis in which the implantable reporting processor is installed. The battery can be configured to be disposed directly within the bone or can be configured to be disposed within a portion of the implantable reporting processor that is disposed within the bone. Alternatively, the battery can be configured to be disposed in a living area other than the bone where it is impractical to recharge the battery and where it is impractical to replace the battery prior to replacing the prosthesis or other device associated with the battery.

[0111] The IRP generally includes an outer cannula that encapsulates multiple components. Exemplary suitable IRP components include a signal portal, an electronic assembly, and a power source. In one embodiment, the IRP does not include each of a signal portal, an electronic assembly, and a power source. The signal portal is used to receive and transmit wireless signals and can include, for example, an antenna for transmitting wireless signals. The electronic assembly includes a circuit assembly that can include, such as a PC board and electronic components formed on one or more integrated circuits (ICs) or chips, such as a radio transmitter chip, a real-time clock chip, one or more sensor components, such as an inertial measurement unit (IMU) chip, a temperature sensor, a pressure sensor, a pedometer, a memory chip, etc. In addition, the electronic assembly includes a plug assembly that provides a communication interface between the circuit assembly and the signal inlet (e.g., the antenna). The power source provides the energy required to operate the IRP and can be, for example, a battery. The IRP will also include one or more sensors, such as a gyroscope, an accelerometer, a pedometer, and temperature and pressure sensors, and these sensors can be located anywhere within the outer cannula of the IRP, such as they can all be located on the PC board. More precisely, one embodiment of the present disclosure relates to a space-efficient printed-circuit assembly (PCA) for an implantable reporting processor (IRP). The implantable reporting processor can also include multiple transmission antennas configured in different configurations. Thus, one embodiment of the present disclosure relates to multiple enhanced space-efficient and power-efficient antenna configurations for an implantable reporting processor such as the IRP.

[0112] Examples of implantable reporting processors include an outer cannula or housing sized to fit within a prosthesis or form a part of a prosthesis, at least a portion of which is designed to fit within the bone of a living patient. Electronic circuitry is disposed within the housing and is configured to provide information related to the prosthesis to a destination outside the patient. A battery is also disposed within the housing and is coupled to the electronic circuitry.

[0113] Examples of prostheses include receptacles for receiving an implantable reporting processor, which can be designed to fit within a cavity formed in the bone of a living patient. For example, the implantable reporting processor can be disposed within or form part of the tibial component or tibial extension of a knee prosthesis, where the tibial component or tibial extension is designed to fit within a cavity formed in the tibia of a living patient.

[0114] The power distribution of the electronic circuitry of the implantable reporting processor can be configured such that the battery has a desired expected lifetime appropriate for the type of prosthesis (or other device) associated with the battery. For example, such desired expected lifetimes can range from 1 to 15+ years, such as 10 years. One implementation of such circuitry includes a power supply node configured to couple to the battery, at least one peripheral circuit, a processing circuit coupled to the power supply node and configured to couple the at least one peripheral circuit to the power supply node, and a timing circuit coupled to the power supply node and configured to activate the processing circuit at one or more set times.

[0115] Before and after implanting the implantable reporting processor as part of a prosthesis into a patient, a base station can be provided to facilitate communication with the implantable reporting processor and act as an interface between the reporting processor and another computing system (such as a database or remote server on the "cloud"). The base station can have different configurations. For example, the base station can be configured for use by a surgeon or other professional before implanting the prosthesis. The base station can also be configured for use in the patient's residence. For example, the base station can be configured to periodically and automatically (e.g., while the patient is sleeping) poll the implantable reporting processor for information about the prosthesis obtained or generated by the processor and provide that information to another computing system via a wireless Internet connection for storage or analysis. And the base station can be configured for use in a doctor's office while the doctor is examining the operation and functionality of the prosthesis and the patient's health related to the prosthesis. Additionally, the network to which the base station belongs can include a voice-command device (e.g., Amazon Amazon Google ) configured to interact with the base station.

[0116] See, for example, U.S. Publication No. 2016 / 0310077, titled "Devices, Systems and Methods for Using and Monitoring Medical Devices", which is incorporated herein by reference in its entirety to disclose a medical device having sensors that can act as intelligent implants in accordance with the present disclosure, optionally supplemented as described herein. See also, for example, PCT Publication No. WO 2017 / 165717, titled "Implantable Reporting Processor for an Intelligent Implant", which is incorporated herein by reference in its entirety to disclose a medical device having sensors that can act as intelligent implants in accordance with the present disclosure, optionally supplemented as described herein. B. Systems with smart implants

[0117] An intelligent implant can be a component of the system of the present disclosure and can include one or more of the following: 1) a sensor that detects and / or measures the function of the implant and / or the immediate environment around the implant and / or the activity of the patient, 2) a memory that stores data from such detection and / or measurement, 3) an antenna that transmits data; 4) a base station that receives data generated by the sensor and can transmit the data and / or the analyzed data to a cloud-based location; 5) a cloud-based location where the data can be stored and analyzed, and the analyzed data can be stored and / or further analyzed; 6) a receiving terminal that receives the output from the cloud-based location, where this receiving station can be accessed by, for example, a healthcare professional or an insurance company or the manufacturer of the implant, and the output can identify the status of the implant and / or the function of the implant and / or the status of the patient receiving the implant, and can also provide recommendations for resolving any issues arising from the analysis of the raw data. A kinematic implantable device can be used as an intelligent implant to illustrate the system of the present disclosure, as provided in the following paragraphs. However, these systems can be used for any intelligent medical device, including the intelligent medical devices identified herein.

[0118] See, e.g., U.S. Publication No. 2016 / 0310077, titled "Devices, Systems and Methods for Using and Monitoring Medical Devices", which is incorporated herein by reference in its entirety to disclose a system in accordance with the present disclosure, optionally supplemented as described herein. See also, e.g., PCT Publication No. WO 2017 / 165717, titled "Implantable Reporting Processor for an Intelligent Implant", which is incorporated herein by reference in its entirety to disclose a system in accordance with the present disclosure, optionally supplemented as described herein. C. Joint implant and system having a joint implant

[0119] Figure 3 A relationship diagram of the kinematic implant device environment 1000 is illustrated. In the environment, the kinematic implant device 1002 is implanted into a patient (not shown in Figure 3 by a medical practitioner (not shown in Figure 3 ). The kinematic implant device 1002 is arranged to collect data, including operational data of the device 1002 together with kinematic data associated with a specific movement of the patient or a specific movement of a part of the patient's body (e.g., one of the patient's knees). The kinematic implant device 1002 communicates with one or more base stations or one or more smart devices during different stages of monitoring the patient.

[0120] For example, in conjunction with a medical procedure, the kinematic implant device 1002 is implanted into the patient. Synchronized with the medical procedure, the kinematic implant device 1002 communicates with an operating room base station (not shown in Figure 3 ). Subsequently, after fully recovering from the medical procedure, the patient returns home, where the kinematic implant device 1002 is arranged to communicate with the home base station 1004. At other times, the kinematic implant device 1002 is arranged to communicate with a doctor's office base station (not shown in Figure 3 ). The kinematic implant device 1002 communicates with each base station via a short-range network protocol, such as Medical Implant Communication Service (MICS), Medical Device Radio Communication Service (MedRadio), or some other wireless communication protocol suitable for use with the kinematic implant device 1002.

[0121] The kinematic implant device 1002 is implanted into the patient (not shown in Figure 3shown) within the body. The kinematic implantable device 1002 can be an independent medical device or it can be a component within a larger medical device, such as an artificial joint (e.g., knee replacement, hip replacement, spinal device, etc.), breast implant, femoral rod, or some other implantable medical device, which can desirably collect and provide in-situ kinematic data, operational data, or other useful data.

[0122] The kinematic implantable device 1002 includes one or more sensors to collect information and kinematic data associated with the use of the body part associated with the kinematic implantable device 1002. For example, the kinematic implantable device 1002 can include an inertial measurement unit that includes one or more gyroscopes, one or more accelerometers, one or more pedometers, or other kinematic sensors to collect acceleration data of the inner / outer axes, front / rear axes, and front / lower axes of the associated body part; angular velocity of the sagittal, frontal, and transverse planes of the associated body part; force, stress, tension, pressure, duress, migration, vibration, bending, stiffness, or some other measurable data.

[0123] The kinematic implantable device 1002 collects data at various different times and at various different rates during the monitoring process of the patient. In some embodiments, the kinematic implantable device 1002 can operate at multiple different stages during the monitoring of the patient, such that more data is collected shortly after the kinematic implantable device 1002 is implanted in the patient, but less data is collected after the patient has recovered.

[0124] In one non-limiting example, the monitoring process of the kinematic implantable device 1002 can include three different stages. The first stage can last for four months, during which kinematic data is collected once a day for one minute, every day of the week. After the first stage, the kinematic implantable device 1002 transitions to the second stage, which lasts for eight months and kinematic data is collected once a day for one minute, two days a week. And after the second stage, the kinematic implantable device 1002 transitions to the third stage, which lasts for nine years and kinematic data is collected for one minute once a week for the next nine years. Of course, the time periods associated with each stage can be longer, shorter, and otherwise controllable; for example, time periods compatible with those specified by medical-insurance telemedicine codes can be selected so that doctors billing according to telemedicine codes can receive the maximum reimbursement allowed by medical insurance companies. The type and amount of data collected are also controllable. An additional benefit of this passive monitoring process is that after the first stage of monitoring, the patient will not know when the data is being collected. Therefore, the data collected will be free from potential biases.

[0125] Along with the various different phases, the kinematic implantable device 1002 can operate in various modes to detect different types of movement. In this way, when a predetermined type of movement is detected, the kinematic implantable device 1002 can increase, decrease, or otherwise control the amount and type of kinematic data and other data collected.

[0126] In one example, the kinematic implantable device 1002 can use a pedometer to determine if a patient is walking. If the kinematic implantable device 1002 measures a determined number of steps that cross a threshold within a predetermined time, the kinematic implantable device 1002 can determine that the patient is walking. In response to that determination, the amount and type of data collected can be started, stopped, increased, decreased, or otherwise appropriately controlled. The kinematic implantable device 1002 can further control data collection based on certain conditions, such as when the patient stops walking, when a selected maximum amount of data for the collection session has been collected, when the kinematic implantable device 1002 times out, or based on other conditions. After collecting data in a particular session, the kinematic implantable device 1002 can stop collecting data until the next day, i.e., when the patient walks again, after the previously collected data has been offloaded (e.g., by transmitting the collected data to the home base station 1004), or according to one or more other conditions.

[0127] The amount and type of data collected by the kinematic implantable device 1002 can vary from patient to patient, and the amount and type of data collected can change for an individual patient. For example, the medical practitioner research data collected by the kinematic implantable device 1002 of a particular patient can adjust or otherwise control how the kinematic implantable device 1002 collects future data.

[0128] The amount and type of data collected by the kinematic implantable device 1002 can be different for different body parts, different types of movement, different patient demographics, or other differences. Alternatively or in addition, the amount and type of data collected can change over time based on other factors, such as how the patient is recovering or feeling, how long the monitoring process is expected to last, how much battery power remains and how much should be conserved, the type of movement being detected, the body part being moved, etc. In some cases, the data collected is supplemented with personal descriptive information provided by the patient, such as subjective pain data, quality of life metric data, comorbidities, perceptions, or expectations associated with the patient and the kinematic implantable device 1002.

[0129] In some embodiments, the kinematic implantable device 1002 is implanted into a patient to monitor movement or other aspects of a particular body part. Implanting the kinematic implantable device 1002 into the patient can occur in an operating room. As used herein, an operating room includes any office, room, building, or facility in which the kinematic implantable device 1002 is implanted into a patient. For example, the operating room can be a typical operating room in a hospital, an operating room in a surgical clinic or doctor's office, or any other operating room in which the kinematic implantable device 1002 is implanted into a patient.

[0130] An operating room base station (not shown in Figure 3 ) is used to configure and initialize the kinematic implantable device 1002 associated with the kinematic implantable device 1002 implanted in the patient. A communication relationship is formed between the kinematic implantable device 1002 and the operating room base station, for example, based on a polling signal transmitted by the operating room base station and a response signal transmitted by the kinematic implantable device 1002.

[0131] After forming the communication relationship that typically occurs before implanting the kinematic implantable device 1002, the operating room base station (not shown in Figure 3 ) transmits initial configuration information to the kinematic implantable device 1002. The initial configuration information can include, but is not limited to, a timestamp, a date stamp, an identification of the type and placement of the kinematic implantable device 1002, information about other implants associated with the kinematic implantable device, surgeon information, patient identity, operating room information, etc.

[0132] In some embodiments, the initial configuration information is passed unidirectionally; in other embodiments, the initial configuration is passed bidirectionally. The initial configuration information can define at least one parameter associated with the kinematic implantable device 1002 collecting kinematic data. For example, the configuration information can identify settings of one or more sensors on the kinematic implantable device 1002 for each of one or more operating modes (e.g., accelerometer range, accelerometer output data rate, gyroscope range, gyroscope output data rate, etc.). The configuration information can also include other control information, such as the initial operating mode of the kinematic implantable device 1002, specific movements that trigger a change in the operating mode, radio settings, data collection information (e.g., how often the kinematic implantable device 1002 wakes up to collect data, how often it collects data, how much data to collect), identification information for the home base station 1004, the smart device 1005, and the connected personal assistant 1007, and other control information related to the implantation or operation of the kinematic implantable device 1002. Examples of the connected personal assistant 1007, also referred to as a smart speaker, include Amazon Amazon Google Patient monitor, Comcast health tracking speaker, and Apple

[0133] In some embodiments, the configuration information can be pre - stored in an operating room base station (not shown in Figure 3 ), or on a related computing device. In other embodiments, a surgeon, surgical technician, or some other medical practitioner can input control information and other parameters into the operating room base station for transmission to the kinematic implant device 1002. In at least one such embodiment, the operating room base station can communicate with an operating room configuration computing device (not shown in Figure 3 ). The operating room configuration computing device includes an application with a graphical user interface that enables a medical practitioner to input configuration information for the kinematic implant device 1002. In various embodiments, the application executed on the operating room configuration computing device can have some predefined configuration information that may or may not be adjustable by the medical practitioner.

[0134] The operating room configuration computing device (not shown in FIG. >100) communicates the configuration information to the operating room base station (not shown in Figure 3 ) via a wired or wireless network connection (such as, via a USB connection, a Bluetooth connection, a Bluetooth Low Energy (BTLE) connection, or a Wi - Fi connection), which in turn communicates the configuration information to the kinematic implant device 1002.

[0135] The operating room configuration computing device (not shown in Figure 3 ) can also display information to a surgeon, surgical technician, or other medical practitioner about the kinematic implant device 1002 or the operating room base station (not shown in Figure 3 ). For example, if the kinematic implant device 1002 cannot store or access the configuration information, if the kinematic implant device 1002 is unresponsive, if the kinematic implant device 1002 identifies a problem with one of the sensors or radios during an initial self - test, if the operating room base station (not shown in Figure 3 ) is unresponsive or malfunctioning, or for other reasons, the operating room configuration computing device can display an error message.

[0136] Although the operating room base station (not shown in Figure 3 ) and the operating room configuration computing device (not shown in Figure 3 ) are shown as separate devices, the embodiments are not limited thereto; rather, the functions of the operating room configuration computing device and the operating room base station can be included in a single computing device or in separate devices as shown. In this way, in one embodiment, a medical practitioner can be enabled to directly input configuration information into the operating room base station.

[0137] Once the kinematic implantable device 1002 is implanted in a patient and the patient returns home, the home base station 1004, a smart device 1005 (e.g., the patient's smartphone), a connected personal assistant 1007, or two or more of the home base station, the smart device, and the connected personal assistant can communicate with the kinematic implantable device 1002. The kinematic implantable device 1002 can collect kinematic data at a determined rate and time, a variable rate and time, or otherwise controllable rate and time. Data collection can begin when the kinematic implantable device 1002 is initialized in an operating room, when directed by a doctor, or at some later point in time. At least some of the data collected by the kinematic implantable device 1002 can be transmitted directly to the home base station 1004, directly to the smart device 1005, directly to the connected personal assistant 1007, transmitted to the base station via one or two of the smart device and the connected personal assistant, transmitted to the smart device via one or two of the base station and the connected personal assistant, or transmitted to the connected personal assistant via one or two of the smart device and the base station. Here, "one or two" means via a single item, as well as via two items in sequence or in parallel. For example, data collected by the kinematic implantable device 1002 can be transmitted to the home base station 1004 via the separate smart device 1005, via the separate connected personal assistant 1007, sequentially via the smart device and the connected personal assistant, sequentially via the connected personal assistant and the smart device, and directly and possibly simultaneously via both the smart device and the connected personal assistant. Similarly, data collected by the kinematic implantable device 1002 can be transmitted to the smart device 1005 via the separate home base station 1004, via the separate connected personal assistant 1007, sequentially via the home base station and the connected personal assistant, sequentially via the connected personal assistant and the home base station, and directly and possibly simultaneously via both the home base station and the connected personal assistant. Further in an example, data collected by the kinematic implantable device 1002 can be transmitted to the connected personal assistant 1007 via the separate smart device 1005, via the separate home base station 1004, sequentially via the smart device and the home base station, sequentially via the home base station and the smart device, and directly and possibly simultaneously via both the smart device and the home base station.

[0138] In various embodiments, one or more of the femtocell 1004, the smart device 1005, and the connected personal assistant 1007 send echo messages (pings) to the kinematic implantable device 1002 at regular, predetermined, or other times to determine whether the kinematic implantable device 1002 is within the communication range of one or more of the femtocell, the smart device, and the connected personal assistant. Based on the response of the kinematic implantable device 1002, one or more of the femtocell 1004, the smart device 1005, and the connected personal assistant 1007 determine that the kinematic implantable device 1002 is within the communication range, and the kinematic implantable device 1002 may request, command, or otherwise instruct the transfer of data it has collected to one or more of the femtocell 1004, the smart device 1005, and the connected personal assistant 1007.

[0139] Each of one or more of the femtocell 1004, the smart device 1005, and the connected personal assistant 1007 may be provided with a corresponding optional user interface. The user interface may be formed as a multimedia interface for one-way or two-way transmission of one or more types of multimedia information (such as video, audio, tactile, etc.). Via the corresponding user interfaces of one or more of the femtocell 1004, the smart device 1005, and the connected personal assistant 1007, a patient (not shown in Figure 3 ), or a patient's colleague (not shown in Figure 3 ) may input other data to supplement the kinematic data collected by the kinematic implantable device 1002. For example, the user may input personal descriptive information (such as age change, weight change), changes in medical conditions, comorbidities, pain levels, quality of life, an indication of how the implanted prosthesis 1002 "feels", or other subjective metric data, personal information of medical practitioners, etc. In these embodiments, the personal descriptive information may be input using a keyboard, mouse, touch screen, microphone, wired or wireless computing interface, or some other input means. In cases where personal descriptive information is collected, the personal descriptive information may include one or more identifiers or otherwise be associated with one or more identifiers that associate the information with the unique identifiers of the kinematic implantable device 1002, the patient, the relevant medical practitioner, the relevant medical institution, etc.

[0140] In some of these cases, the corresponding optional user interfaces of each of one or more of the femtocell 1004, the smart device 1005, and the connected personal device 1007 may also be arranged to deliver information associated with the kinematic implantable device 1002 from, for example, a medical practitioner to the user. In these cases, the information delivered to the user may be delivered via a video screen, an audio output device, a tactile transducer, a wired or wireless computing interface, or some other similar means.

[0141] In embodiments where a user interface is arranged in one or more of the femtocell 1004, the smart device 1005, and the connected personal assistant 1007, the user interface may be formed with an internal user interface arranged for communication coupling with the patient portal device. The patient portal device may be a smart phone, a tablet computer, a wearable device, a weight or other health measurement device (e.g., a thermometer, a bathroom scale, etc.), or some other computing device capable of wired or wireless communication. In these cases, the user can input personal description information, and the user can also receive information associated with the implantable device 1002.

[0142] The femtocell 1004 uses the patient's home network 1006 to transmit the collected data (i.e., kinematic data and, in some cases, personal description information) to the cloud 1008. The home network 1006, which may be a local area network, provides access from the patient's home to a wide area network such as the Internet. In some embodiments, the femtocell 1004 may use a Wi-Fi connection to connect to the home network 1006 and access the Internet. In other embodiments, the femtocell 1004 may be connected to the patient's home computer (not shown in Figure 3 ), such as via a USB connection, which itself is connected to the home network 1006.

[0143] The smart device 1005 may communicate directly with the kinematic implantable device 1002 via, for example, a Blue -compatible signal, and may use the patient's home network 1006 to transmit the collected data (i.e., kinematic data and, in some cases, personal description information) to the cloud 1008, or may communicate directly with the cloud, for example, via a cellular network. Alternatively, the smart device 1005 is configured to communicate directly with one or both of the base station 1004 and the connected personal assistant 1007 via, for example, a Blue -compatible signal, and is not configured to communicate directly with the kinematic implantable device 1002.

[0144] In addition, the connected personal assistant 1007 may communicate directly with the kinematic implantable device 1002 via, for example, a Blue -compatible signal, and may use the patient's home network 1006 to transmit the collected data (i.e., kinematic data and, in some cases, personal description information) to the cloud 1008, or may communicate directly with the cloud, for example, via a modem / Internet connection or a cellular network. Alternatively, the connected personal assistant 1007 is configured to communicate directly with one or both of the base station 1004 and the smart device 1005 via, for example, a Blue -compatible signal, and is not configured to communicate directly with the kinematic implantable device 1002.

[0145] In addition to transmitting the collected data to the cloud 1008, one or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1007 may also obtain data, commands, or other information from the cloud 1008 directly or via the home network 1006. One or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1007 may provide some or all of the received data, commands, or other information to the kinematic implant device 1002. Examples of such information include, but are not limited to, updated configuration information, diagnostic requests to determine whether the kinematic implant device 1002 is operating properly, data collection requests, and other information.

[0146] The cloud 1008 may include one or more server computers or databases to aggregate the data collected from the kinematic implant device 1002, and in some cases, personal descriptive information collected from the patient (not shown in Figure 3 ), data collected from other kinematic implant devices (not illustrated), and in some cases, personal descriptive information collected from other patients. In this way, the cloud 1008 can create various different metrics regarding the data collected from each of the multiple kinematic implant devices implanted in different patients. This information can help determine whether the kinematic implant device is operating properly. The information collected may also be helpful for other purposes, such as determining which specific devices may not be working properly, determining whether a procedure or condition associated with the kinematic implant device is helpful to the patient (e.g., whether a knee replacement is operating properly and relieving the patient's pain), and determining other medical information.

[0147] At different times during the entire monitoring process, the patient may be required to visit a medical practitioner for a follow-up appointment. The medical practitioner may be the surgeon who implanted the kinematic implant device 1002 in the patient or a different medical practitioner who supervises the patient's monitoring process, physical therapy, and recovery. For various different reasons, the medical practitioner may wish to collect real-time data from the kinematic implant device 1002 in a controlled environment. In some cases, the patient's request to visit the medical practitioner may be delivered through the respective optional two-way user interfaces of each of one or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1007.

[0148] The medical practitioner uses a doctor's office base station (not shown in Figure 3 to transfer additional information between the doctor's office base station and the kinematic implant device 1002. Alternatively or in addition, the medical practitioner uses a doctor's office base station (not shown in Figure 3(in the Chinese version) commands are passed to the kinematic implant device 1002. In some embodiments, the doctor's office base station instructs the kinematic implant device 1002 to enter a high-resolution mode to temporarily increase the rate or type of data collected over a short period of time. The high-resolution mode directs the kinematic implant device 1002 to collect a different amount (e.g., a large amount) of data while the medical practitioner is also monitoring the patient's activities.

[0149] In some embodiments, the doctor's office base station (not shown in Figure 3 (the Chinese version)) enables the medical practitioner to input events or pain markers, which can be synchronized with the high-resolution data collected by the kinematic implant device 1002. For example, assume that the kinematic implant device 1002 is a component in a knee replacement. When the kinematic implant device 1002 is in high-resolution mode, the medical practitioner can have the patient walk on a treadmill. While the patient is walking, the patient may complain of pain in his / her knee. The medical practitioner can click a pain marker button on the doctor's office base station to indicate patient discomfort. The doctor's office base station records the marker and the time the marker was entered. When the timing of the marker is synchronized with the timing of the collected high-resolution data, the medical practitioner can analyze the data to try and determine the cause of the pain.

[0150] In other embodiments, the doctor's office base station (not shown in Figure 3 (the Chinese version)) can provide updated configuration information to the kinematic implant device 1002. The kinematic implant device 1002 can store the updated configuration information, which can be used to adjust parameters associated with the collection of kinematic data. For example, if the patient is in good condition, the medical practitioner can direct a reduction in the frequency of data collection by the kinematic implant device 1002. Conversely, if the patient is experiencing an unexpected amount of pain, the medical practitioner can direct the kinematic implant device 1002 to collect additional data over a determined period of time (e.g., several days). The medical practitioner can use the additional data to diagnose and treat specific problems. In some cases, the additional data can include personal descriptive information provided by the patient after the patient has left the presence of the medical practitioner and is no longer within the range of the doctor's office base station (not shown in 3). In these cases, the personal descriptive information can be collected and delivered via one or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1007. Firmware within the kinematic implant device and / or the base station will provide safeguards to limit the duration of such enhanced monitoring to ensure that the battery remains sufficiently powered to maintain the life cycle of the implant.

[0151] In various embodiments, the doctor's office base station (not shown in Figure 3 (the Chinese version)) can communicate with a doctor's office configuration computing device (not shown in Figure 3Communication. The doctor's office configured computing device includes an application with a graphical user interface that enables a medical practitioner to input commands and data. Some or all of the commands, data, and other information can later be transmitted to the kinematic implantable device 1002 via the doctor's office base station. For example, in some embodiments, a medical practitioner can use the graphical user interface to instruct the kinematic implantable device 1002 to enter its high-resolution mode. In other embodiments, a medical practitioner can use the graphical user interface to input or modify the configuration information of the kinematic implantable device 1002. The doctor's office configured computing device transmits information (such as commands, data, or other information) to the doctor's office base station via a wired or wireless network connection (such as via a USB connection, a Bluetooth connection, or a Wi-Fi connection), which in turn transmits some or all of the information to the kinematic implantable device 1002.

[0152] The doctor's office configured computing device (not shown in Figure 3 can also display other information to the medical practitioner about the kinematic implantable device 1002, about the patient (such as personal description information), or about the doctor's office base station. For example, the doctor's office configured computing device can display high-resolution data collected by the kinematic implantable device 1002 and transmitted to the doctor's office base station (not shown in Figure 3 ). If the kinematic implantable device 1002 cannot store or access configuration information, if the kinematic implantable device 1002 is unresponsive, if the kinematic implantable device 1002 identifies a problem with one of the sensors or radios, if the doctor's office base station is unresponsive or fails, or for other reasons, the doctor's office configured computing device can also display an error message.

[0153] In some embodiments, the doctor's office configured computing device (not shown in Figure 3 can access the cloud 1008. In at least one embodiment, a medical practitioner can use the doctor's office configured computing device to access data stored in the cloud 1008 that was previously collected by the kinematic implantable device 1002 and transmitted to the cloud 1008 via one or both of the home base station 1004 and the smart device 1005. Similarly, the doctor's office configured computing device can transmit high-resolution data obtained from the kinematic implantable device 1002 to the cloud 1008 via the doctor's office base station. In some embodiments, the doctor's office base station can have Internet access and can be capable of directly transmitting high-resolution data to the cloud 1008 without using the doctor's office configured computing device.

[0154] In various embodiments, when the patient is not in the medical practitioner's office, the medical practitioner may update the configuration information of the kinematic implantable device 1002. In these cases, the medical practitioner may utilize a doctor's office configuration computing device (not shown in Figure 3 to transmit updated configuration information to the kinematic implantable device 1002 via the cloud 1008. One or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1007 may obtain the updated configuration information from the cloud 1008 and pass the updated configuration information to the cloud. This may allow the medical practitioner to remotely adjust the operation of the kinematic implantable device 1002 without the patient having to come to the medical practitioner's office. This may also allow the medical practitioner to send a message to the patient (not shown in Figure 3 in response to, for example, personal description information provided by the patient and passed through one or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1007 to the doctor's office base station (not shown in Figure 3 ). For example, if a patient with a knee prosthesis says to the connected personal assistant 1007, "My leg hurts when I walk," then the medical practitioner may prescribe pain medication and have the connected personal assistant "say" to the patient, "The doctor has adjusted to your preferred pharmacy; the prescription will be available at 4 PM."

[0155] Although the doctor's office base station (not shown in Figure 3 and the doctor's office configuration computing device (not shown in Figure 3 are described as separate devices, the embodiments are not so limited; rather, the functions of the doctor's office configuration computing device and the doctor's office base station may be included in a single computing device or separate devices (as shown). In this manner, in one embodiment, the medical practitioner may be able to directly input configuration information or markers into the doctor's office base station and view high-resolution data (and synchronized marker information) on a display on the doctor's office base station

[0156] Still referring to Figure 3, alternative embodiments are considered. For example, one or two of the femtocell 1004, smart device 1005, and connected personal assistant 1007 may be omitted from the kinematic implant device environment 1000. Additionally, each of the femtocell 1004, smart device 1005, and connected personal assistant 1007 may be configured to communicate with one or two of the implantable device 1002 and the cloud 1008 via another one or two of the femtocell, smart device, and connected personal assistant. Further, the smart device 1005 may be temporarily shrunk to an interface of the implantable prosthesis 1002 and may be any suitable device other than a smartphone, such as a smartwatch, a smart patch, and any IoT device, such as a coffee pot, capable of serving as an interface of the implantable device 1002. Additionally, one or more of the femtocell 1004, smart device 1005, and connected personal assistant 1007 may act as a communication hub for a plurality of prostheses implanted in one or more patients. Additionally, one or more of the femtocell 1004, smart device 1005, and connected personal assistant 1007 may automatically order or reorder prescriptions or medical supplies (such as a knee brace) in response to patient input or implantable-prosthesis input (such as pain level, instability fluid), if such ordering or reordering has been pre-authorized by a healthcare professional and an insurance company; alternatively, one or more of the femtocell, smart device, and connected personal assistant may be configured to request authorization for the ordering or reordering from a healthcare professional or an insurance company. Additionally, one or more of the femtocell 1004, smart device 1005, and connected personal assistant 1007 may be configured with a personal assistant, such as or Additionally, one or more alternative embodiments 4 through 27 described in conjunction with Figures 4 to 27 may be applicable to the kinematic implant device environment 1000.

[0157] Figure 4 is a diagram of an implantable circuit 1010 configured to be included within or otherwise used with an alert kinematic implant, such as a knee implantable prosthesis as part of a total knee arthroplasty (TKA).

[0158] The circuit 1010 is powered by a battery or other suitable implantable power source 1012 and includes a fuse 1014, switches 1016 and 1018, a clock generator and power management unit 1020, an inertial measurement unit (IMU) 1022, a memory circuit 1024, a radio frequency (RF) transceiver 1026, an RF filter 1028, an RF-compatible antenna 1030, and a control circuit 1032. Examples of some or all of these components are described elsewhere in this application or in U.S. Serial No. 16 / 084,544, which is incorporated herein by reference in all jurisdictions in which such incorporation by reference is permitted.

[0159] The battery 1012 can be any suitable battery, such as a Lithium Carbon Monofluoride (LiCFx) battery, or other storage battery configured to store energy to power the circuit 1000 over the expected lifespan of the kinematic implant (e.g., 5 to 25+ years).

[0160] The fuse 1014 can be any suitable fuse (e.g., permanent) or circuit breaker (e.g., resettable) configured to prevent the battery 1012 or current flowing from the battery from harming the patient and damaging the battery and one or more components of the circuit 1000. For example, the fuse 1014 can be configured to prevent the battery 1012 from generating heat sufficient to burn the patient, damage the circuit 1000, damage the battery, or damage the structural components of the kinematic implant.

[0161] The switch 1016 is configured to couple the battery 1012 to the IMU 1022, or decouple the battery from the IMU 1022, in response to a control signal from the control circuit 1032. For example, the control circuit 1032 can be configured to generate a control signal with an open state that causes the switch 1016 to open and thus decouple power from the IMU 1022 during a sleep mode or other low-power mode to conserve energy and thus extend the life of the battery 1012. Similarly, the control circuit 1032 can also be configured to generate a control signal with a closed state that causes the switch 1016 to close and thus couple power to the IMU 1022 after "waking up" from the sleep mode or otherwise exiting another low-power mode. This low-power mode can be used only for the IMU 1022 or for one or more other components of the IMU and the implantable circuit 1010.

[0162] The switch 1018 is configured to couple the battery 1012 to the memory circuit 1024, or decouple the battery from the memory circuit 1024, in response to a control signal from the control circuit 1032. For example, the control circuit 1032 can be configured to generate a control signal with an open state that causes the switch 1018 to open and thus decouple power from the memory 1024 during a sleep mode or other low-power mode to conserve energy and thus extend the life of the battery 1012. Similarly, the control circuit 1032 can also be configured to generate a control signal with a closed state that causes the switch 1018 to close and thus couple power to the memory 1024 after "waking up" from the sleep mode or otherwise exiting another low-power mode. This low-power mode can be used only for the memory circuit 1024 or for the memory circuit and one or more other components of the implantable circuit 1010.

[0163] The clock and power management circuit 1020 can be configured to generate clock signals for one or more of the other components of the implantable circuit 1010, and can be configured to generate periodic commands or other signals (e.g., interrupt requests) in response to the control circuit 1032 causing one or more components of the implantable circuit to enter or exit sleep or other low-power modes. The clock and power management circuit 1020 can also be configured to regulate the voltage from the battery 1012 and provide a regulated power supply voltage to some or all of the other components of the implantable circuit 1010.

[0164] The IMU 1022 has a reference frame including coordinate x, y, and z axes, and can be configured to measure or otherwise quantify the acceleration experienced by the IMU along each of the x, y, and z axes, and the angular velocity experienced by the IMU about each of the x, y, and z axes. Such a configuration of the IMU 1022 is at least a six-axis configuration because the IMU 1022 measures six unique quantities, namely acc x (t), acc y (t), acc z (t), Ω x (t), Ω y (t), and Ω z (t). Alternatively, the IMU 1022 can be configured as a nine-axis configuration, where the IMU can use gravity to compensate for or otherwise correct the cumulative error of acc x (t), acc y (t), acc z (t), Ω x (t), Ω y (t), and Ω z (t). However, in embodiments where the IMU measures acceleration and angular velocity only in short bursts (e.g., 0.10–100), for many applications, the cumulative error can generally be ignored without exceeding the respective error tolerances. The IMU 1022 can include corresponding analog-to-digital converters (ADCs) for each of the x, y, and z accelerometers and gyroscopes. Corresponding sample-and-hold circuits for each of the x, y, and z accelerometers and gyroscopes can be included, as well as as few as one ADC shared by the accelerometers and gyroscopes. Including fewer than one ADC for each accelerometer and each gyroscope can reduce one or both of the size and circuit density of the IMU 1022, and can reduce the power consumption of the IMU. However, since the IMU 1022 includes corresponding sample-and-hold circuits for each accelerometer and each gyroscope, samples of the analog signals generated by the accelerometers and gyroscopes can be acquired at the same or different numbers of samples, at the same or different sampling rates, and at the same or different output data rates (ODRs).

[0165] It can be any suitable non-volatile memory circuit, such as an EEPROM or FLASH memory, and can be configured to store data written by the control circuit 1032 and provide data in response to a read command from the control circuit.

[0166] The RF transceiver 1026 can be a conventional transceiver configured to allow the control circuit 1032 (and optionally the fuse 1014) to communicate with a base station (not shown in Figure 4 the figure) configured to be used with a kinematic implantable device. For example, the RF transceiver 1026 can be any suitable type of transceiver (e.g., Bluetooth, Bluetooth Low Energy (BTLE), and ) and can be configured to operate according to any suitable protocol (e.g., MICS, ISM, Bluetooth, Bluetooth Low Energy (BTLE), and ) and can be configured to operate in a frequency band of 1 MHz - 5.4 GHz or any other suitable range.

[0167] The filter 1028 can be any suitable band-pass filter, such as a surface acoustic wave (SAW) filter or a bulk acoustic wave (BAW) filter.

[0168] The antenna 1030 can be any antenna suitable for the frequency band in which the RF transceiver 1026 generates signals for antenna transmission and for the frequency band in which the base station (not shown in Figure 4 the figure) generates signals for antenna reception.

[0169] The control circuit 1032, which can be any suitable implantable report processor (IRP) such as a microcontroller or a microprocessor, is configured to control the configuration and operation of one or more other components of the implantable circuit 1010. For example, the control circuit 1032 is configured to control the IMU 1022 to measure the movement of an implantable prosthesis associated with the implantable circuit 1010, to quantify the quality of such measurements (e.g., is the measurement "good" or "bad"?), to store the measurement data generated by the IMU in the memory 1024, to generate a message including the stored data as a payload, to package the message, and to provide the message packet to the RF transceiver 1026 for transmission to a base station (not shown in Figure 4 the figure). The control circuit 1032 can also be configured to execute commands received from the base station (not shown in Figure 4Commands received from, e.g., a base station. For example, the control circuit 1032 can be configured to receive configuration data from a base station and provide the configuration data to the components of the implantable circuit 1010 to which the base station has directed the configuration data. If the base station directs the configuration data to the control circuit 1032, the control circuit is configured to configure itself in response to the configuration data.

[0170] Still referring to Figure 4 , the operation of the circuit 1010 is described according to one embodiment, in which the implantable prosthesis in which the circuit is disposed or otherwise associated with the circuit is implanted in a patient (not shown in Figure 4 )).

[0171] A fuse 1014 that is normally electrically closed is configured to open electrically in response to an event that could harm the patient in which the implantable circuit 1010 is located or damage the battery 1012 of the implantable circuit if the event persists for longer than a safety duration. Events in response to which the fuse 1014 can open electrically include overcurrent conditions, overvoltage conditions, overtemperature conditions, over-current-time conditions, over-voltage-time conditions, and over-temperature-time conditions. An overcurrent condition occurs in response to the current through the fuse 1014 exceeding an overcurrent threshold. Similarly, an overvoltage condition occurs in response to the voltage through the fuse 1014 exceeding an overvoltage threshold, and an overtemperature condition occurs in response to the temperature of the fuse exceeding a temperature threshold. An over-current-time condition occurs in response to the integral of the current through the fuse 1014 exceeding a current-time threshold within a measurement time window (e.g., ten seconds), where the window can "slide" forward in time such that the window always extends backward from the current time by the length of the window (in time). Alternatively, an over-current-time condition occurs if the current through the fuse 1014 exceeds the overcurrent threshold for longer than a threshold time. Similarly, an over-voltage-time condition occurs in response to the integral of the voltage through the fuse 1014 within a measurement time window, and an over-temperature-time condition occurs in response to the integral of the temperature of the fuse within a measurement time window. Alternatively, an over-voltage-time condition occurs if the voltage through the fuse 1014 exceeds the overvoltage threshold for longer than a threshold time, and an over-temperature-time condition occurs if the temperature associated with the fuse 1014, battery 1012, or implantable circuit 1010 exceeds an overtemperature threshold for longer than a threshold time. However, even if the fuse 1014 opens, decoupling power from the implantable circuit 1010, the mechanical and structural components of the kinematic prosthesis associated with the implantable circuit (not shown in Figure 4it can still operate fully. For example, if the kinematic prosthesis is a knee prosthesis, the knee prosthesis can still fully perform the functions of the patient's knee; however, the lost capabilities are the ability to detect and measure the kinematic movements of the prosthesis, generate and store data representing the measured kinematic movements, and provide the stored data to a base station or other destinations external to the kinematic prosthesis. The following will be described in conjunction with Fig. 27 the operation of the fuse will be further described.

[0172] The control circuit 1032 is configured to cause the IMU 1022 to measure the movement within a time window (e.g., ten seconds, twenty seconds, one minute) in response to the movement of the kinematic prosthesis associated with the implantable circuit 1010, to determine whether the measured movement is a qualified movement, to store the data representing the measured qualified movement, and to cause the RF transceiver 1026 to transmit the stored data to a base station or other sources external to the prosthesis.

[0173] For example, the IMU 1022 can be configured to start sampling the sensed signals output from one or more of its accelerometers and one or more of its gyroscopes in response to the movement detected within a corresponding time period (day), and the control circuit 1032 can analyze the samples to determine whether the detected movement is a qualified movement. Further in an example, the IMU 1022 can detect movement in any conventional manner, such as by the movement of one or more of its one or more accelerometers. In response to the IMU 1022 notifying the control circuit 1032 of the detected movement, the control circuit can associate the samples from the IMU with the stored accelerator and gyroscope samples that are generated by computer simulation or when the patient or another patient walks normally, and can measure the duration of the movement (the time is equal to the number of samples multiplied by the reciprocal of the sampling rate). If the samples of the accelerator and gyroscope output signals are related to the respective stored samples and the duration of the movement is greater than a threshold time, the control circuit 1032 effectively marks the movement as a qualified movement.

[0174] In response to determining that the movement is a qualified movement, the control circuit 1032 stores the samples together with other data in the memory circuit 1024, and can extend the life of the battery 1012 by turning on the switch 1016 to disable the IMU 1022 until the next time period (e.g., the next day or next week). The clock and power management circuit 1020 can be configured to generate periodic timing signals, such as interrupts, to start each time period. For example, the control circuit 1032 can close the switch 1016 in response to such a timing signal from the clock and power management circuit 1020. Additionally, the other data can include, for example, the respective sampling rates of each set of accelerometer and gyroscope samples, the respective timestamps indicating the time at which the IMU 1022 acquired the respective sets of samples, the respective sampling times of each set of samples, the identifier of the implantable prosthesis (e.g., serial number), and the patient identifier (e.g., number or name). If the sampling rates, timestamps, and sampling times of each set of samples are the same (i.e., the signal samples from all accelerometers and gyroscopes are acquired at the same time and at the same rate), the capacity of the other data can be significantly reduced because the header only includes one sampling rate, one timestamp, and one set of sampling times for all sets of samples. Further, the control circuit 1032 can encrypt some or all of the data in a conventional manner before storing the data in the memory 1024. For example, the control circuit 1032 can dynamically encrypt some or all of the data such that at any given time, the same data has a different encrypted form compared to when it was encrypted at another time.

[0175] As described below in connection with Figures 9 to 24 and elsewhere in this application, the stored data samples of the signals generated by one or more accelerometers and one or more gyroscopes of the IMU 1022 can provide clues about the condition of the implantable prosthesis. For example, one can analyze the data samples (e.g., using a remote server, such as a cloud server) to determine whether the surgeon has correctly implanted the prosthesis, to determine the current instability and degradation levels exhibited by the implanted prosthesis, to determine the instability and degradation characteristics over time, and to compare the instability and degradation characteristics with benchmark instability and degradation characteristics developed using random simulations or data from a statistically significant group of patients.

[0176] In addition, the sampling rate, output data rate (ODR), and sampling frequency of the IMU 1022 can be configured to any suitable values. For example, the sampling rate can be fixed at any suitable value, such as 3200 Hz. The ODR, which can be no greater than the sampling rate and is generated by periodically "discarding" samples, can be any suitable value, such as 800 Hz. And the sampling frequency (the reciprocal of the interval between sampling periods) of qualifying events can be any suitable value, such as twice a day, once a day, once every two days, once a week, once a month, or more or less frequently. Also, the sampling rate or ODR can vary according to the type of event being sampled. For example, to detect whether a patient is walking without analyzing the patient's gait or implant instability or wear, the sampling rate or ODR can be 200 Hz, 25 Hz, or lower. Thus, such a low-resolution mode can be used to detect precursors (the patient walking with a knee prosthesis) of qualifying events (the patient walking at least ten consecutive steps), because the "search" for qualifying events may include multiple false detections before a qualifying event is detected. By using a lower sampling rate or ODR, the IMU 1032 can conserve energy during the search and increase the sampling rate or ODR (e.g., to 800 Hz, 1600, or 3200 Hz) to sample only the detected qualifying events so that the accelerator and gyroscope signals have sufficient sampling resolution to analyze the samples, such as implant instability and wear.

[0177] Still referring to Figure 4 , in response to being polled by a base station (not shown in Figure 4 ), or another device external to the implanted prosthesis, the control circuit 1032 generates a conventional message having a payload and a header. The payload includes stored samples of signals generated by the IMU 1022 accelerometer and gyroscope, and the header includes the sample partitioning in the payload (i.e., at what bit positions the samples of the x-axis accelerometer are located, at what bit positions the samples of the x-axis gyroscope are located, etc.), the respective sampling rates of each set of accelerometer and gyroscope samples, a timestamp indicating the time at which the IMU 1022 acquired the samples, an identifier of the implantable prosthesis (e.g., serial number), and a patient identifier (e.g., number or name).

[0178] The control circuit 1032 generates a data packet including the message according to a conventional data-packet protocol. Each packet can also include a packet header that includes, for example, the serial number of the packet so that even if the packets are transmitted or received out of order, the receiving device can correctly sort the packets.

[0179] The control circuit 1032 encrypts some or all parts of each data packet according to a conventional encryption algorithm, for example, and performs error coding on the encrypted data packet. For example, the control circuit 1032 encrypts at least the prosthesis and patient identifiers so that the data packet complies with the Health Insurance Portability and Accountability Act (HIPAA).

[0180] The control circuit 1032 provides the encrypted and error-coded data packet to the RF transceiver 1026, which transmits the data packet via the filter 1028 and the antenna 1030 to a destination external to the implantable prosthesis, such as the base station 1004 ( Figure 3 ). The RF transceiver 1026 may transmit the data packet according to any suitable data-packet transmission protocol.

[0181] Still referring to Figure 4 , alternative embodiments of the implantable circuit 1010 are considered. For example, the RF transceiver may perform encryption or error coding to replace or supplement the control circuit 1032. In addition, one or both of the switches 1016 and 1018 may be omitted from the implantable circuit 1010. In addition, the implantable circuit 1010 may include components different from those described herein and may omit one or more of the components described herein. In addition, one or more embodiments described in connection with Figure 3 and Figures 5 to 27 may be applicable to the implantable circuit 1010.

[0182] Figure 5 is a diagram of a base station circuit 1040 configured to be included within or otherwise used with a base station, such as Figure 3 the home base station 1004, the base station being configured to communicate with Figure 4 the implantable circuit 100.

[0183] The base station circuit 1040 is powered by a power supply 1042 and includes first and second antennas 1044 and 1046, first and second RF filters 1048 and 1050, first and second RF transceivers 1052 and 1054, a memory circuit 1056, and a base station control circuit 1058. Examples of some or all of these components are described elsewhere in this application or in U.S. Patent Application Serial No. 16 / 084,544, which is incorporated by reference in all jurisdictions in which such incorporation is permitted by reference.

[0184] The power supply 1042 can be any suitable power supply, such as a battery or a supply that receives power from a power outlet; if the power supply is of the latter type, the power supply may also include a backup battery for power outages or when the base station circuit 1040 is "unplugged".

[0185] The antenna 1044 can be any antenna suitable for the frequency band for the RF transceiver 1052 to communicate with Figure 4 the implanted circuit 1010.

[0186] Similarly, the antenna 1046 can be any antenna suitable for the frequency band for the RF transceiver 1054 to communicate with Figure 3 components of the home network 1006 (such as routers, access points, or repeaters).

[0187] Each of the filters 1048 and 1050 can be any suitable band - pass filter, such as a surface acoustic wave (SAW) filter or a bulk acoustic wave (BAW) filter.

[0188] The Rf transceiver 1052 can be a conventional transceiver configured to allow the control circuit 1058 to communicate with Figure 3 the implanted circuit 1010 when the implant circuit is disposed within an implantable prosthesis (such as Figure 4 the kinematic implantable device 1002) or otherwise associated with the implantable prosthesis. For example, the RF transceiver 1052 can be any suitable type of transceiver (such as, Bluetooth, Bluetooth Low Energy (BTLE), and ), configurable to operate according to any suitable protocol (such as, MICS, ISM, Bluetooth, Bluetooth Low Energy (BTLE), and ), and configurable to operate in a frequency band from 1 MHz to 5.4 GHz or any other suitable range.

[0189] Similarly, the RF transceiver 1054 can be any conventional transceiver configured to allow the control circuit 1058 to communicate with Figure 3 components of the home network 1006 (such as routers, access points, or repeaters), or to communicate with Figure 3 one or more of the home base station 1004, the smart device 1005, and the connected personal assistant 1000. For example, the RF transceiver 1026 can be any suitable type of transceiver (such as, Bluetooth, Bluetooth Low Energy (BTLE), and ) can be configured to operate according to any suitable protocol (e.g., MICS, ISM, Bluetooth, Bluetooth Low Energy (BTLE), and ) and can be configured to operate in a frequency band from 1 MHz to 5.4 GHz or any other suitable range.

[0190] The memory circuit 1056 can be any suitable non-volatile memory circuit, such as an EEPROM or FLASH memory, and can be configured to store data written by the control circuit 1058 and provide data in response to a read command from the control circuit. For example, the control circuit 1058 can store in the memory 1056 data packets received from Figure 5 the implantable circuit 1010 and can store data packets received from the cloud server via the RF transceiver 1054, where the data packets include, for example, Figure 4 commands, instructions, or configuration data for the implantable circuit 1010. Alternatively, the memory 1056 can include volatile memory.

[0191] The base station control circuit 1058, which can be any suitable processor such as a microcontroller or microprocessor, is configured to control the configuration and operation of the base station circuit 1040 itself and one or more other components of the base station circuit 1040. For example, the base station control circuit 1058 can be configured to receive data packets from Figure 4 the implantable circuit 1010 via the RF transceiver 1052, convert the received data packets into data packets suitable for transmission to Figure 3 the home network 1006, and transmit the converted data packets to the home network via the RF transceiver 1054. And the base station control circuit 1058 can also be configured to receive data packets from the home network 1006 via the RF transceiver 1054, convert the received data packets into data packets suitable for transmission to the implantable circuit 1010, and transmit the converted data packets to the implantable circuit via the Rf transceiver 1052.

[0192] Still referring to Figure 5 , the operation of the base station circuit 1040 is described according to one embodiment, where the implantable prosthesis with which the base station circuit communicates (not shown in Figure 5 ) is implanted in a patient (not shown in Figure 5 ).

[0193] The control circuit 1058 polls the implantable circuit 1010 (not shown in Figure 5 ) of the implantable prosthesis at a specified interval ( Figure 4), such as once a day, once every other day, once a week, or once a month. If the control circuit 1058 does not receive a response to the poll, the control circuit can poll the implantable circuit 1010 more frequently (e.g., every 5 minutes, every 30 minutes, every hour) until it receives a response or determines that the implanted prosthesis is out of range of the base station.

[0194] The implantable circuit 1010 ( Figure 4 responds to the poll by transmitting all packets of IMU samples generated by the implantable circuit since the last transmitted packet.

[0195] The antenna RF transceiver 1052 receives packets from the implantable circuit 1010 ( Figure 4 ) via the antenna 1044 and the filter 1048, and provides the received packets to the base station control circuit 1058, which decodes and decrypts the packets, parses the messages from the packets, and stores the parsed messages in the memory circuit 1056. Before storing the parsed messages, the base station control circuit 1058 can encrypt some or all of each parsed message to comply with HIPAA.

[0196] Then, the base station control circuit 1058 reformats the stored messages or generates new messages in response to the headers and payloads of the stored messages. For example, the base station control circuit 1058 can generate new messages, each of which includes the corresponding payload and header from the received messages, but each includes additional header information, such as the identifier of the base station 1004 ( Figure 4 ), the reception time of the original message from the implantable circuit 1010 ( Figure 4 ) and the generation time of the new message.

[0197] Before generating the new messages, the base station control circuit 1058 can decrypt the parsed messages stored in the memory 1056.

[0198] The base station control circuit 1058 then generates packets including the new messages, encrypts some or all of each packet, error-codes the packets, and provides the encrypted and encoded packets to the RF transceiver 1054, which transmits the encrypted and encoded packets to the home network 1006 via the filter 1050 and the antenna 1046. The base station control circuit 1058 can temporarily store the encrypted and encoded data packets in the memory 1056 (e.g., in a buffer) before providing the packets to the RF transceiver 1054.

[0199] In an alternative embodiment, the base station control circuit 1058 "passes" the packets received from the implantable circuit 1010 ( Figure 4 ) to the home network 1006 ( Figure 3) That is, the base station control circuit 1058 receives one or more data packets from the implantable circuit 1010 via the RF transceiver 1052, temporarily stores the one or more data packets in the memory 1056, and causes the RF transceiver 1054 to transmit the one or more data packets to the home network 1006.

[0200] In yet another alternative, the control circuit 1058 modifies one or more data packets received from the implantable circuit 1010 ( Figure 4 ) without first parsing the one or more data packets, or parsing some but not all of each data packet.

[0201] The home network 1006 ( Figure 3 ) may "relay" one or more data packets received from the base station 1004 to a destination, such as a server on the cloud 1008 ( Figure 3 ), or may modify the one or more data packets according to a suitable communication protocol and then send the one or more data packets to the destination.

[0202] In combination with Fig.26 The operation of the base station circuit 1040 is further described.

[0203] Still referring to Figure 5 , alternative embodiments of the base station circuit 1040 are contemplated. For example, the embodiments described in combination with Figure 3 to Figure 4 and Figure 6 to Figure 27 may be applicable to the base station circuit 1040.

[0204] Figure 6 is a perspective view of the IMU 1022 according to one embodiment, Figure 4 For example, the IMU 1022 may be a Bosch BMI 160 small low-power IMU.

[0205] As described above in combination with Figure 4 , the IMU 1022 includes three measurement axes 1060, 1062, and 1064, which are arbitrarily labeled x, y, and z for purposes of description. That is, in a Cartesian coordinate system, the labels "x", "y", and "z" may be arbitrarily applied to the axes 1060, 1062, and 1064 in any order or arrangement. The marker 1066 is a reference indicating the position and orientation of the axes 1060, 1062, and 1064 relative to the IMU 1022 package.

[0206] The IMU 1022 includes three accelerometers (not shown in Figure 6 ), each of which senses and measures the acceleration a(t) along a respective one of the axes 1060 (x), 1062 (y), and 1064 (z), where a x (t) is the acceleration along the x-axis, a y(t) is the acceleration along the y-axis and a z (t) is the acceleration along the z-axis. Each accelerometer generates a corresponding analog sense or output signal, the instantaneous magnitude of which represents the instantaneous magnitude of the sensed acceleration along the corresponding axis. For example, the magnitude of the accelerometer output signal at a given time is proportional to the magnitude of the acceleration along the accelerometer sensing axis at the same time.

[0207] IMU 1022 also includes three gyroscopes (not shown in Figure 6 ), each of which senses and measures the angular velocity Ω(t) about a corresponding one of the axes 1060 (x), 1062 (y), and 1064 (z), where Ω(t) is the angular velocity along the x-axis, Ω y (t) is the angular velocity along the y-axis, and Ω z (t) is the angular velocity along the z-axis. Each gyroscope generates a corresponding analog sense or output signal, the instantaneous magnitude of which represents the instantaneous magnitude of the sensed angular velocity about the corresponding axis. For example, the magnitude of the gyroscope output signal at a given time is proportional to the magnitude of the angular velocity about the gyroscope sensing axis at the same time.

[0208] IMU 1022 includes analog-to-digital converters (ADCs) for each of the axes 1060, 1062, and 1064 (not shown in Figure 6 ), one ADC for converting the output signal of the corresponding accelerometer into a corresponding digital acceleration signal, and another ADC for converting the output signal of the corresponding gyroscope into a corresponding digital angular velocity signal. For example, each ADC can be an 8-bit, 16-bit, or 24-bit ADC.

[0209] The circuit designer can configure each ADC (not shown in Figure 6 ) to have corresponding parameter values that are the same as or different from those of the other ADCs. Examples of such parameters with settable values include the sampling rate, the dynamic range at the ADC input node, and the output data rate (ODR). One or more of these parameters can be set to a constant value, while one or more other of these parameters can be set dynamically (e.g., during run time). For example, the corresponding sampling rate of each ADC can be set dynamically such that the sampling rate has one value during one sampling period and another value during another sampling period.

[0210] For each digital acceleration signal and each digital angular velocity signal, the IMU 1022 can be configured to provide parameter values associated with the signals. For example, the IMU 1022 can provide a sampling rate, a dynamic range, and a timestamp indicating the time of acquisition of the first sample or the last sample for each digital acceleration signal and each digital angular velocity signal. The IMU 1022 can be configured to provide these parameter values in the form of a message header (with the corresponding samples forming the message payload) or in any other suitable form.

[0211] Still referring Figure 6 , alternative embodiments of the IMU 1022 are considered. For example, the IMU 1022 can have a shape other than square or rectangular. Additionally, in combination Figures 3 to 5 and 7 to Fig. 27 The described embodiments may apply to the IMU1022.

[0212] Figure 7 is a front view of a standing male patient 1070 with a knee prosthesis 1072 implanted to replace his left knee joint and the axes 1060, 1062, and 1064 (arbitrarily labeled x, y, and z) of the IMU 1022 ( Figure 6 ).

[0213] Figure 8 is, according to one embodiment, of the patient 1070 in a supine position Figure 7 and the axes 1060, 1062, and 1064 (arbitrarily labeled x, y, and z) of the IMU 1022 ( Figure 6 ) in a side view (the knee prosthesis 1072 is shown passing through the patient's right leg).

[0214] Referring Figure 7-8 , in one embodiment, ideally one IMU axis ( Figures 7 and 8 the x-axis 1060 in Figures 7 and 8 ) is vertical when the patient 1070 is standing straight, one IMU axis ( Figures 7 and 8 the y-axis 1062 in

[0215] There are various techniques that can assist a surgeon implanting the knee prosthesis 1072 in aligning the IMU axes 1060, 1062, and 1064 with the ideal orientation axes. First, during the assembly of the tibial extension through the physical design of the components, the IMU 1022 ( Figure 6) The orientation within the tibial extension (described elsewhere in this document) is fixed within a relatively tight tolerance from extension to extension. Second, both the tibial extension portion and the tibial baseplate (described elsewhere in this document) include alignment marks that the surgeon uses during implantation of the knee prosthesis to align the tibial extension with the tibial baseplate assembly so that the extension-plate alignment is within a relatively tight tolerance from implant to implant. Third, the consistency of the tibial head from patient to patient, and how the surgeon modifies the tibial head to accommodate the tibial baseplate, fixes the orientation of the tibial baseplate assembly within a relatively tight tolerance from patient to patient.

[0216] Despite these axis alignment techniques, the IMU axes 1060, 1062, and 1064 may be misaligned relative to the ideal axis alignment described above. For example, such misalignment can have one or both of a translational component and a rotational component, although the rotational component is typically more prominent than the translational component. Further in an example, the range of rotational misalignment can be from about a fraction of a degree to about 90°.

[0217] Techniques for compensating for or correcting such axis misalignment are described elsewhere in this patent application.

[0218] Still referring to Figures 7 and 8 , alternative embodiments of the described axis orientation and axis-orientation techniques are considered. For example, for other types of implant prostheses, such as shoulder prostheses and hip prostheses, the described axis orientation can be modified. Additionally, the embodiments described in conjunction with Figures 3 to 6 and Figures 9 to 27 can be applicable to the described axis orientation and axis-orientation techniques.

[0219] Fig. 9 is, according to one embodiment, a graph 1080 of the digitized versions of the analog acceleration signals a Figures 7 and 8 ) of the accelerometer of the IMU 1022 ( Figure 4 ) in response to accelerations along the x-axis 1060, y-axis 1062, and z-axis 1064 ( Figure 6 ) as the patient 1070 ( x (t), a y (t), and a z (in m / s 2 ) walks forward at a normal gait for a period of about ten seconds. In the described example, the x, y, and z axes have the ideal alignment described in conjunction with Figures 7 and 8 , the knee prosthesis 1072 ( Figures 7 and 8 ) exhibits little or no degradation due to instability or wear, and the IMU 1022 to the analog acceleration signals a x (t), a y (t), and a zEach of those in (t) is sampled at the same sampling time, with a sampling rate of 3200 Hz and an output data rate (ODR) of 800 Hz. The ODR is the sampling rate output by the IMU 1022 and is generated by downsampling the samples acquired at 3200 Hz. That is, since 3200 Hz / 800 Hz = 4, the IMU 1022 generates an 800 Hz ODR by outputting only every fourth sample obtained at 3200 Hz.

[0220] Fig.10 According to one embodiment, while patient 1070 ( Figures 7 and 8 ) walks forward at a normal gait for a period of about ten seconds, the gyroscopes of the IMU 1022 ( Figure 4 ) generate digitized versions of the analog angular velocity signals Ω Figure 6 (t), Ω x (t), and Ω y (t) (in degrees / s) with respect to time in response to the angular velocities about the x-axis 1060, y-axis 1062, and z-axis 1064 ( z ). In the example described, the x, y, and z axes have the ideal alignment described in conjunction with Figures 7 and 8 , the knee prosthesis 1072 ( Figures 7 and 8 ) exhibits little or no degradation due to instability or wear, and the IMU 1022 samples each of the analog angular velocity signals Ω x (t), Ω y (t), and Ω z (t), as well as each of the analog acceleration signals a x (t), a y (t), and a z (t) at the same sampling time and at the same sampling rate of 3200 Hz and an ODR of 800 Hz. That is, graph 1082 is temporally aligned with Fig. 9 's graph 1080.

[0221] Fig.11 According to one embodiment, Fig. 9 the middle portion 1084 of graph 1080, which has an expanded (i.e., higher resolution) time scale and marks walking-related events. For example, the time when the heel of patient 1070 ( Figures 7 and 8 ) strikes the surface he is walking on is marked, as well as the time when the patient lifts his toes off the surface. In addition, the middle portion 1084 does not include the start portion of graph 1080, which represents the period when patient 1070 accelerates to his normal walking speed, and does not include the end portion of curve 1080, which represents the period when the patient is decelerating to a stop and thus represents the period when the patient is walking at an approximately constant speed.

[0222] Fig.12 According to one embodiment, Fig.10 the middle portion 1086 of the graph 1082, which has the same magnified (i.e., higher resolution) time scale as Fig.11 the graph 1084. For example, the number of times the heel of patient 1070 ( Figures 7 and 8 ) strikes the surface on which he is walking, the number of times the patient lifts his toes off the surface, and the peak angular velocity Ω y (t) of the knee prosthesis when bending about the y-axis (or about an axis approximately parallel to the y-axis) are marked. In addition, the middle portion 1086 does not include the start portion of the graph 1082, which represents the time period during which patient 1070 accelerates to his normal walking speed, and does not include the end portion of the curve 1082, which represents the time period during which the patient is decelerating to a stop and thus represents the time period during which the patient is walking at an approximately constant speed.

[0223] Referring to Figure 4 and Figures 9 to 12 , the implantable control circuit 1032 can be configured to determine whether patient 1070 is walking by comparing the acceleration and angular velocity signals generated by the accelerometer and gyroscope of the IMU 1020 with reference normal gait signals (such as those shown in graphs 1080, 1082, 1084, and 1086). For example, the implantable control circuit 1032 can be configured to cause the digitized acceleration signals a x (t), a y (t), and a z (t) and the digitized angular velocity signals Ω x (t), Ω y (t), and Ω z(t) is associated with the corresponding reference normal gait signal, and if the correlation yields a correlation value greater than a correlation threshold (which may have a value, for example, in the range of 0.60 - 0.95) (1.0 being the maximum value that a correlation can yield), it can be determined that patient 1070 is walking. Alternatively, to save processing power and time, the implantable control circuit 1032 can be configured to correlate regions of the acceleration and angular velocity signals generated by the accelerometer and gyroscope of the IMU 1020 with regions of the reference normal gait signal, such as the heel strike region. And determining that patient 1070 is walking is one of the one or more determinations that the implantable control circuit 1032 can be configured to make as to whether the acceleration and angular velocity signals from the IMU 1020 are eligible signals that the implantable control circuit 1032 is configured to store. The corresponding reference normal gait signal can be generated by patient 1070 himself, for example, in a doctor's office (where the doctor can control the implantable control circuit 1032 to store the reference normal gait signal in the memory circuit 1024). Or, the corresponding reference normal gait signal can be generated by simulating the normal gait of patient 1070, or in response to a statistical analysis of the normal gaits of a group of other patients having the same or similar knee prostheses. If the corresponding reference normal gait signal is generated in response to something other than the actual gait of patient 1070 himself, then during the correlation process, the implantable control circuit 1032 can amplify or scale the reference normal gait signal in time or amplitude to account for the stride of patient 1070. For example, the taller patient 1070 is, the longer his stride; conversely, the shorter patient 1070 is, the shorter his stride.

[0224] Still referring to Figures 9 to 12 , alternative embodiments that consider the described reference-signal generation techniques and signal-comparison techniques are provided. For example, in combination with Figures 3 to 8 and Figures 13 to 27 the described embodiments can be applied to the signals and techniques described in combination with Figures 9 to 12 .

[0225] Fig.13 is according to one embodiment, when patient 1070 walks forward in a normal gait, during one of the heel strikes described above in combination with Figures 9 to 12 , the accelerometer of the IMU 1022 ( Figure 4 ) generates digitalized versions of the analog acceleration signals a Figure 6 respectively in response to accelerations along the x-axis 1060, y-axis 1062, and z-axis 1064 ( x )(t), a y (t), and a z (t) plotted against time as graph 1090. In the described example, the x, y, and z axes have the ideal alignment described in combination with Figures 7 and 8 , knee prosthesis 1072 ( Figures 7 and 8 ) exhibits little or no degradation due to instability or wear, and the IMU 1022 measures the simulated acceleration signals a x (t), a y (t), and a z (t), each of which is sampled at the same number of samples, and the effective sampling rate is 800 Hz. For example, for a knee prosthesis, since the weight is transferred to the prosthetic joint during heel strike, the heel strike region can be a good region of the gait signal to analyze the instability and wear of the prosthesis.

[0226] Fig.14 is according to one embodiment, by Fig.13 the digital version of the simulated acceleration signal a x (t), a y (t), and a z (t) represents the corresponding spectral distributions X(f), Y(f), and Z(f) of the x, y, and z accelerations with respect to frequency in Plot 1092. For example, a server remote from the knee prosthesis (e.g., a cloud server) can generate the digital version of the simulated acceleration signal a x (t), a y (t), and a z (t) by employing the Discrete Fourier Transform (DFT) or the Fast Fourier Transform (FFT). Although described as having arbitrary units, the spectral distributions X(f), Y(f), and Z(f) can be mathematically processed to have any suitable units, such as energy units (joules, joules root mean square).

[0227] Fig.15 is according to one embodiment, by Fig.13 the digital version of the simulated acceleration signal a x (t), a y (t), and a z (t) represents the cumulative spectral distribution XYZ(f) (e.g., in joules root mean square, logarithmic scale, or otherwise in arbitrary units) of the x, y, and z accelerations with respect to frequency in Plot 1094. For example, a server remote from the knee prosthesis (e.g., a cloud server) can generate the cumulative spectral distribution by integrating each of X(f), Y(f), and Z(f)( Fig.14 ) over time and summing the respective integration results.

[0228] Refer to Figures 13 to 15, one can use the spectral distributions X(f), Y(f), and Z(f), as well as the cumulative spectral distribution XYZ(f), as a benchmark for determining whether the knee prosthesis 1072 exhibits instability or wear-induced degradation. For example, analysis of the cumulative spectral distribution XYZ(f) indicates that for a knee prosthesis 1072 that does not exhibit instability or degradation, approximately 90% of the RMS motion is at frequencies less than 10 Hz, and approximately 98% of the RMS motion is at frequencies less than 20 Hz. Thus, if the cumulative spectral distribution XYZ(f) is about to produce a significant RMS motion above 20 Hz, then this would be an indication that the knee prosthesis 1072 may exhibit instability or degradation.

[0229] The corresponding reference simulated acceleration signals a x (t), a y (t), and a z (t), in response to the generated reference spectral distributions (f), Y(f), and Z(f), and the reference cumulative spectral distribution XYZ(f), can be generated by the patient 1070 himself, for example, in a doctor's office (the doctor can control the implantable control circuit 1032 to store the reference normal-gait-no-instability-and-no-degradation signal in the memory circuit 1024). Alternatively, the corresponding reference normal-gait-no-instability-and-no-degradation signal can be generated by simulating the normal gait of the patient 1070 or in response to a statistical analysis of the normal gaits of a group of other patients with the same or similar knee prostheses.

[0230] Still referring to Figures 13 to 15 , alternative embodiments of the reference-signal, spectral-distribution, and cumulative-spectral-distribution generation techniques and analysis techniques are considered. For example, the sampling rate and ODR used by the IMU 1022 to generate the reference signal can be different from 3200 Hz and 800 Hz, respectively. In addition, the embodiments described in conjunction with Figures 3 to 12 and Figures 16 to 27 can be applied to the signals, spectral distributions, cumulative spectral distributions, and techniques described in conjunction with Figures 13 to 15 .

[0231] Fig.16 is according to one embodiment, when the patient 1070 ( Figures 7 and 8 ) walks forward in a normal gait, during one of the heel strikes described above in conjunction with Figures 9 to 12 , the accelerometer of the IMU 1022 ( Figure 4 ) generates digitized versions of the analog acceleration signals a Figure 6 in response to the accelerations along the x-axis 1060, y-axis 1062, and z-axis 1064 ( x (t), a y (t), and a z (t) (in m / s 2Graph 1096 of the unit (in units) versus time. In the described example, the x, y, and z axes have the ideal alignment described in conjunction with Figures 7 and 8 The knee prosthesis 1072 ( Figures 7 and 8 ) exhibits instability but shows little or no wear-induced degradation, and the IMU 1022 samples each of the simulated acceleration signals a x (t), a y (t), and a z (t) at the same number of samples, and the sampling rate (sometimes referred to as the "raw sampling rate") is 3200 Hz, and the ODR (effective sampling rate) is 800 Hz. Here, "instability" means that when the patient 1070 walks, the knee prosthesis 1072 ( Figures 7 and 8 ) bends unevenly. That is, if the femoral component of the knee prosthesis vibrates in an unexpected or otherwise undesirable manner along or around one or more of the x, y, and z axes 1060, 1062, and 1064, the knee prosthesis 1072 exhibits instability.

[0232] Fig.17 is according to one embodiment, the corresponding spectral distributions X(f), Y(f), and Z(f) (in arbitrary units such as joules, logarithmic scale) of the x, y, and z accelerations represented by the digitized version of the simulated acceleration signals a Fig.16 (t), a x (t), a y (t), and a z (t) versus frequency, graph 1098. For example, a server remote from the knee prosthesis (e.g., a cloud server) can generate the spectral distributions X(f), Y(f), and Z(f) of each of the digitized version of the simulated acceleration signals a x (t), a y (t), and a z (t) by employing a Discrete Fourier Transform (DFT) or a Fast Fourier Transform (FFT).

[0233] Fig.18 is according to one embodiment, the cumulative spectral distribution XYZ(f) (in arbitrary units such as joules root mean square, logarithmic scale) of the x, y, and z accelerations represented by the digitized version of the simulated acceleration signals a Fig.16 (t), a x (t), a y (t), and a z (t) versus frequency, graph 1100. For example, a server remote from the knee prosthesis 1072 ( Figures 7 and 8 ) (e.g., a cloud server) can by summing the distributions X(f), Y(f), and Z(f) ( Fig.14 Each of those in ) is integrated over time and the corresponding integration results are added together to generate a cumulative spectral distribution.

[0234] Reference Figures 16 to 18 , the analysis of the cumulative spectral distribution XYZ(f) shows that for the knee prosthesis 1072 that exhibits instability but no degradation, approximately 90% of the RMS motion is at frequencies less than 28 Hz (in contrast, 10 Hz for the knee prosthesis 1072 that does not exhibit instability ( Figures 13 to 15 ), and approximately 98% of the RMS motion is at frequencies less than 44 Hz (in contrast, 20 Hz for the knee prosthesis 1072 that does not exhibit instability ( Figures 13 to 15 ). The RMS motion of the knee prosthesis 1072 is significantly wider in the 90% and 98% frequency ranges than the corresponding reference frequency ranges of the RMS motion of the knee prosthesis 1072 that does not exhibit instability and does not exhibit degradation, which may indicate that the knee prosthesis 1072 exhibits at least one of instability or degradation (early experimental results tend to be Fig.18 The RMS motion frequency range generated by the spectral distribution XYZ(f) plotted in indicates knee prosthesis instability, rather than degradation).

[0235] To determine the magnitude, type, and other characteristics of the instability exhibited by the knee prosthesis 1072 ( Figures 7 and 8 ), one can analyze (e.g., automatically on a server remote from the knee prosthesis, such as a cloud server), for example, one or more of the following parameters: (1) Fig.16 The magnitude, number, and relative phase of the peaks of one or more digitized versions of the simulated acceleration signals a x (t), a y (t), and a z (t); (2) The corresponding magnitude of each of one or more spectral distributions X(f), Y(f), and Z(f) at each of one or more frequencies; and (3) The corresponding magnitude of the cumulative spectral distribution XYZ(f) at each of one or more frequencies.

[0236] As described elsewhere in this patent application, one can use one or more deterministic algorithms, or one or more machine learning algorithms (e.g., neural networks) to characterize the instability and suggest one or more procedures to repair the instability. For example, the algorithm can process one or more of the following: digitized versions of the simulated acceleration signals a x (t), a y (t), and a z (t) ( Fig.16)), the spectral distributions X(f), Y(f), and Z(f), and the cumulative spectral distribution XYZ(f) to determine the peak-to-peak amplitude of instability (e.g., less than 2 millimeters (mm) of translation or rotation, 2 - 3 millimeters (mm) of translation or rotation, and 3+ mm of translation or rotation), the possible causes of instability (e.g., too much "tilt" between the femoral component and the spacer ("ball")), and the surgeries that may correct the instability (e.g., sizing and ball replacement, sending patient 1070( Figures 7 and 8 ) for physical therapy to tighten the muscles, ligaments, and tendons associated with the knee prosthesis).

[0237] Still referring Figures 16 to 18 , consider alternative embodiments of the analysis and algorithms for detecting, quantifying, and proposing repairs for instability in knee prosthesis 1072( Figures 7 and 8 ). For example, the analysis and algorithms described may be used for implantable prostheses other than knee prostheses, or may be modified for implantable prostheses other than knee prostheses. Additionally, the embodiments described in conjunction with Figures 3 to 15 and Figures 19 to 27 may be applicable to the analysis and algorithms described in conjunction with Figures 16 to 18 .

[0238] Fig.19 is according to one embodiment, when patient 1070( Figures 7 and 8 ) walks forward in a normal gait, during one of the heel strikes described above in conjunction with Figures 9 to 12 , the accelerometer of IMU 1022( Figure 4 ) generates digitized versions of the analog acceleration signals a Figure 6 respectively in response to accelerations along the x-axis 1060, y-axis 1062, and z-axis 1064( x (t), a y (t), and a z (t) (in m / s 2 ) with respect to time, graph 1102. In the example described, the x, y, and z axes have the ideal alignment described in conjunction with Figures 7 and 8 , knee prosthesis 1072( Figures 7 and 8 ) exhibits instability or degradation due to premature wear, and IMU 1022 samples each of the analog acceleration signals a x (t), a y (t), and a z (t) with the same number of samples, a sampling rate of 3200 Hz and an ODR of 800 Hz. Here, "premature degradation" means that knee prosthesis 1072( Figures 7 and 8 ) has just started to exhibit symptoms of wear caused by repeated flexion of the knee prosthesis (e.g., rough engagement (grinding) of the femoral component with the plastic spacer of the knee prosthesis). That is, if the femoral component in patient 1070( Figures 7 and 8 )When the knee prosthesis is bent (e.g., during walking) and the plastic spacer is roughly engaged (e.g., ground against the plastic spacer), the knee prosthesis 1072 exhibits wear.

[0239] Fig. 20 According to one embodiment, the Fig.19 digitized version of the analog acceleration signals a x (t), a y (t) and a z (t) representing the corresponding spectral distributions X(f), Y(f) and Z(f) of the x, y and z accelerations (in arbitrary units such as joules, logarithmic scale) with respect to frequency, graph 1104. For example, a server remote from the knee prosthesis (e.g., a cloud server) can generate the digitized version of the analog acceleration signals a x (t), a y (t) and a z (t) by employing a Discrete Fourier Transform (DFT) or a Fast Fourier Transform (FFT).

[0240] Fig.21 is according to one embodiment, the Figure 19 digitized version of the analog acceleration signals a x (t), a y (t) and a z (t) representing the cumulative spectral density XYZ(f) of the x, y and z accelerations (in arbitrary units such as root mean square joules, logarithmic scale) with respect to frequency, graph 1106. For example, a server (e.g., a cloud server) remote from the knee prosthesis 1072( Figures 7 to 8 ) can generate the cumulative spectral density by integrating each of the spectral distributions X(f), Y(f) and Z(f)( Figure 14 ) over time and adding the corresponding integration results.

[0241] Referring to Figures 19 to 21 , the analysis of the cumulative spectral distribution XYZ(f) shows that for the knee prosthesis 1072 exhibiting instability and premature degradation, approximately 90% of the RMS motion is at frequencies less than 34 Hz (compared to 10 Hz for knee prostheses that do not exhibit instability and do not exhibit degradation( Figures 13 to 15 ), and 28 Hz for knee prostheses that exhibit instability but do not exhibit degradation( Figures 16 to 18), and approximately 98% of the RMS motion is at frequencies less than 175 Hz (in contrast, for knee prosthesis 1072 that does not exhibit instability and does not exhibit degradation, it is 20 Hz( Figures 13 to 15 ), and for knee prostheses that exhibit instability but do not exhibit degradation, it is 44 Hz( Figures 16 to 18 ). The RMS motion of knee prosthesis 1072 is significantly wider in the 90% and 98% frequency ranges than the corresponding reference frequency ranges of the RMS motion of knee prostheses that do not exhibit instability and do not exhibit degradation, and the corresponding frequency ranges of the RMS motion of knee prostheses that exhibit instability but do not exhibit degradation, which may indicate that knee prosthesis 1072 exhibits both instability and early degradation.

[0242] To determine the magnitude, type, and other characteristics of the instability and degradation exhibited by knee prosthesis 1072( Figures 7 to 8 ), one can analyze (e.g., automatically on a server remote from the knee prosthesis, such as a cloud server), for example, one or more of the following parameters: (1) Figure 19 the magnitude, number, and relative phase of the peaks of one or more digitized versions of the analog acceleration signals a x (t), a y (t), and a z (t); (2) Figure 20 the corresponding magnitude of each of one or more of the spectral distributions X(f), Y(f), and Z(f) of at each of one or more frequencies; and Figure 21 (3)

[0243] the corresponding magnitude of the cumulative spectral distribution XYZ(f) of x at each of one or more frequencies. Figure 16 As described elsewhere in this patent application, one can use one or more deterministic algorithms, or one or more machine learning algorithms (e.g., neural networks) to characterize one or both of the instability and degradation, and recommend one or more procedures for fixing one or both of the instability and degradation. For example, the algorithm can process one or more of the following: digitized versions of the analog acceleration signals a y (t), a z (t)( Figure 16) The spectral distributions X(f), Y(f), and Z(f), and the cumulative spectral distribution XYZ(f) are used to determine the peak-to-peak amplitudes (e.g., less than 2 millimeters (mm) of translation or rotation, 2 - 3 millimeters (mm) of translation or rotation, and 3+ mm of translation or rotation) of one or both of instability and degradation, the possible causes of one or both of instability and degradation (e.g., too much "tilt" between the femoral component and the spacer ("ball")), and the surgeries that may repair one or both of instability and degradation (e.g., sizing and ball replacement, sending patient 1070( Figures 7 to 8 ) to receive physical therapy to tighten the muscles, ligaments, and tendons associated with the knee prosthesis).

[0244] Still referring Figures 19 to 21 , consider alternative embodiments of the described analysis and algorithms for detecting, quantifying, and proposing repairs for instability in knee prosthesis 1072( Figures 7 to 8 ). For example, the described analysis and algorithms can be used for implantable prostheses other than knee prostheses, or can be modified for implantable prostheses other than knee prostheses. Additionally, the embodiments described in conjunction with Figures 3 to 18 and Figures 22 to 27 can be applicable to the analysis and algorithms described in conjunction with Figures 19 to 21 .

[0245] Figure 22 is according to one embodiment, when patient 1070( Figures 7 to 8 ) walks forward in a normal gait, during one of the heel strikes described above in conjunction with Figures 9 to 12 , the accelerometer of IMU 1022( Figure 4 ) generates digitized versions of the analog acceleration signals a Figure 6 respectively in response to accelerations along the x-axis 1060, y-axis 1062, and z-axis 1064( x (t), a y (t), and a z (t) (in m / s 2 ) with respect to time, which is the graph 1108. In the described example, the x, y, and z axes have the ideal alignment described in conjunction with Figures 7 to 8 , knee prosthesis 1072( Figures 7 to 8 ) exhibits instability or degradation caused by late wear, and IMU 1022 samples each of the analog acceleration signals a x (t), a y (t), and a z (t) with the same number of samples, at a sampling rate of 3200 Hz and an ODR of 800 Hz. Here, "late degradation" means knee prosthesis 1072( Figures 7 to 8) has clearly shown wear symptoms caused by repeated bending of the knee prosthesis (e.g., rough engagement (grinding) of the femoral component with the plastic spacer of the knee prosthesis). That is, if the femoral component engages the plastic spacer roughly (e.g., grinds against the plastic spacer) when the knee prosthesis is bent in patient 1070 ( Figures 7 to 8 ) (e.g., during walking), the knee prosthesis 1072 shows wear.

[0246] Figure 23 is according to one embodiment, the analog acceleration signal a Figure 22 of the digital version x (t), a y (t) and a z (t) showing the corresponding spectral distributions X(f), Y(f) and Z(f) of the x, y and z accelerations (in arbitrary units such as joules, logarithmic scale) relative to frequency, graph 1110. For example, a server far from the knee prosthesis (e.g., a cloud server) can generate the digital version of the analog acceleration signal a x (t), a y (t) and a z (t) by using the Discrete Fourier Transform (DFT) or the Fast Fourier Transform (FFT).

[0247] Figure 24 is according to one embodiment, the analog acceleration signal a Figure 22 of the digital version x (t), a y (t) and a z (t) showing the cumulative spectral distribution XYZ(f) of the x, y and z accelerations (in arbitrary units such as root mean square joules, logarithmic scale) relative to frequency, graph 1112. For example, a server (e.g., a cloud server) far from the knee prosthesis 1072 ( Figures 7 to 8 ) can generate the cumulative spectral density by integrating each of the contents X(f), Y(f) and Z(f) ( Figure 14 ) over time and adding the corresponding integration results.

[0248] Referring to Figures 22 to 24 , the analysis of the cumulative spectral distribution XYZ(f) shows that for the knee prosthesis 1072 showing instability and premature degradation, approximately 90% of the RMS motion is at frequencies less than 306 Hz (in contrast, for knee prostheses without instability and without degradation it is 10 Hz ( Figures 13 to 15 ), and for knee prostheses showing instability but not degradation it is 28 Hz (Figures 16 to 18 ), and for knee prostheses exhibiting instability and premature deterioration, it is 34 Hz), and approximately 98% of the RMS motion is at frequencies less than 394 Hz (in contrast, for knee prosthesis 1072 that does not exhibit instability and does not exhibit deterioration, it is 20 Hz( Figures 13 to 15 ), for knee prostheses exhibiting instability but not deterioration, it is 44 Hz( Figures 16 to 18 ), and for knee prostheses exhibiting instability and premature deterioration, it is 175 Hz). The RMS motion of knee prosthesis 1072 is significantly wider in the 90% and 98% frequency ranges than the corresponding reference frequency ranges of the RMS motion of knee prostheses that do not exhibit instability and do not exhibit deterioration, as well as the corresponding frequency ranges of the RMS motion of knee prostheses exhibiting instability but not deterioration and instability and premature deterioration, which can indicate that knee prosthesis 1072 exhibits both instability and late deterioration.

[0249] To determine the magnitude, type, and other characteristics of the instability and deterioration exhibited by knee prosthesis 1072( Figures 7 to 8 ), one can analyze (e.g., automatically on a server remote from the knee prosthesis, such as a cloud server), for example, one or more of the following parameters: (1) Figure 22 the magnitude, number, and relative phase of the peaks of one or more digitized versions of the analog acceleration signals a x (t), a y (t), and a z (t); (2) Figure 23 the corresponding magnitudes of each of one or more of the spectral distributions X(f), Y(f), and Z(f) of (3) Figure 24 at each of one or more frequencies; and

[0250] As described elsewhere in this patent application, one can use one or more deterministic algorithms, or one or more machine learning algorithms (e.g., neural networks) to characterize one or both of the instability and deterioration and to suggest one or more procedures for remedying one or both of the instability and deterioration. For example, the algorithm can process one or more of the following: digitized versions of the analog acceleration signals a x (t), a y (t), and a z (t)( Figure 22) The spectral distributions X(f), Y(f), and Z(f), and the cumulative spectral distribution XYZ(f) are used to determine the peak-to-peak amplitudes (e.g., translations or rotations less than 2 millimeters (mm), 2 - 3 millimeters (mm), and 3+ mm translations or rotations) of one or both of instability and degradation, the possible causes of one or both of instability and degradation (e.g., for instability, too much "tilt" between the femoral component and the spacer ("ball"); for degradation, wear of the ball or femoral component), and one or more surgeries that may repair one or both of instability and degradation (e.g., sizing and ball replacement, sending patient 1070( Figures 7 to 8 ) to receive physical therapy to tighten the muscles, ligaments, and tendons associated with the knee prosthesis).

[0251] Still referring Figures 22 to 24 , consider alternative embodiments of the analysis and algorithm for detecting, quantifying, and proposing repairs for instability in knee prosthesis 1072( Figures 7 to 8 ). For example, the analysis and algorithm described can be used for implantable prostheses other than knee prostheses, or can be modified for implantable prostheses other than knee prostheses. Additionally, the algorithm can generate results in response to a digitized version of one or more of the angular velocities Ω x (t), Ω y (t), and Ω z (t) (in degrees / s), where the digitized versions of the angular velocities Ω x (t), Ω y (t), and Ω z (t) (in degrees / s) are generated by the gyroscopes of IMU 1022( Figure 4 ) in response to angular velocities about the x-axis 1060, y-axis 1062, and z-axis 1064( Figure 6 ), respectively. Additionally, the algorithm can generate results in response to one or more parts of the gait of patient 1070( Figures 7 to 8 ) (e.g., toe-off) rather than heel strike or in addition to heel strike. Additionally, the embodiments described in conjunction with Figures 3 to 21 and Figures 25 to 27 can be applied to the analysis and algorithm described in conjunction with Figures 22 to 24 .

[0252] Figure 25 is a flowchart 1120 of the operation of implantable circuit 1010 according to one embodiment, Figure 4 .

[0253] Referring Figure 4 and Figure 25 , at step 1122, implantable circuit 1010 detects movement of the implantable prosthesis, such as Figures 7 to 8knee prosthesis 1072. For example, the control circuit 1032 monitors the respective digitized output signals from each of one or more accelerometers and gyroscopes of the IMU 1022, and detects movement of the implanted prosthesis in response to the respective magnitudes of each of one or more digitized signals exceeding a movement-detection threshold.

[0254] Next, at step 1124, in response to detecting movement of the implanted prosthesis at step 1122, the control circuit 1032 causes the IMU 1022 to sample the analog signals output from one or both of the IMU accelerometer and gyroscope (hereinafter it is assumed that the IMU 1022 samples the analog signals output from both the IMU accelerometer and gyroscope). The IMU 1022 samples the analog signals at the same sampling rate or at respective sampling rates. For example, the IMU 1022 samples the analog signals output from all x, y, and z accelerometers and gyroscopes at 1600 Hz (original sampling rate), and scales down the original sampling rate to achieve an effective sampling rate (also referred to as the output data rate (ODR)) of 800 Hz for each accelerometer and gyroscope signal. In addition, the control circuit 1032 causes the IMU 1022 to sample the analog signals output from the accelerometer and gyroscope within a limited time period (such as, for example, during a ten-second time window).

[0255] Then, at step 1126, the control circuit 1032 determines whether the samples acquired by the IMU 1022 at step 1124 are samples of a qualified event, such as a patient 1070 Figures 7 to 8 () walking with the implanted knee prosthesis 1072 Figures 7 to 8 . For example, the control circuit 1032 causes the respective samples from each of one or more accelerometers and gyroscopes to be compared with corresponding reference samples of a qualified event (e.g., stored in Figure 4In the memory circuit 1024), the correlation result is compared with a threshold, and if the correlation result is equal to or exceeds the threshold, it is determined that the sample belongs to a qualified event, or if the correlation result is less than the threshold, it is determined that the sample does not belong to a qualified event. Alternatively, the control circuit 1032 can perform a less complex and less energy-consuming determination by determining that the sample belongs to a qualified event, for example, if the sample has a peak-to-peak amplitude and duration that can indicate a patient walking for a threshold time length. A remote destination (such as a cloud server) can determine whether the sample was actually collected while the patient was walking. For example, if the control circuit 1032 is configured to cause the IMU 1022 to sample the analog signals output by one or both of the accelerometer and gyroscope at a relatively high sampling rate (such as 3200 Hz) and ODR (such as 800 Hz) in response to detecting three patient movements per day, and statistically, at least one of the detected movements is a patient walking for at least a threshold time length, then this technique can provide suitable prosthesis information while consuming less battery 1012 energy than the IMU would consume, sampling fewer events, but determining that the movement corresponding to the sampled event is a patient walking.

[0256] If the control circuit 1032 determines that the sample obtained by the IMU 1022 in step 1124 does not belong to a qualified event, the control circuit returns to step 1122.

[0257] However, if the control circuit 1032 determines that the sample obtained by the IMU 1022 in step 1124 belongs to a qualified event, the control circuit proceeds to step 1128, during which the control circuit stores the sample itself and the corresponding sample information of each group of samples in the memory circuit 1024. A group of samples includes samples from a respective one of the accelerometer and gyroscope, and the sample information includes, for example, the identity of the accelerometer or gyroscope that generated the analog signal (from which the group of samples was collected), the original sampling rate and ODR, the start time of the sample group (the time when the first sample of the group was collected), the end time of the sample group (the time when the last sample of the group was collected), the length of the sampling window, and the dynamic amplitude input range and amplitude output range of the ADC being sampled. The dynamic amplitude input range is the maximum peak or peak-to-peak signal amplitude that the ADC can accept without "clipping" the input signal. And the amplitude output range is the maximum peak or peak-to-peak range covered by the samples and is an indication of the analog amplitude represented by each digital sample. If the sample information (such as the original sampling rate, ODR, sample window) of the respective samples from each accelerometer and gyroscope is the same, the control circuit 1032 can group all the accelerometer and gyroscope samples obtained during the same time window into a single group of samples with common sample information.

[0258] Next, at step 1130, control circuit 1032 generates a corresponding message including a header and a payload for each stored sample group and corresponding sample information. The header includes the sample information, and the payload includes the samples forming the group. The header may also include additional information such as a unique identifier of the implantable prosthesis (e.g., serial number), a unique identifier of patient 1070( Figures 7 to 8 ), and the length of the payload.

[0259] Then, at optional step 1132, control circuit 1032 encrypts some or all of each message, e.g., as may be specified by HIPAA. As part of this step or step 1130, control circuit 1032 may include a public encryption key in the message header, which allows an authorized recipient of the message to decrypt the encrypted portion of the message. Alternatively, control circuit 1032 may not encrypt the sample messages or may not perform any encryption until the sample messages are transmitted to a remote destination.

[0260] Next, at step 1134, control circuit 1032 stores each message, encrypted or not, in memory 1024.

[0261] Then, at step 1136, control circuit 1032 determines whether base station 1004( Figure 3 ) has polled implantable circuit 1010 for all messages generated since the implantable circuit last sent a message to the base station.

[0262] If control circuit 1032 determines that base station 1004( Figure 3 ) has not polled implantable circuit 1010, the control circuit takes no further action on the message and effectively waits for the base station to poll the implantable circuit.

[0263] However, if control circuit 1032 determines that base station 1004( Figure 3 ) has polled implantable circuit 1010, the control circuit proceeds to step 1138.

[0264] At step 1138, control circuit 1032 generates one or more data packets that together include the messages stored in memory 1024, as described above in connection with step 1134. Control circuit 1032 generates one or more data packets according to any suitable communication protocol, and each data packet includes a header and a payload. The header includes information such as a unique identifier unique to the implantable prosthesis (e.g., serial number), a unique identifier unique to patient 1070( Figures 7 to 8 ), and an indication that control circuit 1032 will send to base station 1004( Figure 4The sequence number of the relative position within the data packet sequence (the information in the data packet header may be redundant relative to some or all of the information in the message header). And the payload includes one or more messages (in whole or in part) stored in the memory circuit 1024. For example, if the stored message is too long for a single data packet, the control circuit 1032 may divide the message into two or more data packets (so the sequence number allows the destination of the message to reconstruct the message). Conversely, if the stored message is not long enough to fill the payload of the data packet, the data packet may include the message plus one or more other messages in whole or in part. Additionally, instead of including a message header, the data packet payload may only include the message payload (sample), and the content of the message header may be merged with or otherwise included in the data packet header.

[0265] Next, in optional step 1140, the control circuit 1032 encrypts part or all of each data packet, for example, at least the prosthesis and patient identifiers as may be specified by HIPAA. As part of this step or step 1140, the control circuit 1032 may include a public encryption key in the data packet header, which allows an authorized recipient of the message to decrypt the encrypted part of the data packet header. If some or all of the message is encrypted in accordance with step 1132, the control circuit 1032 may decrypt the message before forming the data packet. Alternatively, the control circuit 1032 may maintain the message in encrypted form such that at least a portion of the encrypted part of the message is double encrypted (message-level encryption and data packet-level encryption). In another alternative, the control circuit 1032 may not encrypt the prosthesis identifier so that the base station 1004 or the smart device 1005 ( Figure 3 ) can use the prosthesis identifier to determine whether the base station or the smart device should ignore the data packet or receive and process the data packet.

[0266] Then, in step 1142, the control circuit 1032 error-codes one or more data packets (whether encrypted or unencrypted) according to any suitable error-coding technique (the communication protocol compatible with one or more data packets may specify encryption or not). Error-coding the one or more data packets allows the destination to recover the data packets with errors acquired during the propagation of the data packets from the control circuit 1032 to the destination.

[0267] Next, in step 1144, the control circuit 1032 transmits the error-coded one or more data packets grouped via the RF transceiver 1025, the filter 1028, and the antenna 1030 to the base station 1004 ( Figure 3) Alternatively, the control circuit 1032 transmits one or more error - encoded data packets to the base station 1004 via the smart device 1005, or directly to the smart device 1005, or to the smart device via the base station.

[0268] Then, at step 1146, the control circuit 1032 determines whether it is time to acquire a sample of another qualifying event.

[0269] If the control circuit 1032 determines that it is not yet time to acquire a sample of another qualifying event, the control circuit places the implantable circuit 1010 in a sleep or other low - power mode at step 1148 to conserve energy and extend the life of the battery 1012. For example, the control circuit 1032 can turn on switches 1016 and 1018 to cut power to the IMU 2022 and the memory circuit 1024, respectively. Additionally, the clock - and - power - management circuit 1020 includes a timer that notifies the control circuit 1032 to "wake up" the implantable circuit 1010 at a programmed absolute time or after a programmed amount of time (e.g., one day, two days, one week, one month) has passed. Additionally, the time between qualifying events can be related to the duration since the prosthesis was implanted in the patient 1070 ( Figures 7 to 8 ) or to health insurance billing codes (such as telemedicine codes or CPT codes). For the former, for example, in the first three months after implantation (0 to 3 months), the control circuit 1032 is configured to measure at least one qualifying event (e.g., walk at least ten steps) per day so that the patient's doctor can monitor the function of the implant. Then, between 3 and 6 months after implantation, the control circuit 1032 can be configured to measure at least one qualifying event every other day, e.g., after a waiting period of at least 24 hours, or every three days, e.g., after a waiting period of at least 48 hours. From 6 to 12 months, the control circuit 1032 can be configured to measure at least one qualifying event per week and then one or two qualifying events per month. For the latter (health insurance billing codes), telemedicine codes or CPT codes are insurance codes under which a doctor can bill an insurance company, such as via the Internet, via email, or via phone, to remotely view patient information. Examples of such information are data collected by the IMU 1022 ( Figure 4)Results of an analysis performed on a sample of one or more qualifying events that are detected and sampled. Insurance plans typically specify a maximum payment (e.g., $3000 / year) for a medical problem (such as a knee prosthesis) that a doctor can receive based on telemedicine codes or CPT codes, and how often a doctor must view patient information to be eligible for the maximum payment. Thus, the control circuit 1032 or other parts of the implantable circuit 1010 (such as the clock and power management circuit 1020) can be configured to detect and measure qualifying prosthesis events at a frequency that allows the patient's doctor to be eligible for the payment that he / she can receive from the insurance company based on one or more telemedicine, CPT, or other reimbursement codes. For example, if the insurance plan requires the doctor to view the results generated by analyzing samples produced by the IMU 1022 daily within 0 to 6 months after implantation, weekly within 6 to 12 months after implantation, and monthly thereafter, one can configure the control circuit 1032 or other parts of the implantable circuit 1010 to detect, sample, and store samples of at least one qualifying event daily within the 0 to 6 months, samples of at least one qualifying event weekly within the 6 to 12 months, and samples of at least one qualifying event monthly thereafter. Alternatively, one can configure the control circuit 1032 or other parts of the implantable circuit 1010 to detect, sample, and store samples of at least one qualifying event daily for at least sixteen days per month.

[0270] However, if the control circuit 1032 determines in step 1146 that it is time to acquire a sample of another qualifying event, the control circuit returns to step 1122.

[0271] Still referring Figure 25 , an alternative embodiment of the operation of the implantable circuit 1010 is considered. For example, one or more steps of the flowchart 1120 can be omitted, and one or more additional steps can be added. Additionally, the embodiments described in conjunction with Figures 3 to 24 and Figures 26 to 27 can be applied to the operation of the implantable circuit 1010.

[0272] Figure 26 is a flowchart 1160 of the operation of the base station circuit 1040 according to one embodiment, Figure 5 and.

[0273] Referring Figure 5 and Figure 26 , in step 1162, the base station circuit 1040 polls the implantable circuit 1010 ( Figure 4 ) to obtain a data packet including a kinematic-motion message (if any), the kinematic-motion message being generated by the implantable circuit since the last time the implantable circuit sent a data packet to the base station 1004 ( Figure 3 ).

[0274] Next, at step 1164, the base station circuit 1040 determines whether it has received a valid response to the poll from the implantable circuit 1010 (e.g., Figures 7 to 8 ) of an implantable prosthesis (such as the knee prosthesis 1072 Figure 4 ). For example, the base station circuit 1040 determines whether it has received a valid response from the implantable prosthesis by comparing the implant identifier in the response with the version of the implant identifier stored in the memory circuit 1056 of the base station to determine whether the implant is registered to the base station 1004. If the implant identifier in the response is encrypted, the base station circuit 1040 decrypts the response before determining whether the implant identifier is validly registered to the base station 1004.

[0275] If the base station circuit 1040 determines that it has not received a valid poll response from the implantable circuit 1010 ( Figure 4 ), then at step 1166, the control circuit 1058 determines whether the number of unsuccessful poll attempts during the current poll period exceeds a first threshold, threshold_1.

[0276] If at step 1166, the control circuit 1058 determines that the number of unsuccessful poll attempts does not exceed threshold_1, the control circuit returns to step 1162 and polls the implantable circuit 1010 of the implantable prosthesis again; before re-polling the implantable circuit, the control circuit may wait for a programmed delay time. For example, the value of threshold_1 may be in the approximate range of 1 - 100.

[0277] However, if at step 1166, the control circuit 1058 determines that the number of unsuccessful poll attempts exceeds threshold_1, the control circuit proceeds to step 1168.

[0278] At step 1168, the control circuit 1058 transmits an error message to a destination (such as the cloud or other server) via the RF transceiver 1054, filter 1050, and antenna 1046, where the error message indicates that the implantable prosthesis is not responding to the base station poll. As described elsewhere in this application, the destination may take appropriate actions, such as notifying the patient 1070 ( Figures 7 to 8 ) via email or text to check that the base station 1004 ( Figure 3 ) has been "powered on" and is properly linked to the patient's home network 1006 ( Figure 3 ).

[0279] Referring again to step 1164, if the control circuit 1058 determines that it has received a valid response to its poll of the implantable circuit 1010 of Figure 4 , the control circuit proceeds to step 1170.

[0280] At step 1170, the control circuit 1058 receives a data packet via the antenna 1044, the filter 1048, and the RF transceiver 1052 from the implantable circuit 1010 of the implantable prosthesis ( Figure 4 ), where the data packet includes samples and related information collected by the IMU 1022 ( Figure 4 ). The control circuit 1058 also decodes and decrypts (if necessary) the data packet and parses the IMU samples and related information (e.g., unique prosthesis identifier, unique patient identifier).

[0281] At step 1172, the control circuit 1058 determines whether the patient and prosthesis identifiers and the data parsed from the received data packet according to step 1170 are correct (if the control circuit 1058 has already determined that the prosthesis identifier is correct according to step 1164, the control circuit may no longer determine whether the prosthesis identifier is correct). For example, the control circuit 1058 uses a suitable error decoding algorithm (such as cyclic redundancy check (CRC), Reed - Solomon) corresponding to the error - coding algorithm used by the control circuit 1032 ( Figure 4 ) to error - decode the data packet and determines whether the data packet includes an irrecoverable error in response to the decoding result. And if the control circuit 1058 determines that the data packet does not include an irrecoverable error, the control circuit 1058 compares the received patient and prosthesis identifiers with the corresponding identifiers stored in the memory circuit 1056 or downloaded from a remote location. If the control circuit 1058 determines that the data packet includes an irrecoverable error or at least one of the received patient and prosthesis identifiers is incorrect, the control circuit proceeds to step 1174; otherwise, the control circuit 1058 acknowledges (e.g., according to a suitable handshake protocol) the receipt of the valid data packet to the implant circuit 1010 and proceeds to step 1176.

[0282] At step 1174, the base station control circuit 1058 determines whether the number of error data packets (e.g., data packets with irrecoverable errors or incorrect patient identifiers or incorrect prosthesis identifiers) it has received during the current polling cycle exceeds a second threshold, Threshold_2. If the control circuit 1058 determines that the number of error data packets received during the current polling cycle does not exceed Threshold_2, the control circuit returns to step 1162 and repolls the implant circuit 1010 of the prosthesis ( Figure 4 ) to resend the data packet that the control circuit 1058 determined to be in error after receiving it at the base station 1004 ( Figure 3 ). However, if the control circuit 1058 determines that the number of error data packets received during the current polling cycle does exceed Threshold_2, the control circuit proceeds to step 1168 and sends the error message as described above.

[0283] If at step 1172, the base station control circuit 1058 determines that the patient and prosthesis identifiers are correct, then at step 1176, the control circuit generates base station data packets, the packets including parsed messages from the implantable circuit 1010 ( Figure 4 ) of the implanted prosthesis and conforming to any suitable communication protocol. That is, the control circuit 1058 effectively repackages the messages into one or more base station data packets. The respective headers of each base station data packet can include the message header and some or all of the information in the prosthesis data packet received from the prosthesis, plus additional information such as base-station-data-packet-routing information, such as Internet or other, the packet source (e.g., the home network 1006 ( Figure 3 )) and the packet destination (e.g., the cloud server) address. And the respective payloads of each packet include accelerometer or gyroscope samples acquired by the IMU 1022 ( Figure 4 ). Further, if a message is too long for a single base station data packet, then the control circuit 1058 can split the message into two or more packets (so the sequence numbers allow the destination to reconstruct the message). Conversely, if a message is not long enough to fill the payload of a packet, the packet can include that message plus all or part of one or more other messages. Further, instead of including a message header, the packet payload can include only the message payload (samples), and the content of the message header can be combined with the packet header or otherwise included in the packet header.

[0284] Then, at step 1178, the base station control circuit 1058 encrypts part or all of each base station data packet, e.g., as specified by HIPAA and one or both of the communication protocols via which the control circuit transmits the base station data packets. As part of this step or step 1176, the control circuit 1058 can include a public encryption key in the packet header, which allows an authorized recipient of the packet to decrypt the encrypted portion of the packet. If some or all of the message or prosthesis data packet is encrypted, the control circuit 1058 can decrypt the message before forming the base station data packets. Alternatively, the control circuit 1058 can maintain the message and prosthesis data packet in encrypted form such that at least a portion of the encrypted portion of the base station data packet is double or triple encrypted (two or more of message-level encryption, prosthesis-data-packet-level encryption, and base-station-data-packet-level encryption).

[0285] Then, at step 1180, the control circuit 1032 error-codes one or more of the encrypted base station data packets according to any suitable error-coding technique (the communication protocol(s) with which the one or more base station data packets are compatible can specify the error-coding technique). Error-coding the one or more base station data packets allows the destination to recover packets having errors acquired during propagation of the packets from the base station control circuit 1058 ( Figure 5 ) to the destination.

[0286] Next, at step 1182, the base station control circuit 1058 transmits one or more error-encoded base station data packets to a destination (e.g., a cloud server) via the RF transceiver 1054, the filter 1050, the antenna 1046, the home network 1006( Figure 3 ), and the Internet or other communication network.

[0287] Then, the base station control circuit 1058 returns to step 1162, waits for a programmed time (e.g., one day, between two and six days, one week, one month), and then polls the implantable prosthesis again after the programmed time has elapsed.

[0288] Still referring to Figure 26 , alternative embodiments of the operation of the base station circuit 1040 are considered. For example, the smart device 1005( Figure 3 ) may operate in a manner similar to that described above in connection with the flowchart 1160. Additionally, the smart base station circuit 1040 may perform one or more steps in the flowchart 1160, and the smart device 1005 may perform one or more of the remaining steps in the flowchart 1160. Additionally, as described above, the base station circuit 1040 may communicate with the implantable circuit 1010( Figure 4 ) via the smart device 1005, or the smart device may communicate with the implantable circuit via the base station circuit. Additionally, one or more steps of the flowchart 1160 may be omitted, and one or more additional steps may be added. Additionally, the embodiments described in connection with Figures 3 to 25 and Figure 27 may be applicable to the operation of the base station circuit 1040.

[0289] Figure 27 is a flowchart 1190 of the operation of the fuse 1014 and the control circuit 1032 according to one embodiment, Figure 4 .

[0290] Referring to Figure 4 and Figure 27 , at step 1192, the fuse 1014 is electrically closed and the control circuit 1032 determines whether the current passing through the fuse from the battery 1012 exceeds a first overcurrent threshold. For example, the control circuit 1032 or another part of the implantable circuit 1010 makes this determination by comparing the current passing through the fuse 1014 with a reference representing the overcurrent threshold.

[0291] If the control circuit 1032 determines that the current passing through the fuse 1014 exceeds the overcurrent threshold, the control circuit proceeds to step 1194; otherwise, the control circuit proceeds to step 1196.

[0292] In step 1194, the control circuit 1032 electrically opens the fuse 1014, increments a count value, and implements a delay before determining whether to re-close the fuse. To have the ability to open and re-close the fuse 1014, the connection between the battery 1012 and the control circuit 1032 bypasses the fuse such that opening the fuse does not cut the power to the control circuit, or the control circuit is already or coupled to another power source (e.g., a battery) that powers the control circuit even when the fuse 1014 is open.

[0293] In step 1196, the control circuit 1032 determines whether the current through the fuse 1014 from the battery 1012 exceeds a second overcurrent threshold for a first threshold time length, where the second overcurrent threshold is less than the first overcurrent threshold. For example, the control circuit 1032 or another part of the implantable circuit 1010 makes this determination by comparing the current through the fuse 1014 with a reference representing the second overcurrent threshold and by determining the length of time the current is greater than the second overcurrent threshold.

[0294] If the control circuit 1032 determines that the current through the fuse 1014 exceeds the second overcurrent threshold for the first threshold time length, the control circuit proceeds to step 1194 and at least temporarily opens the fuse, as described above; otherwise, the control circuit proceeds to step 1198.

[0295] In step 1198, the fuse 1014 and the control circuit 1032 determine whether the voltage across the closed fuse exceeds a first overvoltage threshold. For example, the control circuit 1032 or another part of the implantable circuit 1010 makes this determination by comparing the voltage across the fuse 1014 with a reference representing the overvoltage threshold.

[0296] If the control circuit 1032 determines that the voltage across the fuse 1014 exceeds the overvoltage threshold, the control circuit proceeds to step 1194; otherwise, the control circuit proceeds to step 1200.

[0297] In step 1194, as described above, the control circuit 1032 at least temporarily electrically opens the fuse 1014.

[0298] In step 1200, the control circuit 1032 determines whether the voltage across the fuse 1014 exceeds a second overvoltage threshold for a second threshold time length, where the second overvoltage threshold is less than the first overvoltage threshold. For example, the control circuit 1032 or another part of the implantable circuit 1010 makes this determination by comparing the voltage across the fuse 1014 with a reference representing the second overvoltage threshold and by determining the length of time the voltage is greater than the second overvoltage threshold.

[0299] If the control circuit 1032 determines that the voltage across the fuse 1014 exceeds the second overvoltage threshold for a second threshold time length, the control circuit proceeds to step 1194 and at least temporarily opens the fuse, as described above; otherwise, the control circuit proceeds to step 1202.

[0300] At step 1202, the control circuit 1032 determines whether the temperature of the closed fuse 1014 (or the temperature of another part of the prosthesis) exceeds a first overtemperature threshold. For example, the control circuit 1032 or another part of the implantable circuit 1010 makes this determination by comparing the temperature to a reference representing the overtemperature threshold.

[0301] If the control circuit 1032 determines that the temperature exceeds the overtemperature threshold, the control circuit proceeds to step 119; otherwise, the control circuit proceeds to step 1204.

[0302] At step 1194, as described above, the control circuit 1032 electrically opens the fuse 1014 at least temporarily.

[0303] At step 1204, the control circuit 1032 determines whether the temperature of the fuse 1014 (or the temperature of another part of the prosthesis) exceeds a second overtemperature threshold for a third threshold time length, where the second overtemperature threshold is less than the first overtemperature threshold. For example, the control circuit 1032 or another part of the implantable circuit 1010 makes this determination by comparing the temperature to a reference signal representing the second overtemperature threshold and by determining the length of time the temperature is greater than the second overtemperature threshold.

[0304] If the control circuit 1032 determines that the temperature exceeds the second overtemperature threshold for a third threshold time length, the control circuit proceeds to step 1194 and at least temporarily opens the fuse, as described above; otherwise, the control circuit proceeds to step 1206.

[0305] At step 1206, the control circuit 1032 maintains the fuse 1014 electrically closed and returns to step 1192.

[0306] However, if the control circuit 1032 proceeds from any of steps 1192–1204 to step 1194, the control circuit proceeds to step 1208.

[0307] At step 1208, the control circuit 1032 determines whether a count exceeds a count threshold (the count represents the number of times the control circuit has opened the fuse 1014 since the battery 1012 has been powering the implantable circuit 1010). If the control circuit 1032 determines that the count exceeds the count threshold, the control circuit proceeds to step 1210; otherwise, the control circuit proceeds to step 1212.

[0308] In step 1210, the control circuit 1032 permanently opens the fuse 1014. And if the prosthesis has a power source other than the battery 1012 to power the implantable circuit 1010 even when the fuse 1014 is open, the control circuit 1032 generates an error message and one or more data packets including the error message, stores the one or more data packets in the storage circuit 1024, and transmits the one or more data packets to the base station 1004 via the RF transceiver 1026, the filter 1028, and the antenna 1030 Figure 3 ) in response to the next polling request from the base station.

[0309] In step 1212, the control circuit 1032 determines whether the delay has passed. If the delay has not passed, the control circuit 1032 effectively waits until the delay has passed. However, if the delay has passed, the control circuit 1032 proceeds to step 1214.

[0310] In step 1214, the control circuit 1032 closes the fuse 1014 and returns to step 1192. Steps 1212 and 1214 allow the control circuit 1032 to reset the fuse 1014, where it is possible that the event that caused the control circuit 1032 to open the fuse in step 1194 was transient such that the fuse does not need to be permanently opened.

[0311] Still referring Figure 4 and Figure 27 , alternative embodiments considering the relevant operation of the fuse 1014 and the implantable circuit 1010 are contemplated. For example, the fuse 1014 can be a one-time openable fuse that cannot be controlled by the control circuit 1032 such that once the fuse is open, it remains open. Additionally, the fuse 1014 can open in response to less than all or only one of the conditions described in connection with steps 1192–1204. For example, the fuse 1014 can open only in response to the current through the fuse exceeding an overcurrent threshold according to step 1192. Additionally, one or more steps of the flowchart 1190 can be omitted, and one or more additional steps can be added. Additionally, the embodiments described in connection with Figures 3 to 26 are applicable to the relevant operation of the fuse 1014 and the implantable circuit 1010.

[0312] The following are exemplary embodiments of the present disclosure: 1) An implantable medical device, comprising: a. A circuit configured to be fixedly attached to an implantable prosthesis device; b. A power component; and c. A device configured to decouple the circuit from the power component. 2) The implantable medical device according to embodiment 1, wherein the circuit includes an implantable reporting processor. 3) The implantable medical device as described in Embodiment 1, wherein the power assembly includes a battery. 4) The implantable medical device as described in Embodiment 1, wherein the device includes a fuse. 5) The implantable medical device as described in Embodiment 1, wherein the device includes a resettable fuse. 6) The implantable medical device as described in Embodiment 1, wherein the device includes a switch. 7) The implantable medical device as described in Embodiment 1, wherein the device includes a one-time disconnectable fuse. 8) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the current passing through the device exceeding a threshold current. 9) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the voltage passing through the device exceeding a threshold voltage. 10) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the temperature exceeding a threshold temperature. 11) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the circuit temperature exceeding a threshold temperature. 12) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the power assembly temperature exceeding a threshold temperature. 13) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the device temperature exceeding a threshold temperature. 14) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the current passing through the device exceeding a threshold current for at least a threshold time. 15) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the voltage passing through the device exceeding a threshold voltage for at least a threshold time. 16) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the temperature exceeding a threshold temperature for at least a threshold time. 17) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power assembly in response to the circuit temperature exceeding a threshold temperature for at least a threshold time. 18) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power component in response to the temperature of the power component exceeding a threshold temperature for at least a threshold time. 19) The implantable medical device as described in Embodiment 1, wherein the device is configured to decouple the circuit from the power component in response to the temperature of the device exceeding a threshold temperature. 20) The implantable medical device as described in Embodiment 1, further comprising at least one mechanical component configured to act when the device decouples the circuit from the power component. 21) An implantable medical device, comprising: a. A circuit configured to be fixedly attached to an implantable prosthetic device; b. A battery; and c. A fuse coupled between the circuit and the battery. 22) A method comprising electrically disconnecting a fuse disposed between a circuit and a battery, with at least the fuse and the circuit disposed on an implantable prosthetic device. 23) The method as described in Embodiment 22, further comprising operating at least one mechanical component of the implantable prosthetic device when the fuse is electrically disconnected. 24) The method as described in Embodiment 22, wherein the battery is disposed on the implantable prosthetic device. 25) An implantable medical device, comprising: a. At least one sensor configured to generate a sensor signal; and b. A control circuit configured to cause the at least one sensor to generate a sensor signal at a frequency related to a telemedicine code. 26) An implantable medical device, comprising: a. At least one sensor configured to generate a sensor signal; and b. A control circuit configured to cause the at least one sensor to generate a sensor signal at a frequency that allows a doctor to be eligible for payment according to a telemedicine insurance code. 27) An implantable medical device, comprising: a. At least one sensor configured to generate a sensor signal; and b. A control circuit configured to cause the at least one sensor to generate a sensor signal at a frequency that allows a doctor to be eligible for full payment according to a telemedicine insurance code. 28) A method comprising generating a sensor signal related to an implantable medical device at a frequency that allows a doctor to be eligible for payment according to a telemedicine insurance code. 29) A method comprising generating a sensor signal related to an implantable medical device at a frequency that allows a doctor to be eligible for full payment according to a telemedicine insurance code. 30) An implantable prosthesis, comprising: a. A housing; and b. An implantable circuit disposed in the housing and configured i. To generate at least one first signal representative of movement; ii. To determine whether the signal meets at least one first criterion; and iii. In response to determining that the signal meets at least one first criterion, to transmit the signal to a remote location. 31) The implantable prosthesis according to embodiment 30, wherein the housing includes a tibial extension. 32) The implantable prosthesis according to embodiment 30, wherein the movement includes movement of the patient. 33) The implantable prosthesis according to embodiment 30, wherein the movement includes the patient walking. 34) The implantable prosthesis according to embodiment 30, wherein at least one first criterion includes the signal representing movement for at least a threshold duration. 35) The implantable prosthesis according to embodiment 30, wherein at least one first criterion includes the signal representing movement for at least a threshold number of events. 36) The implantable prosthesis according to embodiment 30, wherein: a. The movement includes the patient walking; and b. At least one first criterion includes the signal representing movement for at least a threshold number of steps taken by the patient. 37) The implantable prosthesis according to embodiment 30, wherein the implantable circuit is further configured: a. To determine whether the movement meets at least one second criterion before determining whether the signal meets at least one first criterion; and b. To determine whether the signal meets at least one first criterion in response to determining that the movement meets the second criterion. 38) The implantable prosthesis according to embodiment 37, wherein at least one second criterion includes the movement being the patient walking. 39) The implantable prosthesis according to embodiment 30, wherein the implantable circuit is further configured: a. To determine whether the movement meets at least one second criterion in response to the signal, before determining whether the signal meets at least one first criterion; and b. To determine whether the signal meets at least one first criterion in response to determining that the movement meets the second criterion. 40) The implantable prosthesis according to embodiment 30, wherein the implantable circuit is further configured: a. To determine whether the movement meets at least one second criterion in response to the signal; and b. To stop generating the signal in response to determining that the movement does not meet at least one second criterion. 41) The implantable prosthesis as described in embodiment 30, wherein the implantable circuit is further configured: a. In response to the signal, to determine whether the movement meets at least one second criterion; and b. In response to determining that the movement does not meet at least one second criterion, to stop generating the signal before determining whether the signal meets at least one first criterion. 42) The implantable prosthesis as described in embodiment 30, wherein the implantable circuit is further configured: a. In response to determining that the signal meets at least one first criterion, to store the signal; and b. To send the stored signal to a remote location. 43) The implantable prosthesis as described in embodiment 30, wherein the implantable circuit is further configured to encrypt the signal before sending the signal to a remote location. 44) The implantable prosthesis as described in embodiment 30, wherein the implantable circuit is further configured to encode the signal before sending the signal to a remote location. 45) The implantable prosthesis as described in embodiment 30, wherein the implantable circuit is further configured: a. To generate a message including the signal; and b. wherein sending the signal includes sending the message. 46) The implantable prosthesis as described in embodiment 30, wherein the implantable circuit is further configured: a. To generate a data packet including the signal; and b. wherein sending the message includes sending the data packet to a remote location. 47) A base station, comprising: a. A housing; and b. A base station circuit disposed in the housing and configured i. To receive from the implantable prosthesis at least a first signal representative of movement; ii. To send at least one first signal to a destination; iii. To receive at least one second signal from a source; and iv. To send at least one second signal to the implantable prosthesis. 48) The base station as described in embodiment 47, wherein the base station circuit is configured to poll the implantable prosthesis for the first signal. 49) The base station as described in embodiment 47, wherein the base station circuit is configured to decrypt at least one first signal before sending at least one first signal to a destination. 50) The base station as described in embodiment 47, wherein the base station circuitry is configured to encrypt at least one first signal before transmitting the at least one first signal to a destination. 51) The base station as described in embodiment 47, wherein the base station circuitry is configured to decode at least one first signal before transmitting the at least one first signal to a destination. 52) The base station as described in embodiment 47, wherein the base station circuitry is configured to encode at least one first signal before transmitting the at least one first signal to a destination. 53) A method comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to current through the fuse exceeding an overcurrent threshold. 54) A method comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to current through the fuse exceeding an overcurrent threshold for at least a threshold time. 55) A method comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to voltage across the fuse exceeding an overvoltage threshold. 56) A method comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to voltage across the fuse exceeding an overvoltage threshold for at least a threshold time. 57) A method comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to temperature exceeding an overtemperature threshold. 58) A method comprising disconnecting a fuse on an implantable prosthesis disposed between a power source and an implantable circuit in response to temperature exceeding an overtemperature threshold for at least a threshold duration. 59) A method comprising: a. generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; and b. transmitting the sensor signal to a remote location. 60) A method comprising: a. generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. sampling the sensor signal; and c. transmitting the sample to a remote location. 61) A method comprising: a. generating a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. determining whether the sensor signal represents a qualified event; and c. transmitting the signal to a remote location in response to determining that the sensor signal represents a qualified event. 62) A method comprising: a. Generate a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Receive a polling signal from a remote location; and c. In response to the polling signal, transmit the sensor signal to the remote location. 63) A method comprising: a. Generate a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Generate a message comprising the sensor signal or data representative of the sensor signal; and c. Transmit the message to a remote location. 64) A method comprising: a. Generate a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Generate a data packet comprising the sensor signal or data representative of the sensor signal; and c. Transmit the data packet to a remote location. 65) A method comprising: a. Generate a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Encrypt at least a portion of the sensor signal or data representative of the sensor signal; and c. Transmit the encrypted sensor signal to a remote location. 66) A method comprising: a. Generate a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Encode at least a portion of the sensor signal or data representative of the sensor signal; and c. Transmit the encoded sensor signal to a remote location. 67) A method comprising: a. Generate a sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Transmit the sensor signal to a remote location; and c. After transmitting the sensor signal, place an implantable circuit associated with the prosthesis into a low power mode. 68) A method comprising: a. Generate a first sensor signal in response to movement of a subject in which the prosthesis is implanted; b. Transmit the first sensor signal to a remote location; c. After transmitting the sensor signal, place at least one component of an implantable circuit associated with the prosthesis into a low power mode; and d. After a period of time in the low power mode configured for the implantable circuit has elapsed, generate a second sensor signal in response to movement of the subject. 69) A method, comprising: a. Receiving a sensor signal from a prosthesis implanted in a subject; and b. Transmitting the received sensor signal to a destination. 70) A method, comprising: a. Sending a request to a prosthesis implanted in a subject b. Receiving a sensor signal from the prosthesis after sending the request; and c. Transmitting the received sensor signal to a destination. 71) A method, comprising: a. Receiving a sensor signal and at least one identifier from a prosthesis implanted in a subject; b. Determining whether the identifier is correct; and c. Responsive to determining that the identifier is correct, transmitting the received sensor signal to a destination. 72) A method, comprising: a. Receiving a message including a sensor signal from a prosthesis implanted in a subject; b. Decrypting at least a portion of the message; and c. Transmitting the decrypted message to a destination. 73) A method, comprising: a. Receiving a message including a sensor signal from a prosthesis implanted in a subject; b. Decoding at least a portion of the message; and c. Transmitting the decoded message to a destination. 74) A method, comprising: a. Receiving a message including a sensor signal from a prosthesis implanted in a subject; b. Encoding at least a portion of the message; and c. Transmitting the encoded message to a destination. 75) A method, comprising: a. Receiving a message including a sensor signal from a prosthesis implanted in a subject; b. Encrypting at least a portion of the message; and c. Transmitting the encrypted message to a destination. 76) A method, comprising: a. Receiving a data packet including a sensor signal from a prosthesis implanted in a subject; b. Decrypting at least a portion of the data packet; and c. Transmitting the decrypted data packet to a destination. 77) A method, comprising: a. Receiving a data packet including a sensor signal from a prosthesis implanted in a subject; b. Decoding at least a portion of the data packet; and c. Transmit the decoded data packet to the destination. 78) A method, comprising: a. Receiving a data packet including a sensor signal from a prosthesis implanted in a subject; b. Encoding at least a portion of the data packet; and c. Transmitting the encoded data packet to the destination. 79) A method, comprising: a. Receiving a data packet including a sensor signal from a prosthesis implanted in a subject; b. Encrypting at least a portion of the data packet; and c. Transmitting the encrypted data packet to the destination. 80) A method, comprising: a. Receiving a sensor signal from a prosthesis implanted in a subject; b. Decrypting at least a portion of the sensor signal; and c. Transmitting the decrypted sensor signal to the destination. 81) A method, comprising: a. Receiving a sensor signal from a prosthesis implanted in a subject; b. Decoding at least a portion of the sensor signal; and c. Transmitting the decoded sensor signal to the destination. 82) A method, comprising: a. Receiving a sensor signal from a prosthesis implanted in a subject; b. Encoding at least a portion of the sensor signal; and c. Transmitting the encoded sensor signal to the destination. 83) A method, comprising: a. Receiving a sensor signal from a prosthesis implanted in a subject; b. Encrypting at least a portion of the sensor signal; and c. Transmitting the encrypted sensor signal to the destination. 84) An implantable circuit for an implantable prosthesis. 85) An implantable or implantable prosthesis comprising an implantable circuit. 86) An implantable or implantable prosthesis comprising a fuse. 87) A base station for communicating with an implantable or implantable prosthesis. D. Computer system for analysis, information dissemination, ordering, and supply: processing IMU recorded during patient monitoring Data

[0313] As discussed in the previous parts of this document, in the home, work environment, doctor's office, or other environments where the patient frequently resides, the patient is intermittently monitored by incorporating sensors in the implant in combination with a base station or another communication device. Sensor data is uploaded from the implant to the base station, temporarily stored within the base station during data accumulation in a patient monitoring session, and subsequently transmitted from the base station to a data processing application running in one or more independent servers, data centers, or cloud computing facilities. As discussed in the previous parts of this document, the data can be transmitted through a variety of different types of communication media, associated communication devices and subsystems, and operating system communication services and functions using many different types of data transmission protocols. The data is encoded and encrypted according to a predefined format and digital-encoding convention. In the current part of this document, it is assumed that the monitoring data is transmitted from the base station to the data processing application as a series of ordered messages. It is assumed that the data includes a time series of encoded IMU data vectors (discussed in more detail below), patient identifiers, device identifiers, configuration parameters of the IMU, and other information required by the data processing application to interpret the encoded IMU data vectors, identify the patient and the sensor-equipped implant, authorize the receipt and processing of monitoring data from the patient, generate output results and output reports, and distribute the output results and output reports to various predefined recipients such as clinicians, insurance providers, and other such recipients. In alternative implementations, the monitoring data can be transmitted as one or more files through various file transfer protocols and facilities, although of course the file transfer protocol is implemented on top of the message protocol. In some implementations, the patient-monitoring-session data can be received on various types of optical or electromagnetic data-storage devices physically transported to the computer or computing facility in which the data processing application runs.

[0314] The main tasks of the data-entry and monitoring-data-processing components of the data-processing application are to convert the raw sensor data output by the sensors incorporated in the implant during a monitoring session into digitally encoded, human-readable reports and / or digitally encoded output results that can be forwarded to clinicians, insurance providers, and / or additional automated systems for further automated processing tasks. In addition, the monitoring-data-processing component of the data processing application may trigger various different types of events and alerts based on the output results of the monitoring session, and these events and alerts can be processed by other components of the data-processing application or by other applications running concurrently in one or more computers or distributed computer systems.

[0315] In different implementations, a very large number of different methods can be employed to analyze the raw sensor data to generate output results. Below will refer to Figures 28 to 37HDescribe a method. In alternative implementations, different and / or additional types of sensor data may be included in the monitoring data received from multiple different sensors by a data processing application and incorporated into the analysis. For example, an implant may include temperature sensors, various types of chemical sensors, acoustic sensors, and other types of sensors, and the data output by these sensors can be used to diagnose various types of problems and abnormalities that occur in various different types of implants. The current discussion focuses on IMU data generated by implants located near the knee joint. The initial part of the following discussion specifically addresses the processing of IMU output data to generate a number of metrics that can then be used to infer the operating conditions and characteristics of a prosthetic knee joint, as well as to infer the characteristics of a patient's walking activity.

[0316] Figure 28 Illustrates a three-dimensional Cartesian coordinate space and the representation of a point in the space by a vector. The three-dimensional coordinate space is defined by the familiar x, y, and z coordinate axes 2201 - 2203 respectively. The position of point p 2204 in this space can be represented by vector r 2205, and the vector components r x 、r y and r z correspond to the projected lengths of the vector on the coordinate axes 2206 - 2208. Different points q 2209 are associated with different position vectors 2210. A time vector-valued function f(t) can return the position vector at each time point within the time domain of the function. One type of vector-valued function can return the position vectors at different time points that describe a space curve or trajectory (such as an object moving in space). Another type of discrete vector-valued function can be a function that represents the time-varying output of an IMU.

[0317] Figures 29A to 29B Illustrates the data output by an IMU. An IMU can be considered a black box device 2220 with a fixed internal coordinate system 2222 - 2224, which outputs a time-ordered sequence of 6-dimensional vectors 2226 - 2228, where the ellipsis 2229 indicates the continuation of the sequence. The ellipsis is used to indicate the entire Figures 28 to 37HAdditional elements of the sequence or series. Each 6-dimensional vector, such as vector 2226, includes three numerical indications 2230 of the linear acceleration of the IMU in the three axis directions and three numerical indications 2232 of the rotational speed or angular velocity about each of the three IMU axes. The vector output by the IMU is associated with a sequence number, such as the sequence number "1" 2234 associated with vector 2226. Typically, the acceleration and angular velocity are sampled at fixed intervals in time 2236 - 2237, so that the relative sampling time of each vector is a linear function of the sequence number associated with the vector. The sampling rate, as well as the meaning of the numerical values, is specified by the IMU parameters, including fixed parameters and configuration or operating parameters. As described above, the data received by the data processing application includes sufficient information about these parameters to decode the numerical values into acceleration and angular velocity expressed in a particular set of units and to determine the sampling interval. In the following discussion, it is assumed that the vector data has been processed (if necessary) so that the angular velocity and acceleration data refer to the same internal coordinate system. It is also assumed that the sampling rate is uniform over the data. When the sampling rate is non-uniform, then the sampling-rate-dependent portion of the analysis discussed below may need to be performed on subsequences of the data-vector IMU output segmented at a uniform sampling rate.

[0318] Figure 29BDescribes a type of information that can be derived from the data vector output of an IMU. As discussed above, the vector output by the IMU can be considered a vector-valued function of time 2240. This function can be transformed through a trajectory-reconstruction process 2242 to produce a corresponding vector-valued function 2244 that returns the position vector for each point in the time domain of the function, thereby describing the spatial curve trajectory 2246 of the origin 2248 of the IMU's internal coordinate system, which represents the motion of the IMU origin relative to its initial position in space and time with respect to the origin of the IMU's internal coordinate system. The IMU-output vector-valued function can also be transformed through an orientation-reconstruction process 2252 to produce a corresponding vector-valued function 2254 whose output describes the direction vector of the orientation of the IMU's internal coordinate system at any point in time relative to its initial orientation with respect to the internal coordinate system 2250. When the true-world initial position and orientation of the IMU are known, the spatial curve can be oriented relative to the true-world coordinate system, and the relative orientation produced by the vector-valued function 2254 can be transformed to the orientation defined by the true-world coordinate system. Of course, there are many different true-world coordinate systems. As discussed below, the current analysis considers a coordinate system in which the x-axis is parallel to the ground and its direction is parallel to the direction in which the patient is walking, the z-axis is perpendicular to the ground and parallel to the patient's bilateral symmetry axis during the monitoring session, and the y-axis is perpendicular to both the x-axis and the z-axis. This coordinate system is referred to as the "natural coordinate system" in the following discussion. Many other coordinate systems can be used in alternative implementations, including coordinate systems fixed to specific rigid parts of the patient's body, as well as cylindrical or spherical coordinate systems fixed to the patient or fixed to a position and orientation relative to the Earth's surface.

[0319] The above-mentioned trajectory-reconstruction and orientation-reconstruction processes will not be discussed further. These processes are well-known and based on Newtonian mechanics, including the integration of acceleration to produce velocity and the integration of linear and angular velocities to produce linear and angular distances. However, additional, complex mathematical processes are employed in trajectory and orientation reconstruction. As with all interpretations of instrument data, there are many sources of error, and errors can propagate and accumulate, resulting in significant differences between the calculated trajectories and orientations and those that the IMU actually experiences during IMU monitoring positions and orientations. Whenever possible, additional data and information are used to detect and interpret instrument errors during the IMU-data processing. In the IMU-data processing method described below, the numerical values of the IMU output vectors are transformed into numerical values representing acceleration and angular velocity relative to the natural coordinate system. One method of performing this transformation is discussed below.

[0320] Figures 30A to 30G Each illustrates a complex spatial curve representing motion and decomposing the complex spatial curve into component motions. Figure 30AShows a small segment of a harmonic spatial trajectory within a three-dimensional Cartesian space volume. The harmonic trajectory 2260 is contained within the xz plane that coincides with the x and z axes 261 - 2262 and the origin 2264. This harmonic trajectory is represented by a vector-valued function 2266 that is a vector-valued function of time 2268. This type of harmonic trajectory can be similar at a high level to the trajectory of an IMU within an implant proximal to the knee joint during patient walking. Figure 30B introduces an additional motion component for Figure 30A the motion or trajectory shown. The new motion component 2270 is a linear harmonic motion in the y direction centered at the origin of the internal IMU coordinate system and is represented by a vector-valued function 2272. The composite vector-valued function 2274 that includes Figure 30A both the original trajectory 2276 and the new motion component 2278 shown in is shown as a curve 2280 within the spatial volume 2282. The new trajectory remains periodic with respect to the x axis but has a rather complex shape characterized by periodic deviations in the y direction with higher frequencies and smaller amplitudes than the periodic frequency and amplitude of the original harmonic trajectory. Figure 30C Illustrates adding a new linear, harmonic motion in the x direction 2292 to the original trajectory 2292 to produce a composite vector-valued function 2294 that represents the complex spatial curve 2296 shown within the volume 2298. In this case, the complex spatial curve 2296 is planar but includes periodic deviations in the x direction with higher frequencies and smaller amplitudes than the periodic frequency and amplitude of the original harmonic motion shown in Figure 30A . Figure 30D Using the same graphical convention as used in Figures 30A to 30C illustrates the spatial curve 2300 that represents the composite vector-valued function 2302 that includes Figure 30A the original harmonic trajectory 2304 shown in, the y-direction motion component discussed above with reference to Figure 30B , and the x-direction motion component discussed above with reference to Figure 30C . The spatial curve 2300 is rather complex even though it represents a relatively simple vector-valued function that combines only three component motions.

[0321] The trajectory of the IMU within a knee implant during walking can be an extremely complex spatial curve, characterized by having many different component motions oscillating at many different frequencies. One component motion can be the rotation of the implant about the knee joint as the lower leg rotates relative to the thigh during walking. Another component motion is the motion of the patient in the direction of walking. The combination of these two motions can produce a periodic trajectory in the xz plane of the natural coordinate system. However, there may be many other component motions, including lateral motion of the knee joint, component motions due to the swaying of the patient's double lateral axis during walking, and high-frequency motions related to frictional forces within the knee joint and other components of the patient's body, as well as the complex geometry of the patient's body components, and may additionally include high-frequency motions due to vibrations or nudging of the implant relative to the patient's body due to loose fit and other reasons. Thus, the spatial trajectory of the IMU may be too complex to be decomposed into component motions by spatial domain analysis techniques.

[0322] As Figure 30D shown, compared to the reference trajectory Figure 30A shown, component motions with higher frequencies and lower amplitudes, when added to the component motions responsible for the reference trajectory, produce relatively fine-grained and complex deviations relative to the reference trajectory. In contrast, additional component motions with the same frequency as the motion generating the reference trajectory tend to produce geometric alterations in the basic trajectory. Figure 30E Illustrates adding two low-amplitude component motions 2310 and 2312 to the component motion 2314, which generates the reference trajectory to produce a composite vector-valued function 2316 represented by the spatial curve 2318 shown in volume 2320. This new trajectory is clearly periodic and has the same frequency as the reference trajectory ( Figure 30A 2260 in

[0323] Figure 30F Illustrates corresponding to by referring above to Figure 30EThe locus of the vector-valued function obtained by subtracting the reference trajectory function 2320 from the complex function 2316 under discussion. The locus 2330 generated by the vector-valued function representing the difference between the vector-valued function 2316 and the reference vector-valued function 2320 is an ellipse. This is not surprising because by subtracting the reference trajectory, there is no longer a translational motion component corresponding to the movement of the patient along the spatial path during patient walking. The elliptical locus can have different orientations and eccentricities, depending on the specific harmonic component motions retained in the vector-valued function representing the difference between the complex vector-valued function and the reference trajectory. When there is only one linear harmonic motion component left in the axis directions, the elliptical locus collapses into a line segment representing the linear harmonic motion. As Figure 30G shown, the elliptical locus 2340 can be projected onto each natural axis to generate the magnitudes 2342 - 2344 of the sum of the component motions in the x, y, and x directions. Thus, the patient's walking trajectory can be described as a composite motion obtained by adding the x, y, and x magnitudes of the elliptical locus representing the additional motion components at the same frequency as the walking trajectory frequency and the x, y, and x magnitudes of the elliptical locus representing the frequencies of the additional motion components and additional non-gait-frequency to the reference trajectory. As discussed below, Fourier analysis is a technique that can be used to decompose a complex multi-frequency-component motion trajectory into component motions of different frequencies. When considering a series of frequencies or frequency bands rather than a single frequency, the above elliptical locus may become somewhat distorted, but can still be analyzed, as discussed above with reference to Figure 30G to obtain the x, y, and x magnitudes of the frequency band. The specific elliptical locus obtained for a particular frequency band may represent a single rotational-motion component or multiple linear harmonic motion components, so it is not possible to accurately decompose the complex spatial curve into a set of motion components corresponding to the individual motions of separate body parts and implant parts, but the complex spatial curve can be decomposed into a set of x, y, and x magnitudes for each of a number of different frequency bands, which when recombined, produce a motion associated with a trajectory very similar to the original measured trajectory. The x, y, and x magnitudes for each of a number of different frequency bands can be used as very detailed and reliable digital fingerprints for many different types of trajectories generated by specific problems, pathologies, and other causes superimposed on the underlying gait profile or trajectory.

[0324] Other types of techniques, including wavelets, can be used in place of, or in addition to, Fourier techniques and, in some cases, may have significant advantages over Fourier techniques. As an example, many different high-frequency motion components may be periodic, but their amplitudes may decrease and increase periodically at lower frequencies. For example, a loose implant screw may cause relatively high-frequency vibrations, but only for a relatively short period of time after each heel strike or knee rotation. Thus, other analysis methods, including wavelets, may help to relate high-frequency motion components to low-frequency gait-related events. For example, these techniques can be used to provide an indication that high-frequency motion components are closely related to heel strike, maximum knee rotation, and other gait-related events. In turn, these types of correlations may be used to decompose higher-frequency motion components into underlying, physiology-based linear harmonics.

[0325] The above reference Figures 30A to 30G to the decomposition of a periodic space curve into component harmonic motions provides a type of digital fingerprint for the component harmonic motions of a periodic space curve. However, there may be non-periodic motions, such as occasional slippage of an implant or non-periodic muscle contractions. Figure 31 illustrates a method for dealing with various types of non-periodic motions. Consider the space curve 2350 plotted in the three dimensions 2352 in Figure 31 . The curve is generally continuous but includes short linear portions 2354, which may represent a sudden slippage of an implant containing an IMU. This type of non-periodic motion can be identified by a pair of discontinuity points 2356 and 2357 in the space curve. Since the IMU samples acceleration and rotational velocity discretely, the space curve obtained from the IMU data is generally discrete rather than continuous, although a continuous curve can be obtained through various types of interpolation. A small portion 2358 of the space curve 2350 near the discontinuity point 2356 is shown at a much higher resolution at the top of Figure 31 . The individual points of the discrete curve are represented by dots, such as dots 2360 - 2361. The resolution is high enough that portions of the curves 2362 and 2363 appear almost linear. The intersection of these two linear portions produces an intersection angle 2364 with the vertex at the discontinuity point. When the intersection angle of the best-fit line segments of two series of points before and after that point is greater than a threshold value 2365, the point in the trajectory can be identified as a discontinuity point. A discontinuity operator can be mathematically moved along the trajectory to identify pairs of discontinuity points 2366 - 2367 that define non-periodic motions, such as offsets or slippages 2368. The average velocity in each component direction can be calculated, along with the distance of the non-periodic motion, for such non-periodic motions bracketed by the discontinuity points in order to characterize the severity of the slippage or offset.

[0326] Figures 32A to 32F Each describes a principal-component-analysis method for rotating an initial coordinate system to a coordinate system in which its axis is aligned with the distribution of points representing experimental observations. Principal component analysis is often used in data analysis. Each observation is a vector of measured data values. Figure 32A Illustrates the equivalence between observations made at a particular time point and P-dimensional vectors in a P-dimensional space. In Figure 32A the example shown, there are only three measurements S1, S2, and S3, and thus P = 3. Each measurement is considered a dimension, and thus three Cartesian axes 2382, 2383, and 2384 are assigned to one of the measurements respectively. Each observation is a tuple of three measured data values 2386, which, when used as components of a vector, describe a vector 2388 in the P-dimensional measurement space.

[0327] Figure 32B Represents observations, each consisting of a set of measured data values for each data source obtained or calculated at a particular time point, as a matrix. For a particular time point, such as time point t i 2394, each row of measured data values, such as row 2392, can be considered a P-dimensional vector 2396, called an "observation". A sequence of N observations can be organized as an N×P matrix 2398, where each row represents an observation and each column represents the time series of data values for a particular measurement. Similarly, the time point corresponding to an observation is inferred from the row index of the observation, since the observations represent a time series with a uniform time interval between consecutive observations. Alternatively, the transpose of the matrix can be considered to include column vectors representing observations.

[0328] Figure 32C Illustrates the scaling and normalization of the observation group represented by the matrix Several statistical parameters are calculated for each time series of measured data values for a particular measurement, such as the measured data values of the second measurement included in the second column 2402 of the matrix 2404, including the mean μ j 406, variance 408, and standard deviation σ j 410. Then, for each column j, each measured data value in the column can be scaled and normalized by subtracting the mean measured data value from the measured data value and dividing by the standard deviation 2412. When this is done for each element in the matrix, a scaled and normalized matrix X 2414 is produced.

[0329] Figure 32D and Figure 32E Illustrates eigenvectors and eigenvalues. The 3×3 matrix A 2422 and the column vector u 2424 are shown in Figure 32DThe top. When u is an eigenvector of matrix A, equation 2426 represents the relationship between the eigenvector u and its corresponding eigenvalue λ, which is a constant or a scalar. This equation expands into matrix equation 2428 in matrix form. Using a set of simple matrix algebra operations 2430 and 2432 of equation 2426, it can be shown that the eigenvector u can be generated by multiplying the inverse of matrix A - λI by the column vector 0 2434 (where I is the identity matrix), or the inverse of matrix A - λI does not exist, as expressed by the fact that the determinant of this matrix is 0 2436. Only the latter proposition is reasonable, which indicates that by solving Figure 32E The polynomial equation 2444 obtained from expression 2436 through the expansion 2442 shown in -1 one can find the eigenvalues of matrix A. Since the polynomial equation 2444 is of the 3rd order, i.e., the dimension of u, there are usually 3 eigenvalues, although one or more roots of equation 2444 can be degenerate. Matrix equation 2446 represents the relationship between matrix A, matrix U (where each column is one of the eigenvectors of matrix A), and matrix Λ (which is a diagonal matrix, where the el...

Claims

1. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the medial side of the implant than on the lateral side of the implant.

2. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the lateral side of the implant than on the medial side of the implant.

3. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the anterior side of the implant than on the posterior side of the implant.

4. A tibial insert for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on the posterior side of the implant than on the anterior side of the implant.

5. A tibial insert or articular spacer for an implantable knee prosthesis, comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mm thicker on one of the medial, lateral, anterior, and / or posterior sides of the implant than on the opposite side of the implant, wherein the medial and lateral sides are opposite sides and the anterior and posterior sides are opposite sides.

6. The tibial insert according to any one of claims 1-5, wherein the tibial insert is made of polyethylene or polyetheretherketone (PEEK).

7. The tibial insert according to any one of claims 1-6, wherein the tibial insert is customized for the patient.

8. The tibial insert according to any one of claims 1 to 7, wherein the insert is manufactured by 3-D printing or by molding.

9. An implantable medical device, comprising: a circuit configured to be fixedly attached to an implantable prosthesis device; a power component; and a device configured to decouple the circuit from the power component.

10. An implantable medical device, comprising: a circuit configured to be fixedly attached to an implantable prosthesis device; a battery; and a fuse coupled between the circuit and the battery.

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