Systems and methods for monitoring a patient's spine, balance, gait, or posture - Patents.com

Abstract

a sensor unit configured and arranged to be disposed on a user, the sensor unit comprising an accelerometer and a communication arrangement; and at least one processor configured and arranged to perform actions including receiving signals from the accelerometer of the sensor unit, determining a number of steps from the signals, determining a walking speed from the signals, determining a stride length from the signals, determining a posture of the user from the signals, and determining a walking posture index responsive to the number of steps, the walking speed, the stride length, and the posture. In other examples, the system may be used to determine criteria other than a walking posture index, such as a fall prediction score, based on the walking speed and posture deviation.

Description

[Technical field]

[0001] This application claims priority to U.S. Patent Application No. 16 / 775,026, filed January 28, 2020, which issued as U.S. Patent No. 10,863,928, the entirety of which is incorporated herein by reference.

[0002] The present invention is directed to the areas of patient monitoring, physical therapy and rehabilitation. The present invention is also directed to systems and methods for monitoring a patient's spine, balance, gait or posture. [Background technology]

[0003] The diagnosis, treatment, and management of musculoskeletal patients in the modern health care environment are challenging. The lack of objective scoring systems and meaningful real-time data streams based on meaningful criteria contributes to these challenges. Current classification systems rely on subjective data with influence based on the health care provider's own opinion and the patient's own opinion rather than objective evidence.

[0004] Monitoring the prognosis of spinal treatment or surgery is important and guides further treatment. Postoperative assessment of patient outcome, their individual performance, and validation of implemented therapeutic interventions are facilitated by observation and measurement. However, the majority of published data were based on subjective prognostic tools, such as the Oswestry Disability Index (ODI) / Neck Disability Index (NDI), Japanese Orthopaedic Association (JOA) scores, or visual analogue scale (VAS). Unfortunately, subjective tools capturing this information are limited by their subjective nature, with biases, timing issues, day-to-day variability, and compliance. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2016 / 0302721 [Patent Document 2] US Patent Application Publication No. 2016 / 0310066 [Patent Document 3] US Patent Application Publication No. 2017 / 0143261 [Patent Document 4] US Patent Application Publication No. 2017 / 0147789 [Patent Document 5] US Patent Application Publication No. 2017 / 0181698 Summary of the Invention [Means for solving the problem]

[0006] One embodiment is a system for monitoring a user including a sensor unit configured and arranged to be disposed on a user, the sensor unit including an accelerometer and a communication arrangement, and at least one processor configured and arranged to perform actions including receiving a signal from the accelerometer of the sensor unit, determining a step count from the signal, determining a gait velocity from the signal, determining a step distance from the signal, determining a posture of the user from the signal, and determining a gait posture index responsive to the step count, gait velocity, stride distance and posture.

[0007] In at least some embodiments, the at least one processor includes a sensor processor disposed in the sensor unit. In at least some embodiments, the system further includes a patient device including a display and a communication arrangement configured for communication with the sensor unit. In at least some embodiments, the at least one processor includes a sensor processor disposed in the sensor unit and a patient device processor disposed in the patient device, and the act further includes displaying the gait posture index on a display of the patient device.

[0008] In at least some embodiments, the system further includes a clinician device including a display and a communications arrangement configured for communication with the sensor unit. In at least some embodiments, the at least one processor includes a sensor processor disposed in the sensor unit and a clinician device processor disposed in the clinician device, and the act further includes displaying the gait posture index on a display of the clinician device.

[0009] In at least some embodiments, determining the walking posture index includes using the measurements to determine a score for each of step count, walking speed, stride length, and posture, and combining the scores to obtain the walking posture index. In at least some embodiments, combining the scores includes weighting the scores and adding the weighted scores together to determine the walking posture index. In at least some embodiments, determining the score includes determining a score for one of the measurements by determining a location of the measurement between a minimum and maximum value for the measurement. In at least some embodiments, the actions further include performing an action selected from: providing an alert to a user or clinician if the walking posture index meets a threshold criterion, modifying a therapy, physical therapy regimen, or exercise regimen based on the walking posture index, recommending a modification of a therapy, physical therapy regimen, or exercise regimen based on the walking posture index, or providing a preliminary or final diagnosis based on the walking posture index.

[0010] Another embodiment is a system for monitoring a user including a sensor unit configured and arranged to be disposed on a user, the sensor unit including an accelerometer and a communication arrangement, and at least one processor configured and arranged to perform actions including receiving a signal from the accelerometer of the sensor unit, determining a walking speed from the signal, determining a postural deviation from the signal, and determining a fall prediction score responsive to the number of steps and the walking speed.

[0011] In at least some embodiments, the at least one processor includes a sensor processor disposed in the sensor unit. In at least some embodiments, the system further includes a patient device including a display and a communication arrangement configured for communication with the sensor unit. In at least some embodiments, the at least one processor includes a sensor processor disposed in the sensor unit and a patient device processor disposed in the patient device, and the act further includes displaying the fall prediction score on a display of the patient device.

[0012] In at least some embodiments, the system further includes a clinician device including a display and a communications arrangement configured for communication with the sensor unit. In at least some embodiments, the at least one processor includes a sensor processor disposed in the sensor unit and a clinician device processor disposed in the clinician device, and the act further includes displaying the fall prediction score on a display of the clinician device.

[0013] In at least some examples, determining the fall prediction score includes using the measurements to determine a score for each of the steps and postural deviations and combining the scores to obtain the fall prediction score. In at least some examples, combining the scores includes weighting the scores and adding the weighted scores together to determine the fall prediction score. In at least some examples, determining the score includes determining a score for one of the measurements by determining a location of the measurement between a minimum and maximum value for the measurement. In at least some examples, the actions further include performing an action selected from: providing an alert to a user or clinician if the gait posture index meets a threshold criterion, modifying a therapy, physical therapy regimen, or exercise regimen based on the fall prediction score, recommending a modification of a therapy, physical therapy regimen, or exercise regimen based on the fall prediction score, or providing a preliminary or final diagnosis based on the fall prediction score.

[0014] In other examples, the system may be used to determine criteria other than the Gait Posture Index or Fall Prediction Score, which are described in more detail below.

[0015] Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings, in which like reference numerals refer to like parts throughout the various views unless otherwise specified, in which:

[0016] For a better understanding of the present invention, reference is now made to the following detailed description which should be read in connection with the accompanying drawings. [Brief description of the drawings]

[0017] [Figure 1] 1 is a schematic diagram of one embodiment of a system for monitoring a patient's rehabilitation after an implant surgery according to the present invention; [Diagram 2] 2 is a schematic diagram of one embodiment of a computing device for use in the system of FIG. 1, in accordance with the present invention. [Diagram 3] FIG. 2 is a top view of one embodiment of a sensor module including sensors for monitoring the rehabilitation of a patient according to the present invention. [Figure 4] FIG. 2 is a diagram of an embodiment of a user interface for a mobile device for displaying information obtained from a sensor unit according to the present invention. [Diagram 5] FIG. 11 is a diagram of an embodiment of a user interface for a mobile device for displaying a range of motion measurements according to the present invention. [Figure 6] FIG. 2 is a diagram of an embodiment of a user interface for a mobile device for displaying an overview of exercise repetitions according to the present invention. [Figure 7] FIG. 2 is a diagram of an embodiment of a user interface for a mobile device for displaying information obtained from a sensor unit according to the present invention. [Figure 8] FIG. 11 is a diagram of another embodiment of a user interface for displaying information obtained from a sensor unit according to the present invention. [Figure 9] FIG. 11 is a diagram of a further embodiment of a user interface for displaying information obtained from a sensor unit according to the present invention. [Figure 10] FIG. 11 is a diagram of yet another embodiment of a user interface for displaying information obtained from a sensor unit according to the present invention. [Figure 11] FIG. 11 is a diagram of another embodiment of a user interface for displaying information obtained from a sensor unit according to the present invention. [Figure 12] FIG. 11 is a diagram of a further embodiment of a user interface for displaying information obtained from a sensor unit according to the present invention. [Figure 13] 2 is a flow chart of one embodiment of a method for monitoring a user in accordance with the present invention. [Figure 14] FIG. 11 is a diagram of a further embodiment of a user interface for displaying information obtained from a sensor unit according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] The present invention is directed to the areas of patient monitoring, physical therapy and rehabilitation. The present invention is also directed to systems and methods for monitoring a patient's spine, balance, gait or posture.

[0019] The system may be used to monitor a patient's physical activity or health, to monitor recovery from surgery or other treatment, to monitor the healing process, to monitor physical therapy or rehabilitation of a patient after treatment, or to monitor or verify the extent of a patient's activity, or any combination of these purposes, as described herein. Systems for joint rehabilitation and physical therapy monitoring are described in U.S. Patent Application Publication Nos. 2016 / 0302721, 2016 / 0310066, 2017 / 0143261, 2017 / 0147789, and 2017 / 0181698, which are incorporated herein by reference in their entireties. Similar systems may be used to monitor a patient's spine, balance, gait, posture, etc. Instead of placing the sensor device of the system near a joint, such as the knee, the sensor device may be placed on the chest or back, etc.

[0020] The system includes one or more sensors that can be in communication with a processor that can generate information based on sensor readings and data to facilitate a patient or another user, such as a clinician, physician, physical therapist, nurse, care coordinator, or other suitable person, monitoring, for example, the patient's activity, spine, balance, gait, posture, etc. However, it will be understood that the systems, devices, and methods described herein can be used to monitor other conditions or make other measurements.

[0021] The system includes a wearable sensor unit having one or more sensors. For example, the one or more sensors may be provided on a wearable sensor unit that is attached to the patient's skin. In at least some embodiments, the one or more sensors communicate with a sensor processor in the sensor unit. In at least some embodiments, the sensor processor, or alternatively or additionally, the sensor, communicates with a processor of a patient device, such as a mobile phone, tablet, computer, etc., or communicates with a processor of a clinician device, such as a mobile phone, tablet, computer, etc.

[0022] 1 illustrates one embodiment of a system 100 for monitoring, for example, a patient's spine, balance, gait, posture, etc. The system 100 includes one or more sensors 102, an optional sensor processor 104, a patient device 106 (e.g., a mobile phone, tablet, computer, etc.), a clinician device 108, and a network 60. In at least some embodiments, the one or more sensors 102, and preferably the sensor processor 104 (or one or more of the multiple sensor processors) are provided in a sensor unit that is external to the patient, such as, for example, a device attached to the patient's skin or carried in an article or textile worn by the patient.

[0023] While other embodiments of the system may include fewer or more components than those shown in FIG. 1, the system generally includes a sensor 102 and a processor (such as one or more of a sensor processor 104, a patient device 106, or a clinician device 108) for communicating with the sensor and providing information based on the sensor data. It will be understood that while a sensor unit may include a sensor 102 and a sensor processor 104, other sensors may be included that are not part of the sensor unit. For example, one or more additional sensors may be combined with another wearable device that may or may not include a sensor processor. It will also be understood that in some embodiments, a sensor unit may not include a sensor processor 104 or that the sensor processor 104 may have limited functionality (e.g., obtaining and transmitting sensor readings without (or with) limited analysis of the sensor readings).

[0024] In FIG. 1, solid lines indicate communication between components in at least some embodiments of the system. Dotted lines indicate alternative or additional modes of communication between components. In addition to the communication shown in FIG. 1, in at least some embodiments, the sensor processor 104 or the sensor 102 may also communicate directly with the clinician device. The communication may include, but is not limited to, wireless communication, wired communication, optical communication, ultrasonic communication, or combinations thereof. Satellite communication, cellular communication, Bluetooth™, Near Field Communications (NFC), Infrared Data Association standard (IrDA), Wireless Fidelity (WiFi), and Worldwide Interoperability for Microwave Access (WiMAX) are non-limiting examples of wireless communication that may be used for communication. Ethernet, Digital Subscriber Line (DSL), Fiber to the Home (FTTH), and Plain Old Telephone Service (POTS) are non-limiting examples of wired networks that may be used for communication.

[0025] The network 60 may be any suitable type of network, including, but not limited to, a personal area network (PAN), a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), the Internet, or any combination thereof. In at least some embodiments, the network 60 may be bypassed to provide direct connections between components. It will be appreciated that other devices, such as servers or server farms, memory storage devices, etc., may be connected to the patient device 106 or clinician device 108 by the network 60 or directly. For example, a server may be coupled to the patient device 106 or clinician device 108 that stores patient information or other medical information, applications, user interfaces, web interfaces, etc. for access by the patient device 106 or clinician device 108.

[0026] The patient device 106 and the clinician device 108 may be any suitable device, such as, for example, a computer (e.g., a notebook computer, a mobile medical station or computer, a server, a mainframe computer, or a desktop computer), a mobile device (e.g., a cellular phone or smartphone, a personal digital assistant, or a tablet), or any other suitable device. In at least some embodiments, the clinician device 108 may be incorporated into a medical station or system.

[0027] 2 illustrates one embodiment of a computing device 201 for use as a patient device 106 or a clinician device 108. The computing device 201 includes a processor 214, a memory 216, a display 218, and an input device 220. The computing device 201 may be local to a user or may include components that are non-local to the computer, including one or both of the processor 214 or the memory 216 (or portions thereof). For example, in some embodiments, a user may operate a terminal connected to a non-local processor or memory.

[0028] The computing device 201 may utilize any suitable processor 214, including one or more hardware processors that may be local to a user or may be non-local to a user, or other components of a computing device. The processor 214 is configured to execute instructions provided to the processor. Such instructions may include any of the steps of the methods or processes described herein.

[0029] Any suitable memory 216 may be used for the computing device 214. The memory 216 represents one type of computer-readable medium, namely, computer-readable storage media. Computer-readable storage media may include, but is not limited to, non-volatile computer-readable storage media, non-transitory computer-readable storage media, removable computer-readable storage media, and non-removable computer-readable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computing device.

[0030] The communication method provides another type of computer-readable medium, namely, communication media. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, data signal, or other transport mechanism, and includes any information delivery media. The terms "modulated data signal" and "carrier wave signal" include a signal that has one or more of its characteristics set or changed in such a manner as to encode information, instructions, data, etc., in the signal. By way of example, communication media includes wired media, such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media, and wireless media, such as acoustic, radio frequency (RF), infrared, Bluetooth™, short range communications, and other wireless media.

[0031] The display 218 may be any suitable display device, such as a monitor, screen, display, etc., and may include a printer. The input device 220 may be, for example, a keyboard, a mouse, a touch screen, a track ball, a joystick, a voice recognition system, a camera, a microphone, or any combination thereof.

[0032] Returning to FIG. 1 , the sensor processor 104 may be any suitable processor, including one or more hardware processors. The sensor processor 104 is configured to execute instructions provided to the processor. The sensor processor 104 is configured to receive sensor data from the sensors and communicate with the patient device 106, the network 60, the clinician device 108, or any combination thereof. Optionally, the sensor processor 104 may also process or analyze the sensor data and may have instructions stored thereon for performing such processing or analysis, including, for example, instructions for performing any of the steps of the processing or analysis described herein. In at least some embodiments, one or more of the sensors 102 may each include a processor that performs some or all of the functions of the sensor processor 104.

[0033] Any suitable type of sensor 102 may be used, including, but not limited to, an accelerometer, a magnetometer, a gyroscope, a proximity sensor, an infrared sensor, an ultrasonic sensor, a thermistor or other temperature sensor, a camera, a piezoelectric sensor or other pressure sensor, a sonar sensor, an external fluid sensor, a skin color change sensor, a pH sensor, a microphone, a smoke sensor or a nicotine sensor, or the like, or any combination thereof. In at least some embodiments, the system 100 includes at least 1, 2, 3, 4, 5, 6, or more different types of sensors 102. The system may include at least 1, 2, 3, 4, 5, 6, 8, 10, or more sensors 102. Further examples of suitable sensors and their configurations and uses are described in U.S. Patent Application Publication Nos. 2016 / 0302721, 2016 / 0310066, 2017 / 0143261, 2017 / 0147789, and 2017 / 0181698, all of which are incorporated by reference herein.

[0034] One or more sensors 102 may be used to measure, monitor, or otherwise observe a patient's physical activity or health, recovery from surgery or other medical treatment, healing after injury, surgery, or treatment, a patient's physical therapy or rehabilitation after injury, surgery, or treatment, or patient activity, or any combination thereof. Step count, walking speed (velocity), walking distance, sagittal posture, coronal posture, exercise repetition or regimen, movement speed, skin temperature, pulse or pulse profile or heart rate recovery time after activity, sleep profile or rest duration, gait analysis, body / limb / joint alignment, heart rate, oxygen level, etc. are examples of observations or measurements that may be made or interpreted using one or more of the sensors. The system 100 may observe or measure one or more of these items or any combination of items.

[0035] 3 shows one embodiment of a sensor unit 358 that may be attached to or otherwise placed adjacent to a patient's skin. The sensor unit includes a housing 360, a base 370 (or other skin attachment configuration) with a receiving shell 369 for attachment of the housing, and an adhesive pad 368 for attaching the base to the patient's skin. Within the housing 360, the sensor unit 358 includes one or more sensors 302, a power source 362, a communication unit 366, and a sensor processor 304.

[0036] Housing 360 may be manufactured from any suitable material, such as a plastic material (e.g., silicone), and is preferably flexible enough to conform to the anatomical contours of the patient's skin and to bend as the patient moves. In at least some embodiments, housing 360 is also water resistant to withstand ingress of sweat, rain, and other fluids to the inside of the housing. In at least some embodiments, housing 360 is sufficiently water resistant to allow a patient to shower while sensor unit 358 is attached to the patient's skin without covering the sensor unit. In some embodiments, housing 360 is sufficiently water resistant to allow a patient to bathe or swim without covering sensor unit 358. In at least some embodiments, housing 360 may include tabs 371 that can facilitate removal of housing 360 from base 370.

[0037] In at least some embodiments, the housing 360 has shapes or indicia thereon that visually indicate or suggest the orientation of the device when the housing is attached to the patient. In the embodiment shown, one end of the device is narrower than the other end, which indicates or suggests to the user that the narrow end is pointed down toward the leg.

[0038] The sensors 302, power supply 362, communication unit 366, and sensor processor 304 may be disposed within the housing 360. In some embodiments, a portion of one or more of the sensors, such as a temperature sensor, a pulse sensor, or a pressure sensor, a moisture sensor, a strain gauge, etc., may extend into the housing to provide contact with the skin or access to the patient without an intervening portion of the housing 360 or other components of the sensor unit 358. In some embodiments of the sensor unit 358, the sensors may include an accelerometer and a gyroscope. The accelerometer and gyroscope may be used to measure steps, walking speed (velocity), walking distance, sagittal posture, coronal posture, etc. Other suitable sensors include, but are not limited to, a microphone, a pulse oximetry sensor, a heart rate monitor, etc., or any combination thereof. As will be appreciated, any suitable sensor described above may be included in the sensor unit, and any combination of those sensors may be used in the sensor unit.

[0039] Power may be provided to the sensor 302 and sensor processor 304 using any suitable power source 362, including, but not limited to, a primary battery, a button cell battery, a rechargeable battery, a storage capacitor, other power storage device, or the like, or any combination thereof. In some embodiments, power may be provided by a kinetic energy power source that utilizes the patient's body movement to generate power for the components or to charge a battery or storage capacitor or other power storage device coupled to the components. In some embodiments, a wireless power source may be used instead of (or in addition to) a battery, storage capacitor, or other power storage device.

[0040] Additionally, a charging port may be provided for charging a battery or storage capacitor or other power storage device from a source such as an electrical outlet. Alternatively or additionally, wireless charging systems and methods may also be used. It will be appreciated that in some embodiments there may be multiple ways to power a component or a power storage device associated with a component. All sensors and sensor processors may be coupled to the same power source, or some (or even all) of the sensors and sensor processor may have individual power sources.

[0041] In at least some embodiments, power source 362 may be a primary battery and may have an expected life under normal use of at least 1 week, 2 weeks, or 4 weeks, or at least 1 month, 2 months, 3 months, 4 months, 6 months, 8 months, 10 months, 12 months, 15 months, 18 months, 24 months, or more. In at least some embodiments, power source 362 is rechargeable, for example, using a recharging port in sensor unit 358, or may be wirelessly charged, such as with an inductive charging device (such as an inductive mat or sleeve) or using WiFi or ultrasonic charging as described above. (In some embodiments, the recharging device may be connected to a network to take data or measurements or signals from the sensor unit and transmit them to a patient device, clinician device, or other device for use or storage, etc.) Power may be provided to the device by energy harvesting means, such as using a cantilevered piezo reed, a generator and pendulum mechanism, a passive magnet that rotates / slides / swings through or by a coil, etc., to convert an amount of kinetic energy into electrical energy to be used by the device. The power source 362 provides power to the sensor 302, the communication unit 366, the sensor processor 304, and any other components in the sensor unit.

[0042] In at least some embodiments, the sensor 302 and the sensor processor 304 may be constantly active to measure, monitor, or otherwise observe. In other embodiments, one or more of the sensor 302 and the sensor processor 304 may be periodically (e.g., every 15 or 30 seconds, or every 1, 5, 10, 15, or 30 minutes, or every 1, 2, 3, 4, 6, 7, or 24 hours) or irregularly active to measure, monitor, or otherwise observe. Optionally, the period may be programmable. Furthermore, the period may be optionally changed based on data from one or more of the sensors. In still other embodiments, one or more of the sensor 302 and the sensor processor 304 may be manually or automatically activated by a sensor module, a patient device, a clinician device, or other device. In at least some embodiments, the sensors and optional sensor processor may have different activation schedules (continuous, periodic, irregular, or manual). For example, a sensor for measuring temperature may do so periodically, a sensor for measuring steps or movement may be continuous, and a sensor for measuring posture may be manually activated by a wearable device, a patient device or a clinician device.

[0043] The illustrated embodiment also employs a power light 372 that illuminates when the sensor unit 358 is functioning to assure the patient that the device is working. In some embodiments, the power light 372 may also flash or change color to indicate device functionality, such as low battery power, pairing with another device (e.g., the patient device 106, clinician device 108, or network 110 of FIG. 1), actively taking readings using one or more of the sensors (especially in the case of manually or periodically activated sensors), notifying the patient that it is time to do an exercise, change adhesive, etc.

[0044] In at least some embodiments, the housing 360 can include a power button (not shown) that can be activated to turn the device on and, optionally, to turn the device off. In at least some embodiments, the power button can also be activated to manually command one or more of the sensors to take a reading. In at least some embodiments, the sensor unit 358 can be turned on or off using a patient device or a clinician device.

[0045] In other embodiments, the housing 360 may include a sensor, such as a color sensor or a proximity sensor, that can detect when the housing 360 is attached to the base 370 to turn on the sensor unit 358 when attached or to turn off the sensor unit 358 when not attached. For example, the sensor may detect the color of the base or detect the proximity of the base to turn on the sensor unit 358. Alternatively, when the housing 360 is attached to the base 370, the sensor unit 358 may enter an activatable state such that the sensor unit 358 may be turned on manually or using a device, such as a patient device or a clinician device.

[0046] The base 370 is designed to hold the sensor unit 358 on the patient's skin. The adhesive pad 368 of the base 370 can have a substrate with adhesive on both sides of the substrate, such that one side can be attached to the patient's skin and the other side can be attached to the housing 360. In at least some embodiments, the base 370 can be replaced periodically (e.g., every 1, 2, 5, or 3 days, or every 2, 3, 4, or more weeks) because the adhesive adjacent to the patient's skin or the housing 360 can deteriorate or otherwise lose some or all of the adhesive's adhesive properties. Preferably, at least the adhesive for attachment to the patient's skin is selected to prevent or reduce the risk of skin irritation. Preferably, the adhesive on both sides of the substrate is selected to be water resistant and less likely to lose adhesion due to contact with sweat. In at least some embodiments, base 370 extends around the circumference of sensor unit 358 and optionally includes one or more openings that allow housing 360 to contact the patient's skin or access the patient without an intervening portion of base 370.

[0047] In other embodiments, instead of base 370, an adhesive may be applied directly to the housing to adhere it directly to the skin. In other embodiments, instead of adhering the sensor unit to the skin, the sensor unit may be inserted into an article for the patient to wear to hold the sensor unit in place at a desired location on the body.

[0048] The sensor processor 304 may be any suitable processor and may include or be coupled to a memory unit for storing sensor data. The sensor processor 304 may be wired or wirelessly coupled to the sensor 302 to receive data from the sensor. In some embodiments, the sensor processor 304 may include analysis algorithms for analyzing or partially analyzing the sensor data. In other embodiments, the sensor processor 304 may be designed primarily to receive, store, and transmit the sensor data.

[0049] The communication unit 366 may be any suitable communication configuration capable of transmitting information from the sensor processor 304 or sensor 302 to another device (such as the patient device 106, clinician device 108, or network 110 of FIG. 1 ). The communication unit 366 may transmit this information by any suitable wired or wireless technique, including but not limited to Bluetooth™, near field communication, WiFi, infrared, radio frequency, acoustic, optical, or using a wired connection through a data port in the sensor unit, or any other communication technique presented herein.

[0050] In at least some embodiments, base 370 is sufficiently flexible for attachment to the patient's skin as the patient moves during normal activities or physical therapy exercises. Receiving shell 369 may be manufactured from any suitable material, including, but not limited to, flexible plastics, such as silicone or polyurethane. In at least some embodiments, receiving shell 369 removably grips housing 360 to provide additional retention of attachment of the housing to base 370. In at least some embodiments, receiving shell 369 is resiliently flexible to expand when a portion of housing 360 is received in the receiving shell and then compress against the perimeter of the received portion of the housing.

[0051] The adhesive pad 368 may be an adhesive applied to the receiving shell 369 or may be an adhesive on two sides of a substrate, one side of which is attached to the receiving shell 369. Preferably, the adhesive is selected to be water resistant and not prone to loss of adhesion due to contact with sweat. In at least some embodiments, the base 370 or the adhesive on the base is intended for use for at least 1, 2, 3, 5, 7, or 10 days or 2, 3, or 4 weeks or more under normal conditions of use before replacement or reapplication of the adhesive. In at least some embodiments, the adhesive is selected to maintain adhesion to the skin when the user showers. In at least some embodiments, the adhesive is selected to maintain adhesion to the skin when the user bathes, swims in a pool, or sits in a jacuzzi, hot tub, or rehabilitation pool.

[0052] The diagnosis, treatment, and management of musculoskeletal patients can be a challenge due, at least in part, to the lack of objective scoring systems based on meaningful criteria and the lack of access to meaningful real-time data streams. Traditional classification systems and constructs often rely on subjective data that has an influence based on the opinion of the health care provider or the patient, or any combination of individuals.

[0053] In at least some embodiments, the systems and methods can track, quantify, and qualify musculoskeletal movement in real time. In at least some embodiments, the systems and methods can facilitate measuring a user's health before surgery and during a rehabilitation period after surgery, as well as general monitoring after the rehabilitation period. In at least some embodiments, the systems and methods can allow a physician, clinician, user, or other individual to observe and examine a real-time patient (or user) data stream, providing objective data regarding a patient or user being diagnosed, evaluated for treatment, evaluated post-treatment, or otherwise observed.

[0054] The systems and methods include obtaining or determining one or more user characteristic or activity measurements. Examples include, but are not limited to, step count, walking speed (velocity), walking distance, sagittal plane posture, coronal plane posture, exercise repetition or regimen, etc., or any combination thereof. In at least some embodiments, one or more of these measurements may be performed continuously or over an extended period of time, such as, for example, measuring step count over a period of 1 hour, 2 hours, 4 hours, 6 hours, 8 hours, 12 hours, or 24 hours (or any shorter or longer). In at least some embodiments, one or more of the measurements may be performed when requested by a user, clinician, or other individual, such as, for example, walking speed (velocity), walking distance, sagittal plane posture, coronal plane posture, exercise repetition or regimen (which may include, for example, water activity, stair exercise regime, treadmill activity, or bicycle activity), etc., or any combination thereof. In at least some examples, one or more measurements may be taken periodically or irregularly, such as, for example, walking speed (velocity), walking distance, sagittal posture, coronal posture, fall prediction, pain score, wound photography, or the like, or any combination thereof. In some of these cases, the measurements may be performed only when the user is engaged in an activity, such as standing or walking. The system or method may include a determination that the user is engaged in an activity prior to taking the measurements.

[0055] The systems and methods described herein utilize models to characterize a user's health, treatment, rehabilitation, etc., including determining one or more model-based criteria from measurements. The systems, methods, and models may be used, for example, to generate objective measures of musculoskeletal movement, underlying disease or disorder conditions, treatment effectiveness, rehabilitation progress, improvement effectiveness, decline over time, or general (or musculoskeletal) health. The systems, methods, and models may also be used to monitor or classify musculoskeletal disorders, including, for example, classifying the severity of pain, injury, atrophy, injury, disease, or other problems.

[0056] The system and method can include one or more models that utilize personal characteristics and one or more measurements to determine one or more model-based criteria. Personal characteristics can include one or more of age, gender, height, weight, activity level, mobility level, body mass index (BMI), implant type, surgery, instrumentation, etc., or any combination thereof. Model-based criteria are determined using two or more of the measurements in a specified or predetermined combination. In at least some embodiments, the model-based criteria include a combination of multiple scores, each score being based on one or more of the measurements and can include determining where the measurement falls within a specified or predetermined range. In at least some embodiments, the specified or predetermined range for one or more of the scores (or the scores themselves) can depend on one or more of the personal characteristics. For example, the range of the measurement or the range of the score can vary based on the user's gender, age, or height (or any other personal characteristic, or combination of personal characteristics).

[0057] In at least some embodiments, the score is based on where the measurement falls within a predetermined range. As an example, the range can have a minimum value x0 (which can be 0) and a maximum value x1, and the maximum score can be represented as A. In some embodiments, the score S m for a given measurement x (falling within the range) m can be a linear function such that S m =A*(x m -x0) / (x1-x0). In at least some embodiments, when x m >x1, the score is S m =A, and when x m <x0, the score is S m =0. The score can also be a non-linear function of the measurement (e.g., exponential, logarithmic, step, multi-step, trigonometric, or other function), or a composite function having multiple ranges where each range has a linear or non-linear relationship to the measurement.

[0058] The model-based criterion may be determined from one or more (preferably two or more) of the scores. In at least some embodiments, the criterion M may be a weighted combination (using weights w1, w2, ...) of the scores S1, S2, ... such that M = w1 * S1 + w2 * S2 + .... In some embodiments, the weights may all be equal to each other. In other embodiments, the weights may be different for two or more of the scores. In at least some embodiments, instead of (or in addition to) using weights, the maximum value for each of the scores may be different from one or more of the other scores. It will be understood that in all of the examples described herein, the variation in the maximum score may be used as an alternative to using weights for the different scores.

[0059] Each of the model-based criteria or models associated with the criteria may differ from other criteria or models by changing one or more of the selection of measurements, the selection of ranges, the form of the score equation (e.g., linear, non-linear, etc.), the weights for different scores, etc. For example, one model or criterion may be generated for men between 60 and 70 years old, and a different model or criterion may be generated for middle-aged women, by changing one or more of the ranges for the measurements used to generate the scores. Thus, the present system and method may utilize a number of different models or criteria that depend on personal characteristics. Additionally, other models or criteria may differ for reasons other than personal characteristics. For example, the score weights may differ between two or more models or criteria to place different emphasis on individual measurements.

[0060] In at least some embodiments, the system may automatically select one of the models or criteria based on input data (such as one or more of the features). In at least some embodiments, the system may allow the user or clinician to select a model or criteria. In at least some embodiments, the system may select or use a default model or criteria, but may also allow the user or clinician to select a different model or criteria. In at least some embodiments, the system may automatically select a model or criteria, or a family of models or criteria (e.g., select a model or criteria, or a family of models or criteria based on input of the user's personal characteristics), and then allow the user or clinician to modify or select within the family (e.g., selecting models or criteria has different weights for the score).

[0061] 13 illustrates one embodiment of a method for monitoring a user. In step 1302, signals are obtained from one or more sensors. In some embodiments, the one or more sensors may all be on the sensor unit as described above. In other embodiments, one or more of the sensors may be on the sensor unit and one or more of the sensors may be separate from the sensor unit.

[0062] In step 1304, measurements are determined from the signals. Examples of measurements include, but are not limited to, number of steps, walking speed (velocity), walking distance, sagittal posture, coronal posture, exercise repetition or regimen (which may include, for example, water activity, stair exercise regimen, treadmill activity, or bicycle activity), and the like, or any combination thereof. In some examples, one or more of the measurements may be obtained directly from the signals. In some examples, one or more of the measurements may include analysis or processing of the signals to determine the measurements.

[0063] In step 1306, a criterion is determined based on the measurements. Examples of criteria are provided below and include, but are not limited to, gait posture index (GPi), fall prediction score (FPs), objective claudicant score (OCs), health gait index (HGi), or the like, or any combination thereof.

[0064] In optional step 1308, the system or user may perform an action in response to determining the criteria. For example, the system or user may provide an alert to the user or clinician (e.g., via a sensor unit, patient device, or clinician device) if the criteria meets a threshold criterion, the system or user may modify (or recommend a modification to) a treatment, physical therapy regimen, or exercise regimen based on the criteria, the system or user may provide a preliminary or final diagnosis based on the criteria, etc., or any combination thereof.

[0065] As an example, in at least some embodiments, the system or method is used to determine a Gait Posture Index (GPi). In at least some embodiments, the GPi is a measure that combines scores for measurements of step count, walking speed, stride length, and posture into a composite measure representative of health and mobility. (It will be appreciated that this is just one example, and that the GPi may be generated using a different selection of measurements.) As one example, the GPi may have a rating from 0 to 100, with a maximum score of 25 assigned to each of the following four categories: step count, walking speed, stride length, and posture. In this example, GPi=25*(sc m -sc0) / (sc1-sc0)+25*(gv m -gv0) / (gv1-gv0)+25*(sd m -sd0) / (sd1-sd0)+25*(p m-p0) / (p1-p0), where the subscript m refers to the value of the measurement, the subscript 0 indicates the lower limit of the range of the measurement, the subscript 1 is the upper limit of the range of the measurement, sc = number of steps, gv = walking speed, sd = stride length, and p = posture.

[0066] As another example, the maximum score values ​​may be asymmetric with a maximum score of 20 points for step count, walking speed, and stride length, respectively, and a maximum score of 40 points for posture. In this case, GPi=20*(sc m -sc0) / (sc1-sc0)+20*(gv m -gv0) / (gv1-gv0)+20*(sd m -sd0) / (sd1-sd0)+40*(p m -p0) / (p1-p0).

[0067] In at least some embodiments, the score for step count is calculated by calculating the step count sc over a time period (such as daily or over 1 hour, 2 hours, 3 hours, 4 hours, 6 hours, or 12 hours, or over 1 minute, 5 minutes, 10 minutes, 15 minutes, 20 minutes, or any other suitable time period). m A range for the score may be, for example, sc0=0 steps and sc1=10,0000 steps (any other suitable number of steps may be used, depending on the length of time over which the steps are counted). As one example, the score is determined based on the proportion of daily steps over 10,000 steps. If the number of steps is equal to or greater than the top value of the range (e.g., 10,000 steps), a full score is assigned.

[0068] In at least some embodiments, a score for walking speed may be allocated in the range of gv0=20 cm / s to gv1=160 cm / s (or any other suitable range) based on a comparison of one or more measurements by the system. If the walking speed is equal to or greater than gv1 (e.g., 160 cm / s), a full score is awarded. If the walking speed is less than gv0 (e.g., 20 cm / s), a score of 0 is awarded.

[0069] In at least some embodiments, a score for stride length (or stride length) may be allocated in the range sd0=0 to sd1=80 cm based on a comparison of one or more measurements by the system. If the stride length is equal to or greater than sd1 (e.g., 80 cm), a full score is awarded. In at least some embodiments, this range may be modified based on the patient's height or leg length.

[0070] In at least some embodiments, a score for posture may be assigned based on a comparison of one or more measurements of the user's upper torso flexion angle by the system, ranging from p0=30 degrees to p1=0 degrees. If the upper torso angle is equal to or greater than sd0 (e.g., 30 degrees), a score of 0 points is assigned. In at least some embodiments, posture may include both sagittal (front-to-back) and coronal (side-to-side) measurements.

[0071] Walking is a fundamental part of life, and its importance is not limited by age, race, or medical condition. The ability to walk, and the relative ability to walk, can affect an individual's social participation and engagement. Walking measurements can often be a relatively accurate assessment of health. A decline in walking measurements can be associated with a decline in health and can be highly correlated with poor health outcomes. The GPi scores described herein can be used for a variety of applications, including, but not limited to, monitoring health conditions, aiding in diagnosis, monitoring recovery after surgical intervention or other treatment, guiding rehabilitation, and the like, or any combination thereof. In at least some embodiments, the GPi scores are particularly useful for assessing or monitoring musculoskeletal and neurological problems, since these problems tend to affect mobility as a causative event.

[0072] In at least some embodiments, the ranges for the different measurements may be modified for age, sex, height, or other personal characteristics, or any combination thereof. The range of the final GPi scale in the example given above is 0-100, with 0 indicating very poor health (possibly bedridden and non-ambulatory) and 100 indicating good health. For example, a healthy 85-year-old who walks with an upright neutral posture, good speed and stride length, has a high daily step count, and is always active, would have a GPi in the range of 90-100 in this example. A long-lasting GPi score of 0 may correlate with end-stage health. A score of 100 correlates to a healthy individual who walks in an upright posture ("balanced") without lateral trunk deviation or stoop / kyphosis, with a minimum of 10,000 steps / day, a gait velocity (GV) within the normal range for their age, and a mean stride length (MSL) within the normal range for their height. This scoring structure may not be appropriate for paraplegic or quadriplegic individuals and may be modified for people using walking aids, for example a patient with a single upper limb walking aid could subtract 5 from the criterion, or a patient with two walking aids or a forearm support frame could subtract 10 from the criterion.

[0073] Another measure is the cone of balance score (CBs). CBs can combine measurements of gait, optionally in combination with measurements of coronal and sagittal position. Higher scores indicate more time spent in neutral position and lower scores indicate more time spent with gait deformation.

[0074] In at least some embodiments, a score of 0 is perfect posture. A drop off the cone of balance or a high score may put the patient at high risk for a fall accident. A low score indicates more time spent in a neutral position and a high score indicates more time spent with a gait variation. As another example of scoring (which may be used to provide the postural component of the GPi criterion), an average or measured postural angle from a straight line of 0 degrees or 1 degree will give a score of 25 out of 25. An average or measured postural angle of 2 degrees to 5 degrees will give a score of 20. An average or measured postural angle of 6 degrees to 10 degrees will give a score of 15. An average or measured postural angle of 11 degrees to 15 degrees will give a score of 10. An average or measured postural angle of 16 degrees or more will give a score of 5. FIG. 14 shows an interface (described in more detail below) showing a slouching posture 10 degrees to the right and forward, which would provide a daily posture score of 15 out of 25 for the overall posture score component of the GPi formula.

[0075] Another example of a metric is the Healthy Walking Index (HGi). In at least some embodiments, HGi is a metric that combines scores for measurements of heart rate, walking speed, sleep cycle, steps or activity, and posture (or cone of balance (CB)) into a composite metric that represents health and mobility. As one example, HGi can have a rating from 0 to 100, with a maximum score of 20 assigned to each of the following five categories: heart rate, walking speed, sleep cycle, steps or activity, and posture (or cone of balance (CB)). In this example, HGi=20*(hr m -hr0) / (hr1-hr0)+20*(gv m -gv0) / (gv1-gv0)+20*(sc m -sc0) / (sc1-sc0)+20*(sl m -sl0) / (sl1-sl0)+20*(p m-p0) / (p1-p0), where subscript m refers to the value of the measurement, subscript 0 indicates the lower limit of the range of the measurement, subscript 1 is the upper limit of the range of the measurement, hr=heart rate, gv=gait speed, sc=step count (or measure other activity), sl=sleep cycle, and p=posture.

[0076] Another example of a criterion is the Objective Claudication Score (OCs). In at least some embodiments, OCs is a criterion that combines scores for measurements of gait speed, stride rate, sagittal balance, and walk time. As one example, OCs can have a rating from 0 to 100, with a maximum score of 25 assigned to each of the following four categories: gait speed, stride rate, sagittal balance, and walk time. In this example, OCs=25*(gv m -gv0) / (gv1-gv0)+25*(sc m -sc0) / (sc1-sc0)+25*(sb m -sb0) / (sb1-sb0)+25*(tw m -tw0) / (tw1-tw0), where subscript m refers to the value of the measurement, subscript 0 indicates the lower limit of the range of the measurement, subscript 1 indicates the upper limit of the range of the measurement, gv=gait speed, sc=step count, sb=sagittal balance, and tw=walking time. In at least some embodiments, an OCs of 0 indicates no claudication symptoms and an OCs of 100 indicates severe claudication symptoms.

[0077] A further example of a criterion is the Fall Prediction Score (FPs). In at least some embodiments, FPs is a criterion that combines scores for measurements of walking speed and postural deviation (from 0 degrees relative to perfect posture), which may also be described as swaying or wobbling. As one example, FPs may have a rating from 0 to 100, with a maximum score of 50 assigned to each of two categories: walking speed and postural deviation (e.g., swaying / wobbling). In this example, instead of using a formula, scores are assigned to different ranges for each category. For example, in at least some embodiments, for walking speed, a walking speed of 0-0.6 m / s is assigned a score of 15, a walking speed of 0.6-0.90 m / s is assigned a score of 30, a walking speed of 0.91-1.0 m / s is assigned a score of 40, and a walking speed of greater than 1.1 up to and including 1.2 m / s is assigned a score of 50. For postural deviations, a score of 15 is assigned for postural deviations / unsteadiness of 11-16 degrees or more from midline, a score of 30 is assigned for postural deviations / unsteadiness of 6-10 degrees from midline, a score of 40 is assigned for postural deviations / unsteadiness of 2-5 degrees, and a score of 50 is assigned for postural deviations / unsteadiness of 0-1 degree. The two scores are added to give FPs which are interpreted as 0-30=very high risk of falling, 31-49=moderate risk of falling, 50-70=low risk of falling, 71-90=very low risk of falling, and 91-100=negligible risk of falling.

[0078] In the present systems and methods, one or more (or even all) of the measurements used to generate the criteria described above may be made using the sensor unit 358 (FIG. 3) and its associated sensor 304. For example, an accelerometer in the sensor unit 358 may be used to measure step count, step rate, walking speed, stride length, walking time, posture, sagittal balance / posture, coronal balance / posture, fall prediction, etc., or any combination thereof. A gyroscope in the sensor unit 358 may be used to measure posture, sagittal balance / posture, coronal balance / posture, fall prediction, etc., or any combination thereof. The sensor unit may include one or more other sensors, such as, for example, a heart rate sensor (to measure heart rate or sleep cycles) or a temperature sensor. In at least some embodiments, the sensor unit 358 may include a smoke sensor or a nicotine sensor to determine whether the user smokes (and, optionally, how much) or whether the user is exposed to substantial secondhand smoke.

[0079] The patient device 106 (FIG. 1) or clinician device 108 (or both) may include an application that communicates with a sensor unit 358 (FIG. 3) via a wired or wireless connection. The sensor unit 358 may collect or determine measurements, which may include, for example, step count, walking speed (velocity), walking distance, sagittal posture, coronal posture, fall prediction, etc., or any combination thereof, and may communicate those measurements to the patient device 106 or clinician device 108 (or both).

[0080] The application on the patient device 106 may provide the patient with information about the measurements (e.g., a list of measurements, a graph of the measurements, averages or daily counts for the measurements, etc., or any combination thereof) as well as any of the metrics described above (e.g., including a graph or list associated with the metrics). The application may allow the patient to view and update the progress of their rehabilitation or their physical therapy or exercise. The application may give the patient access to some or all profile details and may allow access to sensor unit setup and calibration applications or protocols.

[0081] An application on the clinician device 108 may enable a clinician to connect to the sensor unit during a clinic visit, such as before or after a diagnostic, rehabilitation treatment, or surgical intervention is performed. The clinician device 108 may enable a clinician to monitor patient recovery. The clinician device may enable a clinician to perform in-clinic measurements that may be used to generate any of the metrics, such as the GPi.

[0082] Other applications that may be provided on the clinician device 108 or other devices may include an account manager, a patient profile manager, etc. This application may also allow management of settings for applications on the patient or clinician device.

[0083] 4-12 show screenshots or interfaces of an example application or user interface for a patient device or clinician device. The user application interfaces shown are particularly useful for mobile devices such as smartphones or tablets, but may also be used with other devices, such as desktop or laptop computers.

[0084] FIG. 4 shows one embodiment of a user interface 460 in which a current value of a measurement is presented, for example, in region 462. The user interface also presents one or more determined criteria, such as GPi, in region 464. In region 466, a graph of one or more measurements is presented and may include information about the measurements over time. In FIG. 4, the graph is a graph of sagittal and coronal postures for a day (or other time period) indicating the frequency of that measurement over that time period by color, brightness, etc. This particular graph also includes an indication of a possible condition (e.g., kyphosis or lordosis) based on the measurements.

[0085] 5-7 show graphs 463 of measured (e.g., weekly or daily) values ​​(FIGS. 5 and 7) or baseline (FIG. 6) over a period of time. In the illustrated embodiment, coronal and sagittal posture graphs are created with each point corresponding to a weekly measurement (or average of measurements over the week). This allows a user (e.g., patient or clinician) to monitor changes (e.g., improvement or deterioration) over time.

[0086] 8-12 diagrammatically illustrate motion targets for movement, range of motion measurement, or posture assessment. In each of these figures, a human icon 465 is presented and may be adjusted based on the sensor unit measurements to represent the user's current movement, position, or posture. Additionally, for the range of movement or motion measurement, range increments 467a, 467b, 467c, 467d, 467e are presented. In some embodiments, at least the range increments 467a, 467b in Figs. 8 and 9 may also be presented for posture assessment. A current position 469 as well as a number of movement iterations 470 may be presented. The range increments 467a, 467b, 467c, 467d, 467e may have different colors to represent approaching the target (e.g., range increments 467b, 467d) or achieving the target (e.g., range increments 467a, 467e). These may relate to colors in range increments or on the background of the interface, or encouraging messages to "keep going" or affirmations of "great rating!", or other messages.

[0087] The methods and systems described herein may be embodied in many different forms and should not be construed as limited to the embodiments described herein. Thus, the methods and systems described herein may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. The systems referred to herein generally include a memory and generally include a method for communication with other devices, including mobile devices. The method of communication may include both wired and wireless (e.g., radio frequency (RF), optical, or infrared) communication methods, such methods providing another type of computer-readable medium, i.e., a communication medium. Wired communication may include communication via twisted pair, coaxial cable, fiber optics, wave guide, or the like, or any combination thereof. Wireless communication may include radio frequency (RF), infrared, acoustic, near field communication, Bluetooth™, or the like, or any combination thereof.

[0088] It will be understood that each block of the flowchart diagrams, and combinations of blocks in the flowchart diagrams and methods disclosed herein, may be implemented by computer program instructions. These program instructions may be provided to a processor to generate a machine such that the instructions, which execute on the processor, create means for implementing the acts specified in one or more of the flowchart blocks disclosed herein. The computer program instructions may be executed by a processor such that a series of operational steps are performed by the processor to generate a computer-implemented process. The computer program instructions may also cause at least some of the operational steps to be performed in parallel. Moreover, some of the steps may also be performed across two or more processors, such as may occur in a multi-processor computer system. Furthermore, one or more processes may also be performed simultaneously with other processes, or even in a different order than that shown, without departing from the spirit or scope of the present invention.

[0089] The computer program instructions may be stored on any suitable computer readable medium, including, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk ("DVD") or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store the desired information and that may be accessed by a computer.

[0090] The above specification provides a description of how to make and use the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention also resides in the claims hereinafter appended.

Claims

1. 1. A system for monitoring a user, comprising: a sensor unit configured and arranged to be disposed on the user, the sensor unit comprising an accelerometer and a communication arrangement; At least one processor; Equipped with The at least one processor: receiving a signal from the accelerometer of the sensor unit; determining a number of steps from the signal; determining a walking speed from said signal; determining a stride length from said signal; determining a posture of the user from the signals, where determining the posture of the user includes making one or more measurements of an angle of flexion of the user's upper torso; determining a walking posture index according to the number of steps, the walking speed, the stride length, and the posture; 16. A system for monitoring a user, the system being constructed and arranged to perform acts including:

2. The system of claim 1 , wherein the at least one processor comprises a sensor processor disposed in the sensor unit.

3. The system of claim 1 , further comprising a patient device comprising a display and a communication arrangement configured for communication with the sensor unit.

4. 4. The system of claim 3, wherein the at least one processor comprises a sensor processor disposed in the sensor unit and a patient device processor disposed in the patient device, and the action further comprises displaying the walking posture index on the display of the patient device.

5. The system of claim 1 , further comprising a clinician device comprising a display and a communication arrangement configured for communication with the sensor unit.

6. 6. The system of claim 5, wherein the at least one processor comprises a sensor processor disposed in the sensor unit and a clinician device processor disposed in the clinician device, and the action further comprises displaying the gait posture index on the display of the clinician device.

7. 2. The system of claim 1, wherein determining the walking posture index comprises using measurements to determine a score for each of the step count, the walking speed, the stride length, and the posture, and combining the scores to obtain the walking posture index.

8. The system of claim 7 , wherein combining the scores comprises weighting the scores and adding the weighted scores together to determine the gait posture index.

9. 8. The system of claim 7, wherein determining the score comprises determining the score for one of the measurements by determining a location of the measurement between a minimum and a maximum value for the measurement.

10. 2. The system of claim 1, wherein the actions further include performing an action selected from: providing an alert to the user or clinician if the walking posture index meets a threshold criterion; modifying a therapy, physical therapy regimen, or exercise regimen based on the walking posture index; recommending a modification of a therapy, physical therapy regimen, or exercise regimen based on the walking posture index; or providing a preliminary or final diagnosis based on the walking posture index.

11. The act determining an attitude deviation from said signal; determining a fall prediction score according to the walking speed and the posture deviation; The system of claim 1 further comprising:

12. The system of claim 1, wherein determining the posture of the user includes determining a sagittal plane posture of the user.

13. The system of claim 1, wherein determining the posture of the user includes determining a coronal posture of the user.

14. The system of claim 1, wherein determining the posture of the user includes determining both a sagittal posture of the user and a coronal posture of the user.

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