Automatic detection of body rotation planes

Abstract

Disclosed herein is a technology for automatically calibrating a reference orientation of an implantable medical device (IMD) within a patient. In one example, a sensor of an IMD senses multiple orientation vectors of the IMD relative to a gravitational field. A processing circuit system of the IMD processes the multiple orientation vectors to identify an upright vector corresponding to an upright posture of the patient. The processing circuit system classifies the multiple orientation vectors relative to the upright vector to define a sagittal plane of the patient and a transverse plane of the patient. The processing circuit system determines a reference orientation of the IMD within the patient based on the upright vector, the sagittal plane, and the transverse plane. When the orientation of the IMD within the patient changes over time, the processing circuit system can recalibrate its reference orientation and accurately detect the patient's posture.

Description

Technical Field

[0001] The present disclosure relates to body posture and body part orientation monitoring in medical devices. Background Art

[0002] Medical devices designed to be worn by or implanted in a patient can automatically monitor various physiological variables within the patient. In some instances, medical devices can monitor physiological variables such as blood pressure or heart rate over time to provide caregivers with trend information that can be used to understand the patient's health status and provide physicians with evidence regarding disease progression or resolution. In other instances, medical devices can automatically adjust treatment based on the measured patient parameters.

[0003] Medical devices containing accelerometers can monitor patient parameters such as body posture, device orientation, activity level, heart rate, heart motion, and sounds, including heart sounds, snoring, the patient's voice, and sounds in the patient's environment. Posture sensing can be used to provide posture trends, such as trends in sleep orientation, which can help patients, caregivers, and physicians understand and track trends or sudden changes in daily living activities, such as sleep duration, sleep quality, and activity levels. Summary of the Invention

[0004] In general, the present disclosure describes techniques for automatically detecting the orientation of an implantable medical device (IMD) within a patient. When an IMD is implanted in a patient, a clinician can calibrate a reference orientation of the IMD relative to a given body posture. Subsequently, as the patient changes body posture, the orientation of the device may change. The IMD can detect such changes in device orientation and use this information to determine changes in the posture of the patient's body to monitor patient parameters or control the delivery of therapy. However, over time, the IMD may shift or rotate within the patient's body. This can cause errors in the posture detection operations performed by the IMD because the reference point used by the IMD can remain valid as long as the relative position of the IMD with respect to the patient remains stable. Therefore, over time, the accumulated shifts, rotations, or other movements of the IMD may require recalibration to account for the new position and / or orientation relative to the patient's body.

[0005] This document describes techniques that allow an IMD to automatically recalibrate a reference orientation and perform posture detection even when the position and / or orientation of the IMD changes within the patient's body. For example, one or more sensors of the IMD sense multiple orientation vectors of the IMD relative to a gravitational field. The IMD's processing circuitry (or another computing device in communication with the IMD) processes the multiple orientation vectors to identify an upright vector from the multiple orientation vectors. For example, by assuming that the patient is in an upright position most of the time and / or during daytime, it can be assumed that the average vector of the multiple orientation vectors corresponds to the patient's upright posture. The processing circuitry classifies the multiple orientation vectors relative to the upright vector to define a sagittal plane for the patient and a transverse plane for the patient. The processing circuitry determines the orientation of the IMD within the patient based on the upright vector, the sagittal plane, and the transverse plane. Furthermore, the processing circuitry can use the orientation of the IMD within the patient to determine the patient's current posture. Thus, even when the position and / or orientation of the IMD within the patient's body changes over time, an IMD according to the techniques of this disclosure can recalibrate its current orientation and accurately detect the patient's posture. Such an IMD may reduce or eliminate the need for a clinician to periodically and manually recalibrate the orientation of the IMD relative to the patient.

[0006] In one example, the present disclosure describes a method comprising: sensing, by one or more sensors of an implantable medical device (IMD), a plurality of orientation vectors of the IMD relative to a gravitational field; processing, by a processing circuit system of the IMD, the plurality of orientation vectors to identify an upright vector among the plurality of orientation vectors, the upright vector corresponding to an upright posture of a patient; classifying, by the processing circuit system, the plurality of orientation vectors relative to the upright vector to define a transverse plane of the patient; classifying, by the processing circuit system, the plurality of orientation vectors relative to the upright vector to define a sagittal plane of the patient; and determining, by the processing circuit system and based on the upright vector, the transverse plane, and the sagittal plane, a reference orientation of the IMD.

[0007] In another example, the present disclosure describes an implantable medical device (IMD) comprising: one or more sensors configured to sense multiple orientation vectors of the IMD relative to a gravitational field; and a processing circuit system configured to: process the multiple orientation vectors to identify an upright vector among the multiple orientation vectors, the upright vector corresponding to an upright posture of a patient; classify the multiple orientation vectors relative to the upright vector to define a transverse plane of the patient; classify the multiple orientation vectors relative to the upright vector to define a sagittal plane of the patient; and determine a reference orientation of the IMD based on the upright vector, the transverse plane, and the sagittal plane.

[0008] In another example, the present disclosure describes a non-transitory computer-readable medium comprising instructions that, when executed, are configured to cause a processing circuit system of an implantable medical device (IMD) to: receive a plurality of orientation vectors of the IMD sensed relative to a gravitational field from one or more sensors; and process the plurality of orientation vectors to identify an upright vector among the plurality of orientation vectors, the upright vector corresponding to an upright posture of the patient; classify the plurality of orientation vectors relative to the upright vector to define a transverse plane of the patient; classify the plurality of orientation vectors relative to the upright vector to define a sagittal plane of the patient; and determine a reference orientation of the IMD based on the upright vector, the transverse plane, and the sagittal plane.

[0009] In another example, the present disclosure describes a system comprising: one or more sensors configured to sense multiple orientation vectors of an implantable medical device (IMD) relative to a gravitational field; and a processing circuit system configured to: process the multiple orientation vectors to identify an upright vector among the multiple orientation vectors, the upright vector corresponding to an upright posture of a patient; classify the multiple orientation vectors relative to the upright vector to define a transverse plane of the patient; classify the multiple orientation vectors relative to the upright vector to define a sagittal plane of the patient; and determine a reference orientation of the IMD based on the upright vector, the transverse plane, and the sagittal plane.

[0010] The details of one or more examples of the disclosed technology are set forth in the accompanying drawings and the following description. Other features, objectives, and advantages of the technology will be apparent from the description and drawings, as well as from items 1-11. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1is a conceptual diagram illustrating an example medical device system configured to automatically detect the orientation of a medical device in accordance with one or more techniques of this disclosure.

[0012] Figure 2 is configured to automatically detect according to one or more techniques of this disclosure Figure 1 An example of the orientation of a medical device is shown in a functional block diagram of a medical device.

[0013] Figure 3 is a functional block diagram of an example external computing device configured to communicate with a medical device.

[0014] Figure 4 is a functional block diagram illustrating an example system including an external computing device such as a server and one or more other computing devices coupled to a server via a network. Figure 1 Medical and external computing devices shown.

[0015] Figures 5A to 5C is a conceptual diagram illustrating roll, pitch, and yaw angles of an example medical device that may be worn by or implanted in a patient.

[0016] Figure 6 is a conceptual diagram showing an example of a sagittal plane, a coronal plane, and a transverse plane of a patient.

[0017] Figures 7A to 7C is a diagram depicting example sensed orientation vector data for a patient in accordance with the techniques of this disclosure.

[0018] Figure 8 is a diagram depicting an example transition from a rest period to an active period for determining an upright posture vector, in accordance with techniques of this disclosure.

[0019] Figures 9A to 9H is a diagram illustrating example patient posture data obtained according to the techniques of this disclosure.

[0020] Figure 10 is a flowchart illustrating example operations according to the techniques of this disclosure.

[0021] Figure 11 is a flowchart illustrating example operations according to the techniques of this disclosure.

[0022] Like reference numerals refer to like elements throughout the drawings and the specification. DETAILED DESCRIPTION

[0023] Figure 1is a conceptual diagram illustrating an example medical device system 1 configured to automatically detect the orientation of an implantable medical device 10 in accordance with one or more techniques of the present disclosure. The example medical system 1 may also be referred to as a "medical system" or "system." In general, a system (e.g., system 1) may include one or more medical devices, leads, implantable or external sensors, external devices, or other components configured for the techniques described herein. The medical system 1 is an example of a medical device system that is configured to implement the techniques described herein for monitoring chest orientation, body posture, and other physiological parameters of a patient 2, which may include blood pressure, patient motion, patient activity, or heart rate. In the example shown, the medical system 1 includes an implantable medical device (IMD) 10 (also referred to as an implantable monitoring device, an implantable hub device, or an implantable cardiac monitor (ICM)), for example, that may communicate with one or more external devices 12 or Figure 1 4. The IMD 10A may communicate with one or more other implantable devices not shown in the figure. Such other implantable devices may be an implantable pacemaker, an implantable cardiac defibrillator, a cardiac resynchronization therapy (CRT) device (e.g., a CRT-D defibrillator or a CRT-P pacemaker), a neuro-stimulator, a nerve stimulator, a drug pump (e.g., an insulin pump), or other devices. In some instances, some of these devices may alternatively be external medical devices, such as an insulin pump. In some instances, another implantable device, also not shown, may include an implantable pressure sensing device that can be implanted in the pulmonary artery of the heart 4. The communication link between the external device 12 and the IMD 10A is represented by communication link 6.

[0024] exist Figure 1 In an example of the present invention, IMD 10A is an insertable cardiac monitor (ICM) that is capable of sensing and recording electrocardiogram (ECG) signals from a location external to heart 4 via electrodes. In some examples, IMD 10A includes or is coupled to one or more additional sensors, such as an accelerometer, that generate one or more signals that vary based on patient motion or chest orientation, blood flow, impedance, heart rate, oxygen saturation, or respiration. IMD 10A can monitor physiological parameters indicative of a patient's state, such as posture, heart rate, activity level, heart rate, or respiratory rate, and IMD 10A can measure one or more physiological parameters while another implantable medical device, not shown, is measuring cardiovascular pressure. IMD 10A can include a light emitter and a light detector configured to transmit a signal (e.g., a light signal) into a target site in patient 2.

[0025] IMD 10A may include processing circuitry and a sensor responsive to a gravitational field, such as a three-axis accelerometer. The processing circuitry of IMD 10A may receive the sensor output and determine the posture of patient 2 based on the sensor output. The posture information of patient 2 sensed by IMD 10A may be used to monitor patient parameters or control therapy delivery by IMD 10A. For example, circadian metrics such as sleep onset and end times, the number of bed exits during the night, or the amount of time spent lying down during the day may be used by a clinician to guide therapy delivery for patient 2. For example, determining that patient 2 has a history of sleeping in various lying positions but now sleeps exclusively in a supine position may indicate a change in patient 2's medical condition. Furthermore, determining that patient 2's activity level has remained stable over a period of time, but that patient 2 has spent more time lying down during the day, may also indicate a change in patient 2's medical condition. Furthermore, the use of posture may be used to activate or deactivate the collection of patient parameters by IMD 10A. For example, IMD 10A may collect patient 2's respiratory data only when patient 2 is standing upright. IMD 10A can sense a number of different postures of patient 2 including, for example, an upright or standing position, a prone position, a supine position, a left lateral decubitus (lying on the left side) position, or a right lateral decubitus (lying on the right side) position.

[0026] In some embodiments, IMD 10A can be implanted outside the chest of patient 2 (e.g., subcutaneously or submuscularly, such as in the pectoral region). IMD 10A can be positioned near the sternum, near the level of heart 4 or just below the level of the heart. In some examples, IMD 10A can utilize Reveal LINQ TM In another example, the IMD 10A may be in the form of an ICM, available from Medtronic plc of Dublin, Ireland. In other examples, the IMD 10A may be in the form of another physiological monitor or tracker, a pacemaker, a defibrillator, a drug delivery system, or other type of medical device, or any other implantable or external device.

[0027] IMD 10A may include a timer and processing circuitry configured to determine the time of day based on the timer value, or to determine the amount of time between events (e.g., posture changes). IMD 10A may include wireless communication circuitry configured to transmit or receive signals from another device, such as to or from one or more external devices 12, or to another implantable medical device. IMD 10A may determine, for example, the patient's posture and transmit this determination to external device 12.

[0028] IMD 10A may transmit data regarding posture and other physiological parameter data acquired by IMD 10A to external device 12. For example, IMD 10A may transmit any data related to cardiovascular pressure, posture, heart rate, activity level, respiratory rate, or other physiological parameters described herein to external device 12. For purposes of this disclosure, chest posture or other physiological measurements may include one or more numerical values, a multivariate table (e.g., such values ​​as a function of time), or other techniques for storing such data.

[0029] External device 12 may include processing circuitry configured to communicate, execute programmed instructions, provide alerts to the patient or others, and perform other functions. In some examples, external device 12 may be a computing device that communicates with IMD 10A via wireless telemetry (e.g., for use in a home, outpatient, clinic, or hospital setting). External device 12 may include or be coupled to a medical device such as is available from Medtronic plc in Dublin, Ireland. As examples, external device 12 may be a programmer, an external monitor, or a consumer device (e.g., a smartphone). In some examples, external device 12 may receive data, alarms, patient instructions, or other information from IMD 10A.

[0030] External device 12 can be used to program commands or operating parameters into IMD 10A to control its functions (e.g., when it is configured as a programmer for IMD 10A). External device 12 can be used to query IMD 10A to retrieve data, including device operating data and physiological data accumulated in IMD memory. Querying can be automatic, such as according to a schedule or in response to a remote or local user command. Programmers, external monitors, and consumer devices are examples of external devices 12 that can be used to query IMD 10A. Examples of communication technologies used by IMD 10A and external device 12 include radio frequency (RF) telemetry, which can be an RF link established via Bluetooth, WiFi, or Medical Implant Communication Service (MICS). In other words, medical system 1 is an example of a medical device system configured to determine, monitor, and track the chest orientation, posture, and activity of patient 2. The techniques described herein can be performed by processing circuitry of medical system 1, such as processing circuitry of one or more of IMD 10A, external device 12, and one or more other implantable or external devices (not shown), individually or collectively.

[0031] According to the techniques of the present disclosure, IMD 10A automatically detects and calibrates a reference orientation of IMD 10A within patient 2. As described herein, "calibrating" IMD 10A means determining a reference orientation of IMD 10A within patient 2 for subsequent use in determining patient 2's posture. When IMD 10A is implanted within patient 2, a clinician may calibrate the reference orientation of IMD 10A relative to patient 2's body. Subsequently, as patient 2 changes posture, the orientation of the body part of patient 2 in which IMD 10A is implanted and / or the orientation of IMD 10A may change. IMD 10A may detect such changes in the orientation of IMD 10A relative to patient 2 and use such information to determine changes in patient 2's posture for monitoring patient parameters or controlling the delivery of therapy. However, over time, IMD 10A may shift or rotate within patient 2. This may introduce errors in posture detection operations performed by IMD 10A because the reference points used by IMD 10A to determine posture may remain valid as long as the relative position of IMD 10A remains stable with respect to patient 2. Thus, over time, accumulated displacement, rotation, or other movement of IMD 10A may require recalibration to account for the new position and / or orientation of IMD 10A.

[0032] As described herein, IMD 10 automatically detects patient 2's body rotational plane. For example, even when the position and / or orientation of IMD 10A changes within patient 2's body, IMD 10A automatically recalibrates a reference orientation to accurately detect patient 2's posture. In one example of the disclosed technology, one or more sensors of IMD 10A sense multiple orientation vectors of IMD 10A relative to a gravitational field. IMD 10A processes the multiple orientation vectors to identify an upright vector from the multiple orientation vectors. For example, by assuming that patient 2 is in an upright position most of the time or during daytime, it can be assumed that the average of the multiple orientation vectors corresponds to patient 2's upright posture. IMD 10A classifies the multiple orientation vectors relative to the upright vector to define a sagittal plane of patient 2 and a transverse plane of patient 2. IMD 10A determines a reference orientation of IMD 10A within patient 2 based on the upright vector, the sagittal plane, and the transverse plane. Furthermore, IMD 10A can use the determined orientation of IMD 10A within patient 2 to determine patient 2's current posture. Thus, even when the position and / or orientation of IMD 10A within patient 2 changes over time, IMD 10A operating according to the techniques of this disclosure can recalibrate the reference orientation of IMD 10A relative to patient 2 and accurately detect the posture of patient 2. Thus, by using the techniques of this disclosure, IMD 10A can reduce or eliminate the need for a clinician, caregiver, or patient 2 to periodically and manually recalibrate the reference orientation of IMD 10A relative to patient 2.

[0033] Figure 2 is a functional block diagram illustrating various components of IMD 10 . Figure 2 The IMD 10 may include the same Figure 1 Similarly, the following description of IMD 10 may also apply to other medical devices having orientation and activity sensing circuitry that may be attached to a patient, such as by a belt or other means.

[0034] exist Figure 2 In the example of FIG. 1 , IMD 10 includes processing circuitry 80, memory 82, sensing circuitry 84, telemetry circuitry 88, and power supply 90. Processing circuitry 80 includes orientation and activity circuitry 86 and orientation calibration circuitry 92. Memory 82 may store instructions for execution by processing circuitry 80, stimulation therapy data, orientation and activity state information, orientation and activity state indications, and any other information about therapy or patient 2, such as Figure 1 Therapy information may be recorded for long-term storage and retrieval by the user and may include any data created by or stored in IMD 10.

[0035] Memory 82 may include separate memory for storing instructions, orientation and activity state information, program history, and any other data that may benefit from a separate physical memory module. In some examples, memory 82 is a random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory that includes executable instructions for causing one or more processors to perform actions attributed thereto. Furthermore, memory 82y may be fully implemented in hardware, software, or a combination thereof.

[0036] Processing circuitry 80 controls sensing circuitry 84 to sense physiological signals via an electrode combination formed by electrodes in one or more electrode arrays. For example, sensing circuitry 84 may receive signals, such as electrocardiogram signals, via electrodes on one or more leads 16A and 16B. Figure 1 Components of the external device 12 or any other device described in the present disclosure may each include one or more processors, such as one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), programmable logic circuit systems, etc., alone or in any suitable combination.

[0037] The sensing circuit system 84 can be configured to switch between one or more sensing vectors by, for example, electrodes in an electrode array. In some instances, the sensing circuit system 84 can be configured to sense muscle activity, impedance changes, and sense the electrical activity of the heart. Although not shown, some examples of medical devices that perform the orientation and activity sensing technology of the present disclosure may include medical devices that are configured to deliver treatments such as drug therapy or electrical stimulation therapy. Such medical devices may include treatment generation circuit systems in place of or in addition to the sensing circuit systems, as well as chemical detection, light or temperature detection, or other sensors. Some example medical devices may include pacemakers, cardioverters, defibrillators, and neurostimulators.

[0038] In some instances, IMD 10 can additionally or alternatively be configured to sense one or more physiological parameters of patient 2. For example, the physiological parameters can include heart rate, electromyogram (EMG), electroencephalogram (EEG), electrocardiogram (ECG), body temperature, respiratory rate, or pH. In some instances, these physiological parameters can be used by processing circuitry 80 to confirm or reject sensed changes in orientation and activity state that may be caused by vibration, patient travel (e.g., in an airplane, car, or train), or some other false positives in orientation and activity state. In some instances, processing circuitry 80 can use heart rate, activity, and / or time of day (as examples) to assume or confirm that the patient is upright (awake and active) or lying down (asleep and inactive).

[0039] Wireless telemetry between IMD 10 and external device 12 or another device can be achieved through, for example, radio frequency (RF) communication, proximal inductive interaction, or tissue conduction communication (TCC) between IMD 10 and external device 12. Telemetry circuitry 88 can send and receive information to and from external device 12 continuously, at periodic intervals, at aperiodic intervals, or upon request from a stimulator or programmer. To support wireless communication, telemetry circuitry 88 can include appropriate electronic components, such as amplifiers, filters, mixers, encoders, decoders, etc.

[0040] Power supply 90 delivers operating power to the components of IMD 10. Power supply 90 may include a small rechargeable or non-rechargeable battery and power generation circuitry to generate the operating power. Recharging may be achieved through proximal inductive interaction between an external charger and an inductive charging coil within IMD 10. As an example, external device 12 may include a charger that recharges power supply 90 of IMD 10. Thus, the programmer and charger may be integrated into the same device. Alternatively, in some cases, a charger unit may serve as an intermediary device that communicates with both the IMD and the programmer. In some instances, the power requirements may be small enough to allow IMD 10 to utilize patient motion and implement a kinetic energy scavenging device to trickle charge the rechargeable battery. In other instances, conventional batteries may be usable for a limited time. As another alternative, an external inductive power supply may provide transcutaneous power to IMD 10 when needed or desired.

[0041] Orientation and activity circuitry 86 allows IMD 10 to sense body posture and activity states, such as body part orientation, activity, or any other static orientation or motion of patient 2. Orientation and activity circuitry 86 may include circuitry suitable for amplifying, filtering, or otherwise processing signals received from orientation and activity sensor 87. The orientation and activity state information generated by orientation and activity circuitry 86 and processing circuitry 80 may correspond to an overall level of activity and / or posture or physical activity undertaken by patient 2, such as activity counts based on footsteps or the like.

[0042] exist Figure 2 In an example of , the orientation and activity circuit system 86 can receive signals from the orientation and activity sensor 87. The orientation and activity sensor 87 includes one or more accelerometers, such as a 3-axis accelerometer, which can detect a static orientation or a three-dimensional vector. In some examples, the one or more accelerometers can be responsive to a gravitational field. For example, the orientation and activity sensor 87 can include one or more microelectromechanical accelerometers. In other examples, the orientation and activity sensor 87 can alternatively or additionally include one or more gyroscopes, pressure transducers, or other sensors to sense the posture and activity of the patient 2. Although the orientation and activity sensor 87 is described as including a 3-axis accelerometer, the orientation and activity sensor 87 can include multiple single-axis accelerometers, dual-axis accelerometers, 3-axis accelerometers, or some combination thereof.

[0043] In some examples, processing circuitry 80 processes analog outputs of orientation and activity sensor 87 in orientation and activity circuitry 86 to determine activity and / or orientation data. For example, orientation and activity circuitry 86 of processing circuitry 80 may process raw signals provided by orientation and activity sensor 87 to determine activity counts. In some examples, processing circuitry 80 may process signals provided by orientation and activity sensor 87 to determine velocity information along each axis. In this disclosure, unless otherwise specified, the term "sensor" refers to orientation and activity sensor 87, such as an accelerometer.

[0044] In one example, each of the x, y, and z signals provided by the orientation and activity sensor 87 has a DC component and an AC component. The DC component can describe the gravitational force exerted on the sensor and, thus, can be used to determine the sensor's orientation within the Earth's gravitational field. In other words, the sensor can be responsive to the gravitational field. Assuming the sensor's orientation is relatively fixed relative to patient 2, the DC components of the x, y, and z signals can be used to determine the patient's body part's orientation within the gravitational field, and thus, the patient's body posture or orientation of the patient's body part. In other words, the processing circuitry 80 can receive the output of the orientation and activity sensor 87 and determine the orientation vector of the orientation and activity sensor 87 relative to the gravity vector of the gravitational field. As a result of determining the orientation of the orientation and activity sensor 87 relative to the patient's body part, the processing circuitry 80 can determine the body part's orientation or, in some examples, the patient's body posture. The processing circuitry 80 can interpret the x, y, and z signals as the sensor's orientation vector [ax, ay, az].

[0045] The AC components of the x, y, and z signals can yield information about the patient's motion. In particular, the AC components of the signals can be used to derive values ​​describing the activity of the patient's motion in one or more axis combinations. This activity can relate to the patient's level, direction of motion, or acceleration.

[0046] One method for determining activity is activity counting. Activity counting can be used to indicate patient 2's activity or activity level. For example, a signal processor can sum the amplitude of the AC portion of the accelerometer signal for N consecutive samples. For example, assuming sampling occurs at 25 Hz, N can be set to 25, causing the counting logic to provide the sum of samples acquired within one second. This sum can be referred to as the "activity count."

[0047] In some instances, the number "N" of consecutive samples may be selected by the orientation and activity circuitry 86 of the processing circuitry 80 based on the current orientation and activity state. The activity count may be the activity portion of the orientation and activity state parameter value, which may be associated with the orientation portion. The resulting orientation and activity state parameter value may then be combined with both activity and orientation to generate an accurate indication of the movement of the patient 2. In some instances, the posture portion of the orientation and activity state parameter may include a posture, such as standing upright, lying down, or a combination of dorsal and lateral angles. In certain instances, the orientation and activity state parameter may include an orientation vector of the sensor.

[0048] As another example, the active portion of the orientation and activity state parameter value can describe the direction of motion. The activity parameter can be associated with one or more directional activity vectors that describe the 3-dimensional activity level. Another example of an activity parameter relates to acceleration in a particular direction. The magnitude and orientation value that quantifies the maximum directional change of motion over a period of time can be associated with the active portion of the orientation and activity state parameter value.

[0049] Processing circuitry 80 may store the orientation and activity state information in memory 82 for later review by a clinician, for use in adjusting treatment, for presenting orientation and activity state indications to patient 2, or some combination thereof. As an example, processing circuitry 80 may record the orientation and activity state parameter values ​​or outputs of a 3-axis accelerometer and assign the orientation and activity state parameter values ​​to a predefined posture indicated by the orientation and activity state parameter values. In this manner, IMD 10 is able to track how often and how long patient 2 maintains a particular posture.

[0050] In some instances, when orientation and activity circuitry 86 indicates that patient 2 has actually changed posture, processing circuitry 80 may also adjust therapy or sensing for the new orientation (e.g., by changing electrode vectors, stimulation waveform parameters, or sensing thresholds for one or more physiological parameters). Thus, IMD 10 may be configured to provide posture-responsive therapy or sensing to patient 2. Therapy or sensing adjustments in response to orientation and activity state may be automatic or semi-automatic (e.g., with patient approval). In many cases, fully automatic adjustments may be desired so that IMD 10 can react more quickly to changes in orientation and activity state.

[0051] The orientation and activity state parameter values ​​from orientation and activity circuitry 86 that indicate the orientation and activity state may vary throughout the day for patient 2. However, a particular activity (e.g., walking, running, or cycling) or posture (e.g., standing, sitting, or lying down) may include multiple orientation and activity state parameter values ​​from orientation and activity circuitry 86. Memory 82 may contain a definition for each orientation and activity state for patient 2.

[0052] Posture-responsive stimulation may allow IMD 10 to implement a level of automation in therapy adjustments. Automatically adjusting stimulation may relieve patient 2 from the ongoing task of manually adjusting therapy each time patient 2 changes posture or starts and stops a certain orientation and activity state. Such manual adjustment of stimulation parameters may be tedious, requiring patient 2 to, for example, press one or more keys of external device 12 multiple times during changes in the patient's posture or activity state to maintain adequate symptom control. In some instances, patient 2 may eventually be able to receive orientation- and activity-state-responsive stimulation therapy without having to continue making changes for different postures through external device 12. Alternatively, patient 2 may transition immediately or over time to fully automatic adjustments based on their orientation and activity state.

[0053] As described above, orientation and activity sensor 87 includes one or more accelerometers, such as a three-axis accelerometer, that can detect a static orientation or a three-dimensional vector. In some instances, one or more accelerometers can be responsive to a gravitational field. When IMD 10A is implanted in patient 2, a clinician can calibrate orientation and activity sensor 87 relative to the known orientation of IMD 10A and the body posture of patient 2. For example, a clinician can record the values ​​of the accelerometers of orientation and activity sensor 87 along the X, Y, and Z directions for each of a plurality of postures of patient 2 and store the accelerometer values ​​as reference values. Subsequently, processing circuit system 80 can compare the values ​​of the accelerometers of orientation and activity sensor 87 with the reference values ​​to determine the posture being adopted by patient 2. Processing circuit system 80 uses the detected posture changes to inform monitoring of patient parameters or to control the delivery of therapy to patient 2.

[0054] However, over time, IMD 10 may shift or rotate within patient 2. This may result in errors in the posture detection operations performed by IMD 10. For example, if IMD 10 changes position within patient 2, the reference values ​​of orientation and activity sensor 87 may no longer correspond to the actual posture being adopted by patient 2.

[0055] In accordance with the techniques of the present disclosure, orientation and activity circuitry 86 further includes orientation calibration circuitry 92. Orientation calibration circuitry 92 may automatically and periodically recalibrate the reference orientation of IMD 10 relative to the body of patient 2. Thus, orientation calibration circuitry 92 may ensure that orientation and activity circuitry 86 may accurately detect the posture of patient 2 even when the position and / or orientation of IMD 10 changes within the body of patient 2. Thus, by using the techniques of the present disclosure, orientation calibration circuitry 92 may reduce or eliminate the need for a clinician to periodically and manually recalibrate the reference orientation of IMD 10 relative to patient 2. Without using the techniques of the present disclosure, a clinician may need to periodically recalibrate the reference orientation of IMD 10. Furthermore, if IMD 10 is not frequently recalibrated, patient parameter data obtained by IMD 10 may be inaccurate or unusable.

[0056] In one example of the disclosed technology, one or more sensors of an IMD 10A sense multiple orientation vectors of the IMD 10A relative to a gravitational field. The IMD 10A processes the multiple orientation vectors to identify an upright vector from the multiple orientation vectors. For example, by assuming that patient 2 is in an upright position most of the time or during daytime, it can be assumed that the average of the multiple orientation vectors corresponds to the upright posture of patient 2. The IMD 10A classifies the multiple orientation vectors relative to the upright vector to define a sagittal plane of patient 2 and a transverse plane of patient 2. The IMD 10A determines a reference orientation of the IMD 10A within patient 2 based on the upright vector, the sagittal plane, and the transverse plane. Furthermore, the IMD 10A can use the determined reference orientation of the IMD 10A within patient 2 to determine the current posture of patient 2. Therefore, even when the position and / or orientation of the IMD 10A within patient 2 changes over time, the IMD 10A operating according to the disclosed technology can recalibrate the reference orientation and accurately detect the posture of patient 2. Thus, by using the techniques of this disclosure, IMD 10A may reduce or eliminate the need for a clinician to periodically and manually recalibrate a reference orientation of IMD 10A relative to patient 2 .

[0057] In one example, orientation and activity sensor 87 senses multiple orientation vectors of IMD 10 relative to the gravitational field. Typically, each orientation vector points in a direction opposite to the direction of the Earth's gravitational field. Therefore, the orientation vectors can be assumed to point "up" and can be used as reference points to determine the current orientation of IMD 10 relative to the Earth's gravitational field. The disclosed techniques further utilize various algorithms and assumed patient behaviors to cluster the multiple orientation vectors together, allowing orientation calibration circuitry 92 to estimate a reference orientation of IMD 10 relative to patient 2's body. By calibrating the position of IMD 10 relative to the Earth's gravitational field and estimating the reference orientation of IMD 10 relative to patient 2's body, orientation calibration circuitry 92 estimates patient 2's posture without using external reference points. Consequently, orientation calibration circuitry 92 can recalibrate the reference orientation of IMD 10 relative to patient 2's body, and orientation and activity circuitry 86 can perform accurate posture detection of patient 2 even after IMD 10 moves within patient 2's body.

[0058] The orientation calibration circuit system 92 processes the multiple orientation vectors sensed by the orientation and activity sensor 87 to identify an upright vector from the multiple orientation vectors. The upright vector can correspond to an upright posture of the patient 2, such as standing or sitting. The technology of the present disclosure assumes that the patient is in an upright posture for most of the time in a 24-hour period (or most of the time during the day). Therefore, the average vector of the multiple orientation vectors can be assumed to roughly correspond to the patient's upright posture. However, the upright vector can be made more accurate by filtering the multiple orientation vectors to obtain a set of vectors associated with the time of day when the patient 2 is expected to be active or the patient's high activity level.

[0059] As one example for determining the upright vector, orientation and activity sensor 87 senses a plurality of orientation vectors of IMD 10 relative to the gravitational field over a period of time. Orientation calibration circuitry 92 determines one or more orientation vectors from the plurality of orientation vectors that correspond to an activity level of patient 2 that exceeds a patient activity threshold. Orientation calibration circuitry 92 may average the one or more orientation vectors corresponding to an activity level of patient 2 that exceeds the patient activity threshold such that the average vector corresponds to the upright vector.

[0060] As one example of determining the upright vector, orientation and activity sensor 87 senses multiple orientation vectors of IMD 10 relative to the gravitational field over a period of time. Orientation calibration circuitry 92 determines one or more orientation vectors from the multiple orientation vectors that correspond to the patient's walking activity. For example, orientation calibration circuitry 92 may detect strides within the signal generated by orientation and activity sensor 87 to determine when patient 2 is walking. For example, orientation and activity sensor 87 may include one or more accelerometers or pressure sensors that sense pulses generated when patient 2 moves (e.g., steps, standing, walking). Orientation calibration circuitry 92 may process the pulses to determine whether patient 2 has performed an action and classify the action as a "stride." Orientation calibration circuitry 92 may further count the number and frequency of actions classified as "strides" to determine whether patient 2 is experiencing walking activity. Orientation calibration circuitry 92 may select only those orientation vectors sensed concurrently with the patient's walking activity. Orientation calibration circuitry 92 may average one or more orientation vectors corresponding to patient 2's activity level exceeding a patient activity threshold, such that the average vector corresponds to the upright vector.

[0061] As another example of determining the upright vector, Figure 1 An external device, such as external device 12, may determine the posture of patient 2 and transmit the determined posture to IMD 10. For example, external device 12 may receive input from the patient indicating the posture that patient 2 has assumed. Additionally, external device 12 may include a pedometer, accelerometer, or other type of sensor capable of detecting the posture of patient 12. Orientation and activity sensor 87 senses multiple orientation vectors of IMD 10 relative to a gravitational field over a period of time. Processing circuitry 80 receives the posture of patient 2 determined for the sensed orientation vectors from external device 12 via telemetry circuitry 88. Orientation calibration circuitry 92 determines one or more orientation vectors from a plurality of orientation vectors corresponding to the patient's upright posture based on the sensed orientation vectors associated with various postures of patient 2. Orientation calibration circuitry 92 may average the one or more orientation vectors corresponding to an activity level of patient 2 that exceeds a patient activity threshold, such that the average vector corresponds to an upright vector.

[0062] Orientation calibration circuitry 92 classifies the plurality of orientation vectors relative to the upright vector to define a transverse plane of patient 2. The transverse plane of patient 2 is a conceptual plane that roughly divides the body of patient 2 into upper and lower portions. The disclosed techniques assume that sensed orientation vectors sensed above and below the transverse plane of patient 2 can correspond to an upright position or a recumbent position of patient 2, respectively.

[0063] To determine the transverse plane, the orientation calibration circuitry 92 determines the angle between the orientation vector and the upright vector for each sensed orientation vector. The orientation calibration circuitry 92 compares the angle between the orientation vector and the upright vector to a predetermined upright angle. For example, the predetermined upright angle may differ from the upright angle by approximately 20 degrees. If the angle between the orientation vector and the upright vector is less than the predetermined upright angle, the orientation calibration circuitry 92 classifies the corresponding orientation vector as one of a first subset of orientation vectors associated with the patient's upright posture. If the angle between the orientation vector and the upright vector is greater than or equal to the predetermined upright angle, the orientation calibration circuitry 92 classifies the corresponding orientation vector as one of a second subset of orientation vectors associated with the patient's supine posture. The orientation calibration circuitry 92 defines the patient's transverse plane as the plane between the first subset of the plurality of orientation vectors associated with the patient's upright posture and the second subset of the plurality of orientation vectors associated with the patient's supine posture.

[0064] Furthermore, the orientation calibration circuitry 92 classifies the plurality of orientation vectors relative to the upright vector to define a sagittal plane of the patient 2. The sagittal plane of the patient 2 is a conceptual plane that roughly divides the body of the patient 2 into left and right halves. The disclosed techniques assume that the sensed orientation vectors directed to the left and right sides of the sagittal plane of the patient 2 may correspond to the patient lying on the left or right side of the body (e.g., in a left-lying or right-lying posture).

[0065] As one example for determining the sagittal plane, the orientation calibration circuitry 92 associates each orientation vector with a grid element of the sphere based on the orientation of the orientation vector relative to the upright vector. The orientation calibration circuitry 92 identifies a subset of the plurality of grid elements that form an angle with the upright vector that is less than a predetermined upright angle. In some examples, the predetermined upright angle is 20 degrees. The orientation calibration circuitry 92 applies a clustering algorithm to identify a cluster of grid elements from the plurality of grid elements that are adjacent to the subset of the plurality of grid elements. In some examples, a grid element is a neighbor of the subset of the plurality of grid elements if its offset from a grid element in the subset of the plurality of grid elements is less than 10 degrees. The orientation calibration circuitry 92 determines a principal eigenvector for the cluster of grid elements. The principal eigenvector includes an eigenvalue that is higher than every other eigenvector in the plurality of eigenvectors of the cluster of grid elements. The orientation calibration circuitry 92 determines a normal vector based on the cross product of the upright vector and the principal eigenvector, the normal vector defining the sagittal plane of the patient 2, wherein the normal vector is perpendicular to the sagittal plane.

[0066] As another example for determining the sagittal plane, orientation calibration circuitry 92 classifies each orientation vector as either one of a first subset of a plurality of orientation vectors associated with the patient's upright posture or one of a second subset of a plurality of orientation vectors associated with the patient's supine posture. Orientation calibration circuitry 92 calculates a plurality of cross products between each orientation vector in the first subset of a plurality of orientation vectors associated with the patient's upright posture and each orientation vector in the second subset of a plurality of orientation vectors associated with the patient's supine posture. Orientation calibration circuitry 92 determines a weighted average of each cross product to obtain a normal vector defining the sagittal plane of patient 2.

[0067] Orientation calibration circuitry 92 determines a reference orientation of IMD 10 within patient 2 based on the upright vector, the sagittal plane, and the transverse plane. After orientation calibration circuitry 92 determines the reference orientation of IMD 10, orientation and motion circuitry 86 may use the reference orientation to determine the posture of patient 2 based on the current orientation of IMD 10. For example, orientation and motion sensor 87 senses the orientation vector of patient 2. Orientation and motion circuitry 86 compares the orientation vector with the upright vector, the transverse plane, and the sagittal plane to determine the posture of patient 2. Generally speaking, if the orientation vector is similar to the upright vector and above the transverse plane, orientation and motion circuitry 86 determines that patient 2 is in an upright posture. If the orientation vector is dissimilar to the upright vector, below the transverse plane, and generally aligned with the sagittal plane, orientation and motion circuitry 86 determines that patient 2 is in a prone or supine position. If the orientation vector is dissimilar to the upright vector, generally aligned with the transverse plane, and to the left or right of the sagittal plane, orientation and motion circuitry 86 determines that patient 2 is in a left or right transverse position. Additional details of evaluating the orientation vector relative to the upright vector, the transverse plane, and the sagittal plane are described in more detail below.

[0068] Due to some inherent sensor inaccuracies and modeling errors, the transverse plane constructed by orientation calibration circuitry 92 may or may not be coplanar with the actual true sagittal plane of patient 2. Furthermore, the sagittal plane constructed by orientation calibration circuitry 92 may or may not be coplanar with the actual true transverse plane of patient 2. Furthermore, the transverse and sagittal planes constructed by orientation calibration circuitry 92 may not necessarily be perpendicular to one another (as are the actual true transverse and sagittal planes of patient 2). However, an advantage of the disclosed techniques is that orientation calibration circuitry 92 can still accurately calibrate the orientation of IMD 10 relative to the body of patient 2, such that orientation and motion circuitry 86 can accurately detect the posture of patient 2, even if the transverse and sagittal planes of patient 2 calculated by orientation calibration circuitry 92 are not perpendicular to one another or coplanar with the actual true transverse and sagittal planes of patient 2.

[0069] Figure 3 It shows that Figure 2 10 is a functional block diagram of various components of the external device 12 of the IMD 10. Figure 3 As shown, external device 12 includes processing circuitry 140, memory 142, telemetry circuitry 146, a user interface 144, and post-processing circuitry 112. Clinician or patient 2 can interact with user interface 144 to review information received by the programmer from IMD 10, such as orientation and activity state over time sensed and / or determined by IMD 10 or other information described herein, and to program sensing and / or stimulation functions of IMD 10, e.g., select parameter values ​​for controlling these functions.

[0070] Memory 142 contains operating instructions for processing circuit system 140 and data related to patient 2 and / or IMD 10, such as data received from IMD 10. Memory 142 may also contain operating instructions for other features of external device 12. In some examples, memory 142 is a random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory that includes executable instructions for causing one or more processors to perform actions attributed thereto. Furthermore, memory 142 may be entirely implemented in hardware, software, or a combination thereof.

[0071] Telemetry circuitry 146 allows data to be transferred to and from IMD 10. Telemetry circuitry 146 may communicate with IMD 10 automatically at predetermined times or when the telemetry circuitry detects the proximity of a stimulator. Alternatively, telemetry circuitry 146 may communicate with IMD 10 upon signaling by a user via user interface 144. To support wireless communication, telemetry circuitry 146 may include appropriate electronic components, such as amplifiers, filters, mixers, encoders, decoders, etc. In some cases, external device 12 may be used when coupled to an alternating current (AC) outlet, such as AC line power, either directly or through an AC / DC adapter.

[0072] Figure 4 is a block diagram illustrating an example system 120 including an external device such as a server 164 and one or more computing devices 170A through 170N coupled to a server via a network 162. Figure 1 and Figure 2IMD 10 and external device 12 are shown. In this example, IMD 10 can use its telemetry circuitry 88 to communicate with external device 12 via a first wireless connection 163A and with access point 160 via a second wireless connection 163B.

[0073] exist Figure 4 In the example of FIG1 , access point 160, external device 12, server 164, and computing devices 170A through 170N are interconnected via network 162 and are capable of communicating with one another. In some cases, one or more of access point 160, external device 12, server 164, and computing devices 170A through 170N may be coupled to network 162 via one or more wireless connections. IMD 10, external device 12, server 164, and computing devices 170A through 170N may each include one or more processors, such as processing circuitry 168. The one or more processors may include one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic circuitry, and the like, which may perform various functions and operations, such as those described in the present disclosure.

[0074] Access point 160 may include a device, such as a home monitoring device, that is connected to network 162 through any of a variety of connections, such as a telephone dial-up, a digital subscriber line (DSL), or a cable modem connection. In other examples, access point 160 may be coupled to network 162 through different forms of connection, including a wired connection or a wireless connection.

[0075] During operation, IMD 10 may collect and store various forms of data. For example, IMD 10 may collect sensed orientation and activity state information indicating, for example, Figure 116. How patient 2 moves throughout the day is shown. In some cases, IMD 10 may directly analyze the collected data to assess patient 2's posture and activity state, such as the percentage of time patient 2 is in each identified posture. However, in other cases, IMD 10 may send stored data about posture and activity state information wirelessly or via access point 160 and network 162 to external device 12 and / or server 164 (e.g., processing circuit system 168 of server 164) for remote processing and analysis. For example, IMD 10 may sense, process, trend analyze, and evaluate the sensed posture and activity state information. Alternatively, the processing, trend analysis, and evaluation functions may be distributed to other devices coupled to network 162, such as external device 12 or server 164 (e.g., processing circuit system 168 of server 164). In addition, posture and activity state information may be archived by any such device, for example, for later retrieval and analysis by a clinician. For example, the server 164 may archive posture and activity state information at the memory 166 , which may include patient body angles.

[0076] In some cases, external device 12 or server 164 may process the posture and activity state information, raw orientation data, other physiological sensing information, and / or treatment information into a displayable orientation and activity state report that can be displayed by external device 12 or one of computing devices 170A to 170N. The posture and activity state report may include trend data for evaluation by a clinician, such as through visual inspection of graphical data. In some cases, based on analysis and evaluation automatically performed by IMD 10, external device 12, or server 164, the posture and activity state report may include the number of activities performed by patient 2, the percentage of time patient 2 was in each posture and activity state, the average time patient 2 was continuously in each posture and activity state, and information about sensing and treatment delivery during each activity, or any other information relevant to patient 2's treatment. A clinician or other trained professional may review and / or annotate the posture and activity state report and may identify any challenges or issues related to physiological sensing or treatment that should be addressed.

[0077] In some cases, server 164 may be configured to provide a secure storage site for archiving posture and activity state information collected from IMD 10 and / or external device 12. Network 162 may include a local area network, a wide area network, or a global network, such as the Internet. In some cases, external device 12 or server 164 may compile posture and activity state information in a web page or other document for viewing by a trained professional, such as a clinician, via a viewing terminal associated with computing devices 170A to 170N. In some aspects, a computer system similar to Medtronic developed by Medtronic plc of Minneapolis, MN, may be used. The system 120 is implemented using common network technologies and functions provided by the Internet.

[0078] While some examples of the present disclosure may relate to posture and activity state information and data, system 120 may be used to distribute any information related to the treatment of patient 2 and the operation of any device associated therewith. For example, system 120 may allow treatment errors or device errors to be immediately reported to a clinician. Furthermore, system 120 may allow a clinician to remotely intervene and reprogram IMD 10, external device 12, or communicate with patient 2. In additional examples, a clinician may utilize system 120 to monitor multiple patients and share data with other clinicians in an effort to coordinate the rapid development of effective treatment for the patients.

[0079] Furthermore, the techniques of this disclosure may be applied to IMDs that deliver other therapies in which posture and activity state information is important, such as deep brain stimulation (DBS), pelvic floor stimulation, gastric stimulation, occipital stimulation, functional electrical stimulation, cardiac pacing, anti-tachycardia therapy, etc. Furthermore, in some aspects, the techniques for assessing posture and activity state information as described in this disclosure may be applied to IMDs that are typically dedicated to sensing or monitoring and do not include stimulation or other therapeutic components, such as IMD 10.

[0080] Figures 5A to 5C is a conceptual diagram illustrating roll, pitch, and yaw angles of IMD 10B. Figures 5A to 5C The illustrated techniques may also be applied to any medical device that may be worn by a patient or otherwise attached to a patient, such as using an arrangement of straps, or implanted in a patient.

[0081] 3-axis accelerometers have been used externally for a short time and are placed on or around the surface of the body to monitor body posture, activity level and / or orientation and / or movement of a particular limb or body part for various purposes, such as helping to diagnose various movement disorders or automatically detecting falls. 3-axis accelerometers can also be used in implantable devices to perform the same type of monitoring, but over much longer time periods, such as months to years. Medical devices containing such 3-axis accelerometers can be used to measure activity as well as body position and orientation parameters that can help identify when a patient's condition worsens, such as a patient with heart failure, also known as congestive heart failure (CHF). Such parameters can be combined and supplemented with other physiological indicators that also relate to the patient's health status, such as activity, impedance, body temperature, blood pressure, and heart rate.

[0082] In order to correctly monitor body posture or body part orientation, the axis of the accelerometer should be correctly aligned with the body or body part, or a compensating member should be implemented to correct for misalignment. In most external applications, 3-axis accelerometers can be aligned in a consistent manner between subjects by using a belt or chest strap or a holster on the belt. However, device orientation compensation may still be needed to take into account the differences in body shape or the differences in body part orientation between patients. The degree of compensation depends on the required accuracy of the orientation measurement. The longer time range of implantable devices means that the orientation of the implantable device may change over time, for example, due to patient rotation with the device, soft tissue effects or other device migration. As described in the present disclosure, the device orientation compensation setting can be periodically automatically updated to take these changes into account.

[0083] When using a 3-axis accelerometer or other orientation sensor in an implantable device, not only do differences in body shape and orientation of body parts result in variability in the implant orientation of the internal 3-axis accelerometer relative to the Earth's gravitational field, but the implant orientation of the device can also be selected to, for example, best utilize the primary diagnostic or therapeutic function of the device. For example, a medical device can be implanted in a position and orientation selected to best receive ECG signals. Other factors in the orientation and position of the implantable device may include positioning the scar of the implant to minimize the visibility of the scar and to optimize the performance of other sensors on the device, such as a pH monitor.

[0084] Figure 5A An exemplary roll angle 202 of rotation of the IMD 10B is shown with an orientation vector 205A of a sensor such as a 3-axis accelerometer. The IMD 10B may include a sensor similar to that described above with respect to FIG. Figure 1 and 2 Features of the IMD 10A and IMD 10. Figure 5AIn the example of FIG, the roll angle 202 is the angle about the ideal anterior-posterior central axis of the device and is measured relative to the patient's long axis. The long axis is a line running from the top of the patient's head through the spine and through the heels, and is parallel to the Figure 5A The Y-axis is shown. In other words, orientation vector 205A is rotated relative to the long axis of patient 2 at roll angle 202. IMD 10B may be substantially similar to IMD 10, or may be a different IMD, such as a pacemaker, cardioverter-defibrillator, and / or defibrillator. IMD 10B may include circuitry, such as processing circuitry 80, memory 82, orientation and activity sensor 87, and orientation and activity circuitry 86, as described above with respect to FIG. Figure 2 Similar medical devices, including implantable devices or external devices, can implement the techniques described herein for any of IMDs 10, 10A, and 10B. The implantation location of IMD 10B shown herein is merely an example, and medical devices implanted anywhere in a patient's body or attached to a patient, such as with a harness or strap, can implement any of the techniques described herein.

[0085] Figure 5B An example pitch angle 204 of IMD 10B implanted in a patient is shown. Figure 5B In the example of , pitch 204 is the rotation angle about the left-right axis of the patient / device and is measured from a line parallel to the spine of patient 2. Orientation vector 205A is rotated about the left-right axis by pitch angle 204.

[0086] For some applications, it may be necessary to take the yaw of the implant into account. Figure 5C An example yaw 206 of the apparatus 200 is shown. Figure 5C , yaw is the rotation angle around the patient's long axis, which is Figure 5C The Y axis is the direction of the midline. In contrast to yaw 206 which is a rotation about the long axis, roll angle 202 is a rotation measured from the long axis. For clarity, Figure 5C Using orientation vector 205A, and as Figure 5A and 5B As shown, orientation vector 205A points to 180 degrees. Determining and compensating for yaw 206 may be valuable when it is important to identify what position the patient is in when lying down. Lying down positions include supine (facing up) and prone (facing down), as well as other positions, such as right shoulder facing up, etc.

[0087] like Figure 2The medical device processor of the processing circuit system 80 shown can determine orientation information by receiving the output of a sensor such as a 3-axis accelerometer that responds to a gravitational field. Based on the output of the sensor, the processor can determine the orientation vector of the sensor relative to the gravity vector of the gravitational field. In addition, by determining the orientation of the sensor in the medical device relative to the patient, for example, determining the roll, pitch, and yaw of the sensor relative to the patient, the processor can determine the posture information of the patient 2. In the present disclosure, the process of determining the orientation of the sensor relative to the patient is referred to as device orientation compensation, or simply compensation. In some instances, the patient can be in a predetermined compensated posture, such as upright. An additional description of how the processing circuit system 80 of the IMD 10B determines orientation information by receiving the output of a sensor that responds to a gravitational field is described in more detail in U.S. patent application No. 16 / 109,023, filed by Lee et al. on August 22, 2018, entitled "BODY AND BODY PART ORIENTATION AND POSTURE MONITORING."

[0088] Figures 5A to 5C The embodiments shown and described below relate to an IMD 10B implanted in the chest of a patient 2. In other examples, such as Figure 1 While the IMD 10 is shown, the device may be implanted or strapped in other locations, and the procedure may be modified as needed for the selected location. Yaw 206 is selected as the first rotation angle, which for the selected coordinate convention of the present disclosure is defined as the rotation angle about the long y-axis of the IMD 10B, as shown in FIG. Figure 5C Yaw 206 is used to align the device so that the x-axis of the device is parallel to the skin surface at the implant site. In the example shown, the y-axis remains parallel to the spine, and the plane of the IMD 10B is not yet parallel to the plane of the implant. This orientation may be referred to as a "yawed IMD" orientation.

[0089] In some examples, pitch 204 is selected as the second rotation angle, such as Figure 5B The pitch angle corresponds to the rotation angle about the x-axis of IMD 10B to orient the xy plane of the "yaw IMD" so that it is completely parallel to the skin surface at the center of the implant site. During this rotation, the x-axis of the yaw IMD remains parallel to the plane of the patient's waist, and if the patient is standing upright, the x-axes of the yaw and "pitch IMD" orientations remain parallel to the ground.

[0090] By defining the yaw angle and then the pitch angle 204, the plane of the implant site based on the direction of the orientation vector 205A has now been defined by these two rotations. Figure 5A and 5CAs shown, the implant plane is parallel to the orientation vector 205A, and the x-axis of the implant plane remains parallel to the patient's waist. The third and final rotation angle, roll, corresponds to a rotation about the central anterior / posterior z-axis of the device, which is centered at the implant site, as shown in FIG. Figure 5A shown.

[0091] According to the techniques of the present disclosure, IMD 10B automatically detects and calibrates a reference orientation within patient 2. When IMD 10B is implanted in patient 2, a clinician can calibrate the reference orientation of IMD 10B relative to patient 2's body. Subsequently, as patient 2 changes body posture, the orientation of patient 2's body parts and the orientation of IMD 10B may change. IMD 10B can detect such changes in IMD 10B's orientation and use this information to determine changes in the orientation of patient 2's body parts or changes in body posture for monitoring patient parameters or controlling the delivery of therapy. However, over time, IMD 10B may shift or rotate within patient 2. This can introduce errors in the posture detection operations performed by IMD 10B, as the reference point used by IMD 10B remains valid as long as the relative position of IMD 10B with respect to patient 2 remains stable. Therefore, accumulated shifting, rotation, or other movement of IMD 10B over time may require recalibration of IMD 10B to account for the new position and / or orientation of IMD 10B. As described herein, IMD 10B automatically recalibrates a reference orientation used to perform gesture detection even when the position and / or orientation of IMD 10B changes within the patient's body.

[0092] Figure 6 601, coronal plane 602, and transverse plane 603 of a patient. Figure 6 601 is depicted perpendicular to the reader's line of sight. A sagittal plane 601 is a conceptual plane dividing the body of patient 2 into left and right halves. A coronal plane 602 is a conceptual plane dividing the body of patient 2 into anterior and posterior halves. A transverse plane 603 is a conceptual plane dividing the body of patient 2 into upper and lower halves.

[0093] Figures 7A to 7C is a diagram depicting example sensed orientation vector data for a patient according to the techniques of the present disclosure. For convenience, reference is made to Figure 2 IMD 10 Description Figures 7A to 7C However, if Figure 1 IMD 10A or Figures 5A to 5C Other medical devices of the IMD 10B may be as described with respect to Figures 7A to 7C Operate as described.

[0094] like Figures 7A to 7C As shown, the orientation and activity sensor 87 senses a plurality of orientation vectors 702. The plurality of orientation vectors 702 contains a representative sampling of the activity levels of the patient 2, such as periods of walking and / or activity and periods of sitting and / or inactivity. Figure 7A In the example of FIG, the plurality of orientation vectors 702 represent three days of X, Y, Z accelerometer measurements of patient 2. The orientation and activity circuitry 86 may associate each of the plurality of orientation vectors 702 with an activity level of patient 2, such as with respect to FIG. Figure 8 Described in more detail. Figures 7A to 7C As shown, orientation vector 702 is generally along sagittal plane 712 and transverse plane 714 .

[0095] As described in greater detail herein, orientation calibration circuitry 92 executes a clustering algorithm to identify a cluster of orientation vectors 710 associated with the patient's upright posture. Orientation calibration circuitry 92 processes the cluster of orientation vectors 710 to identify an upright vector that indicates the standing posture of patient 2. Orientation calibration circuitry 92 processes orientation vectors 710 to define a sagittal plane 712 and a transverse plane 714.

[0096] As an example for defining the sagittal plane 712 and the transverse plane 714, the orientation calibration circuit system 92 can consider 1,142 potential planes defined by their normal vectors. This can include up to 500 million possible combinations. The orientation calibration circuit system 92 limits the distance of each plane to the upright vector. For example, considering an orientation vector N, where the angle between N and the upright vector is greater than 70 degrees and less than 110 degrees, the orientation calibration circuit system 92 classifies the orientation vector N as belonging to a subset of orientation vectors associated with the upright posture of the patient 2. In the case where the angle between N and the upright vector is less than 45 degrees, the orientation calibration circuit system 92 classifies the orientation vector N as belonging to a subset of orientation vectors associated with the lying posture of the patient 2. The orientation calibration circuit system 92 can perform a culling operation to remove redundant upright orientation vectors. In some instances, more than 37,000 redundant combinations can be removed. The orientation calibration circuit system 92 calculates the mean square error (MSE) of the remaining 367 planes and retains only plane pairs with low error covariance. This can result in approximately 5,000 plane pair combinations. Orientation calibration circuitry 92 fits sensed orientation vectors 702 to two planes using MSE, resulting in defined orientations for sagittal plane 712 and transverse plane 714. Once orientation calibration circuitry 92 has defined sagittal plane 712 and transverse plane 714, orientation calibration circuitry 92 classifies each of the plurality of orientation vectors 702 as belonging to a subset of orientation vectors associated with an upright posture of patient 2 or as belonging to a subset of orientation vectors associated with a recumbent posture of patient 2 based on the orientation vector's proximity to one of sagittal plane 712 and transverse plane 714.

[0097] Orientation calibration circuitry 92 may use upright vector 703, sagittal plane 712, and transverse plane 714 to generate an estimate of the posture of patient 2, such as estimate 703 of the patient's standing posture, estimate 704 of the patient's prone posture, estimate 706A of the patient's right side lying posture and estimate 706B of the patient's left side lying posture, or estimate 708 of the patient's supine posture. Estimates 703, 704, 706A-706B, and 708 may then be used as reference points by orientation and activity circuitry 86 for determining the posture of patient 2.

[0098] Figure 8 8 is a diagram depicting an example transition from a rest period 806A to an activity period 806B for determining an upright posture vector according to techniques of this disclosure. As described above, IMD 10 may use the activity counts of patient 2 to determine the patient's activity level. Figure 8 As shown, Patient 2 transitions from a rest period 806A to an activity period 806B to a strenuous activity period 806C. Rest period 806A can be characterized as a low level of activity for Patient 2, such as sitting, resting, or sleeping, which is represented by a low value for the activity count. Activity period 806B can be characterized as an activity level for Patient 2, which corresponds to light or moderate activity, such as walking or standing, which is represented by a medium value for the activity count. Strenuous activity period 806C can be characterized as an activity level for Patient 2, which corresponds to a high level of physical exercise, such as running, jogging, or performing strenuous activities, which is represented by a high value for the activity count. Figure 8 As shown, the activity count for patient 2 may exhibit two inflection points 802 and 804. Inflection point 802 occurs between a rest period 806A and an activity period 806B, and inflection point 804 occurs between the activity period 806B and the vigorous activity period 806C. Inflection points 802 and 804 may be determined by a clinician and used to define predetermined thresholds for orientation and activity circuitry 86 to determine an activity level for patient 2 associated with a particular activity count.

[0099] As described herein, IMD 10 may sense multiple orientation vectors of patient 2. IMD 10 may simultaneously determine an activity level of patient 2 based on the activity counts of patient 2. IMD 10 may associate each orientation vector in multiple orientation vectors 702 with an activity level of patient 2. In some examples, IMD 10 determines an upright vector for determining a reference orientation of IMD 10 relative to the body of patient 2. The upright vector may be calculated from an average of the multiple orientation vectors corresponding to activity period 806B.

[0100] In some instances, periods of high intensity activity, such as strenuous activity period 806C, may be associated with artifacts. In an example embodiment, the accelerometer of the orientation and activity sensor 87 that senses multiple orientation vectors may have a maximum measurable limit of -3G to +3G (where "G" refers to 1 unit of Earth's gravity). High intensity activity during strenuous activity period 806C, such as running, jumping, climbing stairs, or posture transitions, may exceed the maximum measurable limit of the accelerometer of the orientation and activity sensor 87, resulting in saturation or "orbital change" of the accelerometer. Although the accelerometer is saturated due to the high intensity activity, the values ​​sensed by the orientation and activity sensor 87 may be inaccurate or subject to error, resulting in a poor estimate of the posture of the patient 2.

[0101] To reduce the likelihood that periods of high-intensity activity may cause errors in the calculated average of the plurality of orientation vectors, IMD 10 may apply a high-intensity activity filtering operation to the plurality of orientation vectors sensed for patient 2. For example, IMD 10 may reject orientation vectors sensed concurrently with periods of high-intensity activity, such as period of vigorous activity 806C. Thus, IMD 10 may only use orientation vectors sensed concurrently with "smooth" regions of activity level to calculate patient 2's upright vector and / or patient 2's posture.

[0102] IMD 10 may implement a high-intensity activity filtering operation to reject orientation vectors sensed concurrently with periods of high-intensity activity in a variety of ways, as described herein. For example, IMD 10 may apply a simple absolute or relative threshold to the determined activity level to reject orientation vectors sensed concurrently with activity exceeding the threshold. The threshold may be, for example, a maximum value of activity counts or a maximum value of raw data sensed by an accelerometer of orientation and activity sensor 87. As another example, IMD 10 may reject orientation vectors sensed concurrently with instances of "orbital variation" or saturation of the accelerometer of orientation and activity sensor 87.

[0103] As another example, IMD 10 may apply a more complex formula to reject orientation vectors sensed concurrently with periods of high intensity activity. As an example embodiment, IMD 10 determines an activity count for an activity count integration window. IMD 10 determines the temporal stability of orientation vectors sensed concurrently with the activity count integration window. IMD 10 may reject orientation vectors that exhibit low temporal stability during the activity count integration window, such as temporal stability less than a predetermined threshold. For example, IMD 10 applies a low-pass filter to the sensed X, Y, Z accelerometer values ​​of orientation and activity sensor 87. IMD 10 applies a mathematical function to the sensed X, Y, Z accelerometer values ​​to determine the variance of the sensed X, Y, Z accelerometer values. The mathematical function may include, for example, a large linear trend, a maximum-minimum value, a sum of absolute differences, or other functions. IMD 10 rejects orientation vectors sensed concurrently with the sensed X, Y, Z accelerometer values ​​of orientation and activity sensor 87 that exhibit high variance.

[0104] Thus, according to the techniques of this disclosure, IMD 10 may use an adaptive algorithm to automatically identify anatomical planes of patient 2 using sensor measurements. The sensor measurements may be, for example, from one or more 3-axis accelerometers located within IMD 10, such as Figure 1 The accelerometer measurements are taken by an external device such as external device 12 or an accelerometer externally attached to the body of patient 2. The disclosed techniques eliminate the need for a clinician to manually calibrate the sensors of IMD 10 to body locations after implantation of IMD 10.

[0105] The algorithm for automatically identifying anatomical planes of patient 2 using sensor measurements performed by IMD 10 can be described as having two components. First, IMD 10 identifies an upright vector corresponding to an upright posture of patient 2. Second, IMD 10 classifies a plurality of orientation vectors sensed for patient 2 as indicating a posture of patient 2, such as upright, prone, supine, lying on the left side, or lying on the right side.

[0106] IMD 10 can identify the upright vector corresponding to the upright posture of patient 2 in different ways. As an example, IMD 10 uses activity counts to identify the upright vector. In this example, IMD 10 senses multiple X, Y, Z accelerometer measurements, described herein as "orientation vectors," that correspond to periods of high intensity activity for patient 2. IMD 10 applies a low pass filter to the accelerometer measurements and averages the measurements over one or more days to obtain an average vector. IMD 10 uses the average vector as the upright vector. As described herein, "high intensity activity" is defined as an integrated activity count value greater than an activity threshold, e.g., Figure 8 One of the inflection points 802 and 804.

[0107] The integration period for activity counts can be in the range of about 2 seconds to about 5 minutes. It is assumed that specificity for upright activity peaks at a duration corresponding to a minimum length of sustained activity of about 10 seconds. Longer durations of activity can be obtained by summing consecutive 2-second activity periods. The IMD 10 averages the low-pass accelerometer measurements within the same integration window as the activity counts. Thus, each N-second activity sum corresponds to an N-second orientation vector average.

[0108] In some examples, IMD 10 continuously averages the activity counts and orientation vectors (e.g., via an infinite impulse response (IIR) filter with an appropriate time constant) every day or every N days. Given the number of events detected per minute or day, IMD 10 can receive feedback from the clinician or patient 2 regarding the threshold used to detect activity periods in patient 2 in order to increase or decrease the sensitivity of detecting activity periods in patient 2.

[0109] As another example, IMD 10 uses step counts or walking detections of patient 2 to identify an upright vector. In this example, IMD 10 senses multiple X, Y, Z accelerometer measurements that correspond to detected walking periods of patient 2. IMD 10 applies a low-pass filter to the accelerometer measurements and averages the measurements over one or more days to obtain an average vector. IMD 10 uses the average vector as the upright vector. For example, IMD 10 detects walking by a stride detection algorithm applied to raw accelerometer or integrated activity counts. For example, IMD 10 may interpret a consistent pattern in 2-second integrated activity counts over several cycles as corresponding to "walking" with a high degree of specificity.

[0110] IMD 10 may continuously average multiple walking sessions (e.g., via an infinite impulse response (IIR) filter with an appropriate time constant) every day or every N days. Given the number of events detected per minute or day, IMD 10 may receive feedback from the clinician or patient 2 regarding the threshold used to detect walking sessions in order to increase or decrease the sensitivity of detecting walking sessions for patient 2.

[0111] As another example, IMD 10 can be configured to communicate with Figure 1The IMD 10 interfaces with an external device, such as an external device 12, to identify the upright vector to confirm the detected posture of the patient 2. For example, in response to receiving a communication from the external device 12 confirming that the patient 2 was upright at a particular time (or previously), the IMD 10 updates the calculated upright vector based on contemporaneous low-pass filtered accelerometer measurements. In some examples, the upright vector is a weighted average of multiple accelerometer measurements, each accelerometer measurement coincident with the time when the external device 12 confirmed that the patient 2 was upright. For example, the external device 12 can be a vehicle that confirms that the patient 2 is in a sitting position (e.g., while driving), a body scale that confirms that the patient 2 is standing on the scale, an application executed on the mobile device that initiates periodic guided calibration with the patient 2, or a mobile device that detects walking through stride detection software and confirms that the patient 2 assumes a standing or walking posture.

[0112] IMD 10 may use a variety of methods to classify the multiple orientation vectors sensed for patient 2 as indicating the posture of patient 2. As an example, IMD 10 may classify the orientation vectors as indicating the posture of patient 2 based on the angle formed by each orientation vector and a calculated upright vector. IMD 10 senses the orientation vectors of patient 2 according to a periodic scale, such as every 2 seconds, every 5 minutes, etc. If the angle formed by the orientation vector and the upright vector is less than or equal to a predetermined upright angle, IMD 10 may interpret the orientation vector as indicating an upright posture. If the angle formed by the orientation vector and the upright vector is greater than the predetermined upright angle, IMD 10 may interpret the orientation vector as indicating a lying posture. In some examples, the predetermined upright angle is 20 degrees. In some examples, the predetermined upright angle is 45 degrees.

[0113] Although using the angle formed by the orientation vector and the upright vector to indicate posture may be easy to implement, this method can only determine whether the patient 2 is upright or lying down. In order to perform more accurate patient parameter data monitoring and / or treatment delivery, the posture of the patient 2 requires additional granularity.

[0114] As another example, IMD 10 may classify an orientation vector as indicating a posture of patient 2 based on the orientation vector's proximity to one of two planes (e.g., a sagittal plane and a transverse plane) that fit to the sensed orientation vector. In this example, IMD 10 stores all or a subset of the orientation vectors (e.g., an average of low-pass filtered X, Y, Z accelerometer measurements) and the corresponding activity values. The orientation vectors and activity values ​​are calculated over the same N time intervals and over a period of several days. In some examples, the orientation vector and activity value data are stored external to IMD 10, such as in a cloud computing infrastructure, or in a computer system. Figure 4 in one of the external server 164 and the computing device 170 .

[0115] IMD 10 fits a transverse plane to the cluster of orientation vectors associated with the upright posture of patient 2. As described herein, the terms transverse plane and "upright plane" are used interchangeably. As an example, to reduce the number of orientation vectors required for analysis, IMD 10 downsamples the plurality of sensed orientation vectors by associating each vector with a grid element on a sphere. In some instances, IMD 10 tracks multiple vectors for each grid element. IMD 10 retains only grid elements that have at least one associated orientation vector. IMD 10 classifies all grid elements within a predetermined upright angle of the upright vector as belonging to a subset of grid elements. In some instances, the predetermined upright angle is 20 degrees.

[0116] The IMD 10 applies a clustering algorithm to expand the subset of grid elements into clusters of grid elements that include nearby neighbors. In some instances, the cluster of grid elements has an angular separation of less than 10 degrees from grid elements in the subset of grid elements. In some instances, the IMD 10 retains only grid elements in the cluster of grid elements that have no more than one additional observation compared to a grid element in the cluster of grid elements to which the grid element is compared. This constraint eases merging of different clusters (e.g., the clustering algorithm is constrained to move from a low-population area to a high-population area). Because the torso of patient 2 typically rotates forward and backward more than left and right during normal activities of daily life, the major axis of the resulting cluster of grid elements typically falls along the transverse plane.

[0117] The IMD 10 computes the principal axis of variation within the cluster of grid elements by determining the eigenvector with the largest eigenvalue. The IMD 10 weights the covariance matrix of the cluster of grid elements by the number of corresponding orientation vectors (e.g., the number of associated observations). The IMD 10 computes the normal vector to the transverse plane by the cross product of the upright vector and the principal eigenvector. The normal vector to the transverse plane is perpendicular to the transverse plane and can be considered to define the orientation of the transverse plane.

[0118] The resulting vector roughly points to one of the two average posture vectors (e.g., lying on the left side or lying on the right side). In order to unambiguously classify the lying posture, the normal vector of the transverse plane must point to a single known vector. As an arbitrary convention used here, this known vector is chosen to be lying on the right side. However, lying on the left side can also be used. In other words, the characteristic vector is assumed to point to the average supine vector of patient 2.

[0119] IMD 10 can assign the correct polarity to the transverse plane by implementing the assumption that a rearward leaning posture (e.g., toward a supine posture) is more prevalent than a forward leaning posture (e.g., toward a prone posture). IMD 10 projects grid elements that are: 1) outside the upper cluster of grid elements; and 2) less than 45 degrees from transverse plane to transverse plane. The average of these projections, weighted by the number of corresponding orientation vectors, points to the more prevalent of the two reclining / lying postures, which the disclosed technique assumes to be the supine position. IMD 10 fixes the polarity of the normal vector to the transverse plane by recalculating the normal vector via the cross product of the upright vector and the preferred lying vector.

[0120] In addition, IMD 10 fits a sagittal plane to the orientation vectors that are not included in the cluster of grid elements associated with the upright posture of patient 2. As described herein, the terms sagittal plane and "recumbent plane" may be used interchangeably. IMD 10 classifies each orientation vector that is not included in the cluster of grid elements associated with the upright posture of patient 2 as belonging to a second subset of orientation vectors. IMD 10 calculates a cross product of each orientation vector in the second subset of orientation vectors with every other orientation vector in the second subset of orientation vectors. IMD 10 flips the sign of any resulting cross product where its plane forms a negative angle with the upright vector.

[0121] The equation for obtaining the resulting cross product is set forth below, where n is the first orientation vector in the second subset of orientation vectors and u is the second orientation vector in the second subset of orientation vectors:

[0122]

[0123] The IMD 10 calculates a weighted average of the resulting cross products. The weighted average is the estimated normal vector to the sagittal plane. The normal vector to the sagittal plane is perpendicular to the sagittal plane and can be considered to define the orientation of the sagittal plane.

[0124] IMD 10 determines the angle of each grid element relative to the transverse and sagittal planes. In some instances, the angles are continuous values ​​that IMD 10 can use to improve tracking of patient posture over time relative to discrete posture classes. For example, if the body angle of patient 2 changes from 5 degrees to 25 degrees during sleep, IMD 10 can determine that patient 2 has assumed different postures. IMD 10 classifies each orientation vector as representing a discrete posture class based on its proximity to each of the transverse and sagittal planes. In some instances, the posture classes include upright, supine, prone, left side lying, and right side lying.

[0125] To associate orientation vectors with an upright posture, IMD 10 identifies those orientation vectors as indicating an upright posture with a high degree of probability. Such highly probable upright vectors are classified as meeting all of the following criteria:

[0126] A OU <45°

[0127] ●|A OT -90°|<45°

[0128] ●|A OT -90°|<90°-A OS

[0129] Among them A OU is the angle between the orientation vector and the vertical vector, A OT is the angle formed by the orientation vector and the normal vector to the transverse plane, and A OS is the angle formed by the orientation vector and the normal vector to the sagittal plane.

[0130] IMD 10 may expand the classification of "indicating an upright posture" to include orientation vectors that are near an orientation vector that indicates an upright posture with a high degree of probability. For example, if a majority of orientation vectors near an unclassified orientation vector are themselves classified as indicating an upright posture, IMD 10 may classify the unclassified orientation vector as indicating an upright posture. In some examples, two orientation vectors are considered "nearby" or "adjacent" if they have an angular separation of less than 20 degrees.

[0131] The IMD 10 may classify the remaining unclassified orientation vectors according to the lying posture. For example, the IMD 10 projects the remaining unclassified orientation vectors onto the sagittal plane. The IMD 10 calculates an estimated supine vector from the cross product of the normal vector of the transverse plane and the normal vector of the sagittal plane. The IMD 10 projects the estimated supine vector onto the sagittal plane. For each projected orientation vector, the IMD 10 calculates the angle between the projection and the projection of the supine vector.

[0132] If the angle between the orientation vector projected onto the sagittal plane and the projection of the supine vector is less than 45 degrees, then IMD 10 classifies the orientation vector as indicating a supine posture. If the angle between the orientation vector projected onto the sagittal plane and the projection of the supine vector is greater than 135 degrees, then IMD 10 classifies the orientation vector as indicating a prone posture.

[0133] If the angle between the orientation vector projected onto the sagittal plane and the projection of the supine vector is greater than or equal to 45 degrees and less than or equal to 135 degrees, and if the angle between the orientation vector and the transverse plane is less than 0 degrees, then the IMD 10 classifies the orientation vector as indicating a left lateral decubitus position. If the angle between the orientation vector projected onto the sagittal plane and the projection of the supine vector is greater than or equal to 45 degrees and less than or equal to 135 degrees, and if the angle between the orientation vector and the transverse plane is greater than or equal to 0 degrees, then the IMD 10 classifies the orientation vector as indicating a right lateral decubitus position. The relationship between the angle between the orientation vector and the transverse plane and the left or right lateral decubitus position is described by the following equation:

[0134]

[0135] Thus, using the foregoing method, IMD 10 can automatically calibrate a reference orientation of IMD 10 within patient 2 and accurately detect the posture of patient 2 without requiring periodic manual recalibration.

[0136] Figures 9A to 9H is a diagram illustrating example patient posture data obtained according to the techniques of this disclosure. Figures 9A to 9H As shown, it will be Figure 1 IMD 10A, Figure 2 IMD 10 or Figures 5A to 5C An IMD similar to the IMD 10B was implanted in 49 subjects and the technology of the present disclosure was studied. Three reference data sets were recorded for each subject. Each reference data set was 1 to 3 months apart and contained standing, supine, left side lying, and right side lying postures. The IMD recorded raw X, Y, and Z accelerometer values ​​for each subject every 5 minutes. For this study, the raw accelerometer values ​​were calibrated and normalized to a unit orientation vector. In addition, the implanted IMD recorded the average and maximum activity counts every 5 minutes for each subject. The IMD used 3 days of accelerometer data to estimate the reference orientation of the IMD relative to the subject. The actual orientation was measured and compared to the reference orientation estimated by the IMD from the transmission date.

[0137] Figure 9A is a graph showing the estimated error between measured orientations compared to a reference orientation estimated by IMD. Figure 9A The reference orientation errors of the estimated reference orientation vectors for upright, supine, left lateral decubitus, and right lateral decubitus postures are plotted.

[0138] Figure 9B is a graph showing the distance of a reference orientation estimated by the IMD to the transverse (eg, upright) plane or the sagittal (eg, lying down) plane. Figure 9ADepicted are the distance from the upright reference orientation to the transverse (e.g., upright) plane, the distance from the supine reference orientation to the sagittal (e.g., lying down) plane, the distance from the left lateral decubitus reference orientation to the sagittal (e.g., lying down) plane, and the distance from the right lateral decubitus reference orientation to the sagittal (e.g., lying down) plane.

[0139] Figure 9C is a diagram illustrating an orthogonal normal vector according to the technology of the present disclosure. Specifically, Figure 9C The X, Y, and Z values ​​of the estimated upright vector of the subject are shown over time. Figure 9C It can be inferred that as the position of the IMD within the subject changes over time, the X component of the estimated upright vector also changes over time.

[0140] Figure 9D and 9E Shown are simulated data from 9 healthy subjects converted to a 1-minute sampling period. Figure 9D and 9E = represents a sampling period of approximately 20 hours per subject, including sleep time. Figure 9D and 9E As shown, the rotation plane estimated by the IMD operating according to the techniques of this disclosure can distinguish body positions well.

[0141] Figure 9D is a graph showing the estimated error between measured orientations compared to a reference orientation estimated by IMD. Figure 9D The reference orientation errors of the estimated reference orientation vectors for upright, supine, left lateral decubitus, and right lateral decubitus postures are plotted.

[0142] Figure 9E is a graph showing the distances of a reference orientation estimated by the IMD to the transverse (eg, upright) plane and the sagittal (eg, lying down) plane. Figure 9E The distances to the transverse and sagittal planes for the upright reference orientation, the supine reference orientation, the left lateral decubitus reference orientation, and the right lateral decubitus reference orientation are depicted.

[0143] Figure 9F is a graph showing the activity counts of a simulated patient compared to the X, Y, Z values ​​of the subject's estimated upright vector.

[0144] Figure 9G is a graph showing the distance of a reference orientation estimated by the IMD to the transverse (eg, upright) plane or the sagittal (eg, lying down) plane. Figure 9GDepicted are the distance from the upright reference orientation to the transverse (e.g., upright) plane, the distance from the supine reference orientation to the sagittal (e.g., lying down) plane, the distance from the left lateral decubitus reference orientation to the sagittal (e.g., lying down) plane, and the distance from the right lateral decubitus reference orientation to the sagittal (e.g., lying down) plane.

[0145] Figure 9H is a graph showing a reference orientation estimated by an IMD compared to a measured orientation of the IMD. Figure 9H The correlation between several types of methods for estimating the reference orientation of the IMD 10 is depicted. Figure 9H As shown, the estimated upright vectors are highly correlated with each other (e.g., r=0.95). In addition, the estimated upright vectors are well correlated with the measured reference orientation. For example, the exponential mean is determined to be r=0.82, and the change point threshold is determined to be r=0.80.

[0146] Thus, the techniques of this disclosure can allow an IMD to estimate the rotational plane of a patient's body in order to obtain accurate estimates for multiple body postures. While in some instances, a moving average of the upright vector as described above may be sufficient to estimate a reference vector for the patient's upright posture, more sophisticated methods of estimating reference points by fitting orientation vectors to the transverse and sagittal planes described above can allow the IMD to estimate reference vectors for other postures, such as distinguishing between left-side and right-side lying postures or distinguishing between supine and prone postures.

[0147] Figure 10 is a flow chart illustrating an example operation according to the technology of the present disclosure. Figure 1 IMD 10A Description Figure 10 However, Figure 10 The operation can be performed by Figure 2 IMD 10 or Figures 5A to 5C Other medical devices such as the IMD 10B. Figure 10 The operations of may be performed at least in part by a computing device, such as external device 12, in communication with the IMD, or by processing circuitry of any one or more coordinating devices and other circuitry described herein.

[0148] IMD 10A senses a plurality of orientation vectors relative to the Earth's gravitational field over a period of time (1002). Typically, each orientation vector points in a direction opposite to the direction of the Earth's gravitational field. Thus, the orientation vectors can be assumed to point "up" and can be used as reference points to determine the current orientation of IMD 10 relative to the Earth's gravitational field. In some examples, IMD 10 includes one or more accelerometers, such as a three-axis accelerometer, capable of detecting a static orientation or a three-dimensional vector.

[0149] IMD 10A processes the plurality of orientation vectors to identify an upright vector (1004). The upright vector may correspond to an upright posture of patient 2, such as standing or sitting. The disclosed techniques assume that the patient is in an upright posture for most of the 24-hour period (or for most of the daytime). Therefore, the average vector of the plurality of orientation vectors may be assumed to roughly correspond to the patient's upright posture. However, the upright vector may be made more precise by filtering the plurality of orientation vectors to obtain a set of vectors associated with times of the day when patient 2 is expected to be active or with a high activity level of patient 2.

[0150] As one example for determining the upright vector, IMD 10A senses a plurality of orientation vectors of IMD 10A relative to a gravitational field over a period of time. IMD 10A determines one or more orientation vectors from the plurality of orientation vectors that correspond to an activity level of patient 2 that exceeds a patient activity threshold. IMD 10A may average the one or more orientation vectors corresponding to the activity level of patient 2 that exceeds the patient activity threshold, such that the average vector corresponds to the upright vector.

[0151] As another example for determining the upright vector, IMD 10A senses multiple orientation vectors of IMD 10A relative to a gravitational field over a period of time. IMD 10A determines one or more of the multiple orientation vectors that correspond to the patient's walking activity. For example, IMD 10A may use a stride detector or pedometer to determine when patient 2 is walking and select only those orientation vectors sensed concurrently with the patient's walking activity. IMD 10A may average the one or more orientation vectors corresponding to patient 2's activity level exceeding a patient activity threshold, such that the average vector corresponds to the upright vector.

[0152] As another example of determining the upright vector, an external device, such as external device 12, may determine the posture of patient 2 and transmit the determined posture to IMD 10A. For example, external device 12 may receive input from the patient indicating the posture that patient 2 has assumed. Additionally, external device 12 may include a pedometer, an accelerometer, or other type of sensor capable of detecting the posture of patient 12. IMD 10A senses a plurality of orientation vectors of IMD 10A relative to a gravitational field over a period of time. IMD 10A receives, from external device 12, the posture of patient 2 determined for the sensed orientation vectors. IMD 10A determines one or more orientation vectors of the plurality of orientation vectors that correspond to the patient's upright posture based on the sensed orientation vectors associated with the various postures of patient 2. IMD 10A may average the one or more orientation vectors corresponding to an activity level of patient 2 that exceeds a patient activity threshold, such that the average vector corresponds to the upright vector.

[0153] IMD 10A classifies the plurality of orientation vectors to define a transverse plane (1006). IMD 10A classifies the plurality of orientation vectors relative to the upright vector to define the transverse plane of patient 2. The transverse plane of patient 2 is a conceptual plane that roughly divides the body of patient 2 into an upper portion and a lower portion. The disclosed technology assumes that the sensed orientation vectors sensed above and below the transverse plane of patient 2 can correspond to the upright position or the lying position of patient 2, respectively.

[0154] To determine the transverse plane, IMD 10A classifies each sensed orientation vector and determines the angle between the orientation vector and the upright vector. IMD 10A classifies and compares the angle between the orientation vector and the upright vector to a predetermined upright angle. For example, the predetermined upright angle may differ from the upright angle by approximately 20 degrees. If the angle between the orientation vector and the upright vector is less than the predetermined upright angle, IMD 10A classifies the corresponding orientation vector as one of a first subset of multiple orientation vectors associated with the patient's upright posture. If the angle between the orientation vector and the upright vector is greater than or equal to the predetermined upright angle, IMD 10A classifies the corresponding orientation vector as one of a second subset of multiple orientation vectors associated with the patient's supine posture. IMD 10A defines the patient's transverse plane as a plane between the first subset of multiple orientation vectors associated with the patient's upright posture and the second subset of multiple orientation vectors associated with the patient's supine posture.

[0155] Furthermore, IMD 10A classifies the plurality of orientation vectors to define a sagittal plane of patient 2 (1008). The sagittal plane of patient 2 is a conceptual plane that roughly divides the body of patient 2 into left and right halves. The disclosed techniques assume that sensed orientation vectors directed to the left and right sides of the sagittal plane of patient 2 correspond to the patient lying on the left or right side of the body (e.g., in a left-lying or right-lying posture).

[0156] As one example for determining the sagittal plane, IMD 10A associates each orientation vector with a mesh element of a sphere based on the angle formed by the orientation vector relative to the upright vector. IMD 10A identifies a subset of the plurality of mesh elements that form an angle with the upright vector that is less than a predetermined upright angle. In some examples, the predetermined upright angle is 20 degrees. IMD 10A applies a clustering algorithm to identify a cluster of mesh elements from the plurality of mesh elements that are adjacent to the subset of the plurality of mesh elements. In some examples, a mesh element is a neighbor of the subset of the plurality of mesh elements if its offset from a mesh element in the subset of the plurality of mesh elements is less than 10 degrees. IMD 10A determines a primary eigenvector for the cluster of mesh elements. The primary eigenvector includes an eigenvalue that is higher than every other eigenvector in the plurality of eigenvectors of the cluster of mesh elements. IMD 10A determines a normal vector based on the cross product of the upright vector and the primary eigenvector, the normal vector defining the sagittal plane of patient 2, wherein the normal vector is perpendicular to the sagittal plane.

[0157] As another example for determining the sagittal plane, IMD 10A classifies each orientation vector as either one of a first subset of a plurality of orientation vectors associated with the patient's upright posture or one of a second subset of a plurality of orientation vectors associated with the patient's supine posture. IMD 10A calculates a plurality of cross products between each orientation vector in the first subset of the plurality of orientation vectors associated with the patient's upright posture and each orientation vector in the second subset of the plurality of orientation vectors associated with the patient's supine posture. IMD 10A determines a weighted average of each cross product to obtain a normal vector defining the sagittal plane of patient 2.

[0158] The IMD 10A determines a reference orientation of the IMD 10A relative to the body of patient 2 based on the upright vector, the transverse plane, and the sagittal plane (1010). The IMD 10A senses the patient's current orientation vector relative to the gravitational field (1012). The IMD 10A determines the patient's posture (1014) based on the relationship between the current orientation vector and the reference orientation. Generally speaking, if the orientation vector is similar to the upright vector and is above the transverse plane, the IMD 10A determines that the patient 2 is in an upright posture. If the orientation vector is not similar to the upright vector, is below the transverse plane, and is positioned approximately along the sagittal plane, the IMD 10A determines that the patient 2 is in a prone or supine position. If the orientation vector is not similar to the upright vector, is positioned approximately along the transverse plane, and is to the left or right of the sagittal plane, the IMD 10A determines that the patient 2 is in a left lateral position or a right lateral position. Additional details of evaluating the orientation vector relative to the upright vector, the transverse plane, and the sagittal plane are described in more detail below.

[0159] Due to some inherent sensor inaccuracies and modeling errors, the transverse plane constructed by IMD 10A may or may not be coplanar with the actual true sagittal plane of patient 2. Furthermore, the sagittal plane constructed by IMD 10A may or may not be coplanar with the actual true transverse plane of patient 2. Furthermore, the transverse and sagittal planes constructed by IMD 10A may not necessarily be perpendicular to one another (as are the actual true transverse and sagittal planes of patient 2). However, an advantage of the disclosed techniques is that IMD 10A can still accurately calibrate the orientation of IMD 10A relative to the body of patient 2, such that IMD 10A can accurately detect the posture of patient 2 even if the transverse and sagittal planes of patient 2 calculated by orientation calibration circuitry 92 are not perpendicular to one another or coplanar with the actual true transverse and sagittal planes of patient 2.

[0160] Figure 11 is a flow chart illustrating an example operation according to the technology of the present disclosure. Figure 1 IMD 10A Description Figure 10 However, Figure 10 The operation can be performed by Figure 2 IMD 10 or Figures 5A to 5C Specifically, Figure 11 Example operations are shown for determining the upright vector of IMD 10. As described in more detail below, IMD 10A sets an activity threshold based on an average activity count of patient 2, determines whether a sensed orientation vector should be incorporated into a subset of orientation vectors used to estimate the upright vector based on the current activity count, and then estimates the current upright vector based on a moving average of the subset of orientation vectors consistent with periods of high intensity activity of the patient.

[0161] IMD 10A determines an activity count for patient 2 (1102). The activity count may be used to indicate the activity or activity level of patient 2. For example, IMD 10A sums the amplitude of the AC portion of the accelerometer signal for N consecutive samples. For example, assuming sampling occurs at 25 Hz, N may be set to 25 so that the counting logic provides the sum of the samples obtained in one second. This sum may be referred to as an "activity count." When the activity count for patient 2 exceeds a first predetermined level, IMD 10A may determine that patient 2 has transitioned from a resting state to an active state.

[0162] In some instances, IMD 10A applies a high-intensity activity filter to patient 2's activity count (1103). For example, IMD 10 may compare patient 2's current activity count to a second predetermined level. If patient 2's current activity count exceeds the second predetermined level, IMD 10 discards orientation vectors sensed concurrently within the time interval of the current activity count. In some instances, periods of high-intensity activity may be associated with artifacts. For example, high-intensity activity by patient 2, such as running, jumping, or climbing stairs, may exceed the maximum measurable limit of the accelerometer of orientation and activity sensor 87, resulting in accelerometer saturation or "orbiting." Although the accelerometer is saturated due to the high-intensity activity, the values ​​sensed by orientation and activity sensor 87 may be inaccurate or subject to error, resulting in a poor estimate of patient 2's posture. By rejecting orientation vectors sensed concurrently with activity counts indicating periods of high-intensity activity for patient 2, IMD 10A may reduce errors due to periods of high-intensity activity for patient 2. Thus, IMD 10 may then calculate patient 2's upright vector and / or patient 2's posture using only orientation vectors sensed concurrently with the "plateau" region of activity level.

[0163] IMD 10A further calculates an exponential average of past activity counts for patient 2 (1104). IMD 10A compares the current activity count to the exponential average (1106). In response to determining that the current activity count is less than the exponential average of the activity counts (e.g., a "No" block of 1106), IMD 10A discards the orientation vectors contemporaneously sensed within the time interval of the current activity count (1108). In response to determining that the current activity count is greater than the exponential average of the activity counts (e.g., a "Yes" block of 1106), IMD 10A includes the orientation vectors contemporaneously sensed within the time interval of the current activity count in the subset of orientation vectors associated with the upright posture of patient 2 (1110).

[0164] In the foregoing examples, IMD 10A is described as maintaining a subset of orientation vectors associated with the upright posture of patient 2. However, in other examples, IMD 10A may use infinite impulse response (IIR) filtering to estimate the upright vector. The IIR filter may only require the previous filter output (e.g., a moving average of the previous orientation vector associated with high-intensity activity of patient 2) and the current orientation vector to calculate the next output of the IIR filter (e.g., an updated upright vector). Using an IIR filter can reduce the need for IMD 10A to store a large set of orientation vectors sensed for patient 2 in order to accurately estimate the upright vector.

[0165] IMD 10A calculates an exponential average of the orientation vectors in the subset of orientation vectors associated with the upright posture of patient 2 (1112). IMD 10A may use the exponential average of the orientation vectors as a reference upright vector. In other words, IMD 10A may classify the reference upright vector as indicating the upright posture of patient 2.

[0166] In some examples, the technology of this disclosure includes a system including means for performing any of the methods described herein. In some examples, the technology of this disclosure includes a computer-readable medium including instructions for causing processing circuitry to perform any of the methods described herein.

[0167] It should be understood that the various aspects disclosed herein may be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that, depending on the instance, certain actions or events in any of the processes or methods described herein may be performed in a different order, may be added, combined, or omitted entirely (e.g., all described actions or events may not be necessary for performing these techniques). In addition, although certain aspects of the present disclosure are described as being performed by a single module, unit, or circuit for clarity, it should be understood that the techniques of the present disclosure may be performed by a combination of units, modules, or circuit systems associated with, for example, a medical device.

[0168] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored in the form of one or more instructions or codes on a computer-readable medium and may be executed by a hardware-based processing unit. A computer-readable medium may include a non-transitory computer-readable medium, which corresponds to a tangible medium such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0169] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Thus, as used herein, the terms "processor" or "processing circuitry" may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Furthermore, the techniques may be fully implemented in one or more circuits or logic elements.

[0170] Item 1. A method comprising:

[0171] sensing, by one or more sensors of an implantable medical device (IMD), a plurality of orientation vectors of the IMD relative to a gravitational field;

[0172] processing, by processing circuitry of the IMD, the plurality of orientation vectors to identify an upright vector among the plurality of orientation vectors, the upright vector corresponding to an upright posture of a patient;

[0173] sorting, by the processing circuitry, the plurality of orientation vectors relative to the upright vector to define a transverse plane of the patient;

[0174] sorting, by the processing circuitry, the plurality of orientation vectors relative to the upright vector to define a sagittal plane of the patient; and

[0175] A reference orientation of the IMD is determined by the processing circuitry based on the upright vector, the transverse plane, and the sagittal plane.

[0176] Item 2. The method of item 1, wherein processing the plurality of orientation vectors to identify the upright vector in the plurality of orientation vectors comprises:

[0177] sensing the plurality of orientation vectors over a period of time;

[0178] determining one or more orientation vectors of the plurality of orientation vectors corresponding to an activity level of the patient that exceeds a patient activity threshold; and

[0179] The one or more orientation vectors are averaged to calculate the upright vector.

[0180] Item 3. The method of item 1, wherein processing the plurality of orientation vectors to identify the upright vector in the plurality of orientation vectors comprises:

[0181] sensing the plurality of orientation vectors over a period of time;

[0182] determining one or more orientation vectors of the plurality of orientation vectors corresponding to walking activity of the patient; and

[0183] The one or more orientation vectors are averaged to calculate the upright vector.

[0184] Item 4. The method of item 1, wherein processing the plurality of orientation vectors to identify the upright vector in the plurality of orientation vectors comprises:

[0185] sensing the plurality of orientation vectors over a period of time;

[0186] receiving, from an external device, an indication of the patient's posture for each of the plurality of orientation vectors;

[0187] determining, based on the indication of the patient's posture, one or more orientation vectors of the plurality of orientation vectors that correspond to an upright posture of the patient; and

[0188] The one or more orientation vectors are averaged to calculate the upright vector.

[0189] Item 5. The method of item 1, wherein classifying the plurality of orientation vectors to define the transverse plane of the patient comprises:

[0190] For each orientation vector in the plurality of orientation vectors:

[0191] determining an angle between the orientation vector and the upright vector;

[0192] comparing the angle between the orientation vector and the upright vector to a predetermined upright angle; and

[0193] Based on the comparison, classifying the orientation vector as one of a first subset of the plurality of orientation vectors associated with an upright posture of the patient or one of a second subset of the plurality of orientation vectors associated with a lying posture of the patient; and

[0194] The transverse plane of the patient is defined as a plane between the first subset of the plurality of orientation vectors associated with the upright posture of the patient and the second subset of the plurality of orientation vectors associated with the lying posture of the patient.

[0195] Item 6. The method of Item 5, wherein classifying the orientation vector as one of the first subset of the plurality of orientation vectors associated with the upright posture of the patient or as one of the second subset of the plurality of orientation vectors associated with the lying posture of the patient comprises:

[0196] responsive to determining that the angle between the orientation vector and the upright vector is less than the predetermined upright angle, classifying the orientation vector as one of the first subset of the plurality of orientation vectors associated with the upright posture of the patient; and

[0197] responsive to determining that the angle between the orientation vector and the upright vector is greater than or equal to the predetermined upright angle, classifying the orientation vector as one of the second subset of the plurality of orientation vectors associated with the lying posture of the patient;

[0198] Item 7. The method of item 1, wherein classifying the plurality of orientation vectors to define the sagittal plane of the patient comprises:

[0199] associating each orientation vector of the plurality of orientation vectors with a grid element of a plurality of grid elements of a sphere based on an angle formed by the orientation vector and the upright vector;

[0200] identifying a subset of the plurality of mesh elements, the subset of the plurality of mesh elements forming an angle with the upright vector that is less than a predetermined upright angle;

[0201] applying a clustering algorithm to identify a cluster of grid elements of the plurality of grid elements that are adjacent to the subset of the plurality of grid elements;

[0202] determining a dominant eigenvector for the cluster of mesh elements, wherein the dominant eigenvector comprises an eigenvalue that is higher than each other eigenvector in a plurality of eigenvectors for the cluster of mesh elements; and

[0203] A normal vector is determined based on a cross product of the upright vector and the principal eigenvector, wherein the normal vector defines the sagittal plane of the patient.

[0204] Item 8. The method of Item 1, wherein the reference orientation of the IMD is a reference orientation of the IMD within the patient, and

[0205] Wherein the method further comprises determining a posture of the patient based on the reference orientation of the IMD within the patient.

[0206] Item 9. The method of Item 8, wherein determining the posture of the patient comprises:

[0207] sensing, by the one or more sensors of the IMD, a current orientation vector of the patient relative to the gravitational field; and

[0208] The posture of the patient is determined by the processing circuitry based on a relationship of the current orientation vector to the reference orientation of the IMD.

[0209] Item 10. The method of Item 1, wherein the patient's defined transverse plane is not substantially perpendicular to the patient's defined sagittal plane.

[0210] Item 11. The method of item 1, wherein processing the plurality of orientation vectors to identify the upright vector in the plurality of orientation vectors comprises:

[0211] determining at least one orientation vector of the plurality of orientation vectors that is concurrent with a high intensity activity level of the patient; and

[0212] The at least one orientation vector is rejected to identify the upright vector among the plurality of orientation vectors.

[0213] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

1. An implantable medical device (IMD), comprising: one or more sensors configured to sense a plurality of orientation vectors of the IMD relative to a gravitational field; as well as processing circuitry configured to: processing the plurality of orientation vectors to identify an upright vector among the plurality of orientation vectors by applying a clustering algorithm to identify a cluster of orientation vectors associated with an upright posture of a patient and processing the cluster of orientation vectors, the upright vectors corresponding to the upright posture of the patient; sorting the plurality of orientation vectors relative to the upright vector to define a transverse plane of the patient; sorting the plurality of orientation vectors relative to the upright vector to define a sagittal plane of the patient; as well as A reference orientation of the IMD is determined based on the upright vector, the transverse plane, and the sagittal plane.

2. The IMD of claim 1 , wherein to process the plurality of orientation vectors to identify the upright vector among the plurality of orientation vectors, the processing circuitry is configured to: sensing the plurality of orientation vectors over a period of time; determining one or more orientation vectors of the plurality of orientation vectors corresponding to an activity level of the patient that exceeds a patient activity threshold; as well as The one or more orientation vectors are averaged to calculate the upright vector.

3. The IMD of claim 1 , wherein to process the plurality of orientation vectors to identify the upright vector among the plurality of orientation vectors, the processing circuitry is configured to: sensing the plurality of orientation vectors over a period of time; determining one or more orientation vectors of the plurality of orientation vectors corresponding to walking activity of the patient; as well as The one or more orientation vectors are averaged to calculate the upright vector.

4. The IMD of claim 3 , wherein to classify the plurality of orientation vectors to define the transverse plane of the patient, the processing circuitry is configured to: For each orientation vector in the plurality of orientation vectors: determining an angle between the orientation vector and the upright vector; comparing the angle between the orientation vector and the upright vector to a predetermined upright angle; as well as classifying the orientation vector as one of a first subset of the plurality of orientation vectors associated with an upright posture of the patient or as one of a second subset of the plurality of orientation vectors associated with a recumbent posture of the patient based on the comparison; as well as The transverse plane of the patient is defined as a plane between the first subset of the plurality of orientation vectors associated with the upright posture of the patient and the second subset of the plurality of orientation vectors associated with the lying posture of the patient.

5. The IMD of claim 4 , wherein to classify the orientation vector as one of the first subset of the plurality of orientation vectors associated with an upright posture of the patient or as one of the second subset of the plurality of orientation vectors associated with a lying posture of the patient, the processing circuitry is configured to: responsive to determining that the angle between the orientation vector and the upright vector is less than the predetermined upright angle, classifying the orientation vector as one of the first subset of the plurality of orientation vectors associated with an upright posture of the patient; and In response to determining that the angle between the orientation vector and the upright vector is greater than or equal to the predetermined upright angle, the orientation vector is classified as one of the second subset of the plurality of orientation vectors associated with the lying posture of the patient.

6. The IMD of claim 1 , wherein the reference orientation of the IMD is a reference orientation of the IMD within the patient, and Wherein the processing circuitry is further configured to determine a posture of the patient based on the reference orientation of the IMD within the patient.

7. The IMD of claim 6 , wherein to determine the posture of the patient, the processing circuitry is configured to: sensing, by the one or more sensors of the IMD, a current orientation vector of the patient relative to the gravitational field; and The posture of the patient is determined by the processing circuitry based on a relationship of the current orientation vector to the reference orientation of the IMD.

8. The IMD of claim 1 , wherein to process the plurality of orientation vectors to identify the upright vector among the plurality of orientation vectors, the processing circuitry is configured to: sensing the plurality of orientation vectors over a period of time; receiving, from an external device, an indication of the patient's posture for each of the plurality of orientation vectors; determining, based on the indication of the patient's posture, one or more orientation vectors of the plurality of orientation vectors that correspond to an upright posture of the patient; as well as The one or more orientation vectors are averaged to calculate the upright vector.

9. The IMD of claim 1 , wherein to classify the plurality of orientation vectors to define a sagittal plane of the patient, the processing circuitry is configured to: associating each orientation vector of the plurality of orientation vectors with a grid element of a plurality of grid elements of a sphere based on an angle formed by the orientation vector and the upright vector; identifying a subset of the plurality of grid elements, the subset of the plurality of grid elements forming an angle with the upright vector that is less than a predetermined upright angle; applying a clustering algorithm to identify a cluster of grid elements of the plurality of grid elements that are adjacent to the subset of the plurality of grid elements; determining a dominant eigenvector for the cluster of grid elements, wherein the dominant eigenvector comprises an eigenvalue that is higher than each other eigenvector in a plurality of eigenvectors for the cluster of grid elements; as well as A normal vector is determined based on a cross product of the upright vector and the principal eigenvector, wherein the normal vector defines the sagittal plane of the patient.

10. The IMD of claim 1, wherein the patient's defined transverse plane is not substantially perpendicular to the patient's defined sagittal plane.

11. The IMD of claim 1 , wherein to process the plurality of orientation vectors to identify the upright vector among the plurality of orientation vectors, the processing circuitry is configured to: determining at least one orientation vector of the plurality of orientation vectors that is concurrent with a high intensity activity level of the patient; and The at least one orientation vector is rejected to identify the upright vector among the plurality of orientation vectors.

12. A system comprising: one or more sensors configured to sense a plurality of orientation vectors of an implantable medical device (IMD) relative to a gravitational field; as well as processing circuitry configured to: processing the plurality of orientation vectors to identify an upright vector among the plurality of orientation vectors by applying a clustering algorithm to identify a cluster of orientation vectors associated with an upright posture of a patient and processing the cluster of orientation vectors, the upright vectors corresponding to the upright posture of the patient; sorting the plurality of orientation vectors relative to the upright vector to define a transverse plane of the patient; sorting the plurality of orientation vectors relative to the upright vector to define a sagittal plane of the patient; as well as A reference orientation of the IMD is determined based on the upright vector, the transverse plane, and the sagittal plane.

13. The system of claim 12, wherein the IMD comprises the processing circuitry.

14. The system of claim 12, wherein the system further comprises an external device comprising the processing circuitry.

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