Wearable activity tracker systems and methods

Abstract

A wearable electronic device includes a motion sensing unit, and a data storage. The wearable electronic device is configured to be worn by a human user. The motion sensing unit senses motion of the human user and outputs motion data according thereto. The wearable electronic device is configured to store the motion data with the data storage at a storage frequency that is less than two Hz. A system includes an analysis computing system and multiple of the wearable electronic devices, each associated with a different user. The analysis computing system receives the motion data from the data storage of each of the wearable electronic devices, and processes the motion data to output normalized metrics for each of the users. The normalized metrics are normalized to a normalization period. The normalization period may be one day.

Description

WEARABLE ACTIVITY TRACKER, SYSTEMS, AND METHODS CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 663,070, filed June 21, 2024, the entire disclosure of which is incorporated by reference herein.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under 5U44NS 122002 awarded by the National Institutes of Health (NIH) / National Institute of Neurological Disorders and Stroke (NINDS). The government has certain rights in the invention.TECHNICAL FIELD

[0003] This disclosure relates to assessing patient activity and, specifically, assessing patient activity objectively with motion data of the patient.BACKGROUND

[0004] Physicians often prescribe pain interventions to patients for pain management, for example, to manage pain associated with chronic conditions or after medical procedures. To assess the efficacy of the pain interventions, physicians are typically reliant on the patient providing feedback. However, patient feedback is subjective and, therefore, may be an unreliable and / or inaccurate indicator of the efficacy of the pain interventions.SUMMARY

[0005] Disclosed herein are implementations of wearable electronic devices, systems, and methods for assessing patient activity.

[0006] In an implementation, a wearable electronic device is provided for collecting motion data of patients for assessing activity thereof. The wearable electronic device generally includes a motion sensing unit, a data storage, a communications interface, a power source, and an electronics housing. The motion sensing unit senses motion and outputs motion data accordingthereto. The data storage receives and stores the motion data. The communications interface is for transferring the motion data from the data storage. The electronics housing is configured to be worn by a patient. The wearable electronic device is configured to sense motion of a human at a sensing frequency of less than 2 Hz and store the motion data at a storage frequency that is less than the sensing frequency.

[0007] In an implementation, a wearable electronic device includes a motion sensing unit, and a data storage. The wearable electronic device is configured to be worn by a human user. The motion sensing unit senses motion of the human user and outputs motion data according thereto. The data storage receives and stores the motion data. The wearable electronic device is configured to store the motion data at a storage frequency of less than 2 Hz. The motion sensing unit may be a three-axis accelerometer. The resultant vector of the three-axis accelerometer may be stored as the motion data. The motion data may be stored with a dynamic range of + / - 2g or less. The motion data may be stored in 8-bit format. The electronic device may not include an output device configured to output the motion data or values derived therefrom to the user.In an implementation, a system includes multiple of the wearable electronic devices according to any of the preceding claims and a computing system. The computing system is configured to, for each of the wearable electronic devices: receive the motion data from the storage of the wearable electronic device; generate one or more normalized metrics from the motion data for successive normalization periods; and output the normalized metrics to other computing systems associated with prescribers. The normalization periods may be 24 hours. The normalized metrics may include a normalized activity metric that is an average of absolute values of derivatives of the motion data. The normalized metrics may include a normalized active period metric during the normalization period in which magnitudes of the motion data is above an active threshold. The normalized metrics may include a normalized inactive period metric during the normalization period in which the magnitudes of the motion data is below an inactive threshold that is lower than the active threshold. The computing system may be further configured to output one or more aggregated metrics that is an average of the one or more normalized metrics over a total wear period. The computing system may be further configured to determine when the wearable electronic device is worn by the user.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity.

[0009] FIG. l is a side elevation view of a wearable electronic device with hidden components depicted in broken lines.

[0010] FIG. 2 is a schematic view of the wearable electronic device of FIG. 1.

[0011] FIG. 3 is a schematic of an example controller of the wearable electronic device ofFIG. 1

[0012] FIG. 4 is a flowchart of a first method for collecting motion data of a patient.

[0013] FIG. 5 is a schematic view of a system for analyzing patient activity that includes multiple of the wearable electronic devices of FIG. 2.

[0014] FIG. 6 is a flowchart of a method for processing motion data of multiple patients.DETAILED DESCRIPTION

[0015] Disclosed herein are devices, systems, and methods for collecting physiological data that, among other uses, may provide an objective indicator, used alone or in conjunction with other indicators, to assess efficacy of pain interventions. In particular, it is theorized that patient activity, which may be assessed from motion data measured with an accelerometer, may indicate efficacy of the pain interventions, for example, with higher levels of activity indicating greater efficacy of the pain interventions.

[0016] In one implementation, the devices may be configured as wearable electronic devices that collect motion data. The devices may be configured as limited-time use (e.g., one-time use) devices that are worn by a patient for a limited period of time and which may be constrained to such usage times and patient uses by power capacity, data storage capacity, and / or limitations on the manners in which the patient may interact with the device (e.g., patients are unable to transfer or otherwise access motion data). The patient may send the device to a processing facility that processes the device to transfer and assess the collected motion data and from which an activity assessment is generated and sent to the prescriber (e.g., a physician).

[0017] To provide as a one-time use device, as well as to avoid influencing behavior of thepatient, the wearable electronic device may be configured, for example, to receive few or no patient inputs, provide few or no patient outputs, and be physically compact and lightweight. Moreover, the electronic components are configured to reduce power and storage requirements as compared to conventional activity tracking devices, so as to lower the cost of the wearable electronic device. Still further, considering an intended use of the device is to provide physicians with high-level patient activity information (e.g., a daily average) to indicate efficacy of pain medication, the wearable electronic device may be optimized to collect, process, and / or store relatively little data while still providing sufficiently accurate and clear measures of patient activity, which in turn allows for relatively low sensing, processing, storage, and power requirements.

[0018] Referring to FIG. 1, a wearable electronic device 100 generally includes a band 110 and a module 120 coupled to the band 110. As shown, the band 110 is, for example, formed of a flexible elastomer. The module 120 generally includes a housing 122, which may be generally rigid (e.g., stiffer than the band 110) and electronics 124 contained within the housing 122. The band 110 and the module 120 are configured to couple to each other to form a loop (e.g., with two ends of the band 110 being coupled to two ends of the module 120), such that the wearable electronic device 100 may be worn on a wrist of the user (e.g., a patient, wearer), which is a human. Alternatively, the band 110 may form an entire loop with the module 120 being coupled thereto. In still further embodiments, the wearable electronic device 100 may include a different coupling device besides the band 110, which is configured to couple the module 120 to the user, such as to be coupled to a garment of the user (e.g., spring clip, mechanical clip, magnet, pin) or the user (e.g., a ring to be worn on a finger of the user).

[0019] The electronics 124 are configured to collect physiological data of the patient, such as movement data. As shown schematically in FIG. 2, the electronics 120, for example, include one or more motion sensing units 230, a controller 240, a data storage 250, a power source 260, and a communications interface 280, which may be coupled to a substrate 290 and form an electronics module. The electronics 120 may, in some embodiments, also include other components, such as a proximity sensor 270. When the wearable electronic device 100 worn by the patient, such as on a wrist of the patient, the electronics 124 move with the patient, sense motion of the patient, and store data pertaining to the motion sensed.

[0020] Referring to FIG. 2, as referenced above, the electronics 124 include one or more ofthe motion sensing units 230, the controller 240, the data storage 250, the power source 260, and the communications interface 280, which may be coupled to the substrate 290 to form an electronics module. In some embodiments, the electronics 124 may also include a proximity sensor 270, As described above, the electronics 120 are contained in the housing 122,

[0021] The motion sensing unit 230 is configured to sense motion of the patient, such as motion of the wrist of the patient on which the wearable electronic device 100 is worn, and outputs motion data according thereto. The motion sensing unit 230 may sense the motion while the wearable electronic device 100 is operated in a motion data collection mode. The motion sensing unit 230 may begin sensing motion upon activation of the power source 260 (e.g., when a metal-air battery is exposed to air, switch is operated, or the power source 250 electrically coupled to the other electronics 124), upon detection of environmental light (e.g., with a light sensor 292, discussed in further detail below), and / or or upon detection of the wearable electronic device being worn (e.g., detecting the patient with the proximity sensor 270).

[0022] The motion sensing unit 230 includes one or more sensors that sense motion, which may be referred to as motion sensors 232. The term motion is considered to include acceleration and velocity, such as linear acceleration, linear velocity, rotational acceleration, and rotational velocity. Motion may be measured directly, for example, with motion sensors that measure linear acceleration (i.e., accelerometers) or rotational velocity (i.e., gyroscopes). Alternatively, motion may be calculated from other measurements, such as linear velocity or cumulative displacement being derived from measured linear acceleration or linear acceleration and / or linear velocity being derived from measured position.

[0023] As discussed in further detail below, the motion sensing unit 230 may be configured to measure different motion, such as linear acceleration and / or rotational velocity, and environmental conditions that may influence the motion measurements. However, for the wearable electronic device 100 to be compact, lightweight, and low cost, it may be particular advantageous for the wearable electronic device 100 (i.e., the motion sensing unit 230) to perform only one or few types of motion measurement (e.g., linear acceleration in one or more axes), while not performing other types of motion measurement (e.g., rotational velocity) or position measurements from which motion can be calculated (e.g., local or global positioning). As a result, the number and cumulative size of the motion sensing unit 230 and the motion sensors thereof, by performing only one type of motion measurement, may be lower than ifperforming additional types of motion or position measurement, and the power consumption and associated power source may also be reduced. In a still further embodiment, the wearable electronic device includes the motion sensors 232 that are accelerometers and includes no other sensors that are configured to measure motion.

[0024] As discussed in further detail below, the motion sensing unit 230 is configured to sense motion at a sensing frequency, which is preferably relatively low, so as to reduce power and data storage requirements.

[0025] Further, the motion sensing unit 230, or the motion sensors thereof, may be considered to include additional features or components suitable for outputting the measured motion as motion data, such as power management, analog filter(s), analog-to-digital conveiter(s), digital filter(s), control logic, data processing, and / or an input / output (I / O).

[0026] The motion sensing unit 230 includes one or more motion sensors that measure linear acceleration in one or more axes and may be referred to as accelerometers. For example, the motion sensing unit 230 measures linear acceleration in three axes, for example, being or including a three-axis accelerometer. The motion sensing unit 230 may additionally include one or more motion sensors that measure rotational velocity about one or more axes and may be referred to as gyroscopes. In one specific example, the motion sensing unit 230 measures rotational velocity about the three axes, for example, being or including a three-axis gyroscope. Alternatively, the motion sensing unit 230 may measure acceleration in and / or rotational velocity about fewer than three axes, such as one or two axes. The accelerometer may, for example, be a micro-electromechanical system (MEMS) three-axis accelerometer. In a preferred example, the wearable electronic device 100 is configured to sense and / or measure linear acceleration (e.g., include three three-axis accelerometer) and not sense other types of motion as described previously (includes no further motion sensors). The motion sensing unit 230 may also include one or more additional sensors that measure one or more environmental conditions that influence measurements of the motion sensors and / or which may have other purposes. Such an additional sensor may be referred to as an environmental sensor. The environmental sensor of the motion sensing unit 230 may be or include a temperature sensor. In one specific example, the motion sensing unit 230 is or includes a combined accelerometer, such as a three-axis accelerometer, and a temperature sensor that are provided cooperatively as a singular device (e.g., a chip).

[0027] Further aspects of the motion sensing unit 230, including operations, collectingmotion data, and processing date, are discussed in further detail below with respect to FIG. 4.

[0028] Referring to FIG. 3, the controller 240 is generally configured to control one or more operations of the electronics 124 of the wearable electronic device 100. The controller 240 generally includes a processing unit 342, a memory 344, a storage 346, a communications interface 348, and a bus 349 by which the other components of the controller 240 are in communication with each other. The processing unit 342 may be any suitable processing unit, such as a central processing unit, that executes instructions. The memory 344 is a short-term, volatile memory, such as random access memory (RAM). The storage 346 is a long-term, nonvolatile storage device, such as a solid-state storage medium. The storage 346 may, for example, be a computer readable medium that includes the instructions that are executed by the processing unit 342 for implementing the devices, systems, and methods described herein. The communications interface 348 is configured to send and / or receive signals, such as for operating various other electronic components and / or receiving information therefrom.

[0029] The controller 240 may be provided in any suitable form, including, but not limited to, a microcontroller, application specific integrated circuit (ASIC), field programmable gate array (FGPA), or as separate components. The controller 240 may also be provided as an integrated unit with the motion sensing unit 230, for example, being configured as a system-on- a-chip (SOC) therewith.

[0030] The data storage 250 is configured to store the motion data that is output from the motion sensing unit 230 and the motion sensors thereof. The data storage 250 is a non-volatile, long-term storage device, such as solid-state storage. The motion data (e.g., accelerometer readings) is stored by the data storage 250 in association with a time indicator provided, for example, by a timer of the controller 240 (e.g., clock, time stamp counter). The time indicator may include known dates and times or may be another numerical indicator from which the dates and times may be associated with the collected motion data (e.g., a time counter). In some embodiments, the motion data may undergo minor processing, for example, to reduce the amount of motion data stored (e.g., via data compression, filtering, and / or averaging), as discussed in further detail below.

[0031] The motion data may be stored in one or more secured manners and / or for privacy. For example, the motion data may be stored in an encrypted format and / or otherwise require a security key for access thereto (e.g., when transferred from the data storage 250). Still further,the motion data may be stored in an anonymous manner, for example, with the data storage not storing an identifier of the patient (e.g., patient identification number or name) and / or the wearable electronic device 100 may be configured to not receive any patient identifying information from the patient and / or the prescriber (e.g., being configured to not transfer data with devices associated with the patient and / or the prescriber). Rather, the wearable electronic device may include a device identifier (e.g., a serial number) that the prescriber and / or the distributor stores in association with a patient identifier, such the only the prescriber and / or the distributor may be able to associate the motion data, or assessment thereof, with the patient.

[0032] The data storage 250 may be any suitable size to record the time indicator and the motion data, which as described in further detail below, may account for different stages of a useful life of the wearable electronic device 100 and modes of operation. For example, the data storage 250 may be between 1 MB and 4 GB, such as between 400 MB and 4 GB, more, or less. In one illustrative, non-limiting example, the motion data is stored in 8-bit format, which is aggregated from three accelerometers that sense motion of the human patient at a sensing frequency of 2 Hz or less (e.g., 1.6. Hz) and is downsampled to be stored at a storage frequency that is less than the sensing frequency (e.g., 1 Hz). The motion data will require approximately 4 MB of the data storage 250.

[0033] The data storage 250 may be the storage 346 of the controller 240, a separate component, or be provided as an integrated unit with the motion sensing unit 230, for example, being configured as a system-on-a-chip (SOC) therewith.

[0034] The power source 260 is configured to provide electrical power for operating the electronics 120 of the wearable electronic device 100. In one example, the power source 260 is a primary battery (i.e., a non-rechargeable battery), such as a metal-air battery (e.g., zinc-air, lithium-air, aluminum-air, or magnesium-air) or other type of primary battery (e.g., lithium, alkaline, or zinc-carbon). A primary battery may be advantageous over a secondary battery (i.e., a rechargeable battery) by being lower cost and / or by having a higher power density and, thereby, a smaller size for equivalent power capacity. Alternatively, the power source 260 may instead be or include a secondary battery (i.e., a rechargeable battery), a battery having an exchangeable electrolyte (i.e., being refillable), a capacitor, a super capacitor, and / or an energy harvester.

[0035] The power source 260 may be provided in any suitable format. In one example, thepower source 260 is a coin cell battery, which may be advantageous by having a relatively small form factor (e.g., low height), being readily available, and relatively low cost.

[0036] The wearable electronic device 100 may, in some embodiments, include the proximity sensor 270. The proximity sensor 270 may be used to determine whether to operate the motion sensing unit 230 to collect the motion data (e.g., by determining whether the wearable electronic device 100 is being worn, or a proxy indicative thereof, such as capacitance exceeding a threshold). Capacitance data may be recorded, for example, for the prescriber to assess compliance with the prescriber's instructions to the patient. The proximity sensor 270 may be operated in a patient detection mode and may be further operated in the motion data collection mode, as discussed in further detail below.

[0037] The proximity sensor 270 may be any suitable type of sensor for detecting proximity of the patient thereto. In one example, the proximity sensor 270 is a capacitive sensor. The proximity sensor 270 may be arranged on an underside of the substrate 290, such that the substrate 290 is not positioned between the patient and the proximity sensor 270 when the wearable electronic device 100 is worn by the patient. In a preferred example, a physical barrier is arranged between the proximity sensor 270 and the patient being detected thereby, which may include a portion of the body 110 (e.g., the housing portion 112).

[0038] The communications interface 280 of the wearable electronic device 100 allows data to be transferred between the wearable electronic device 100 and another computing device (e.g., of the processing facility, as discussed in further detail below). For example, the communications interface 280 enables the motion data to be transferred from the wearable electronic device 100 after being worn by the patient. The communications interface 280 may further enable receipt of data by the wearable electronic device 100, such as signals to initiate transfer of the motion data (e.g., which may include an encryption key) and / or to provide data to the wearable electronic device 100 (e.g., updated software programming by which the controller 240 operates the wearable electronic device 100).

[0039] The communications interface 280 may take any suitable form for transfer of data with the wearable electronic device 100. In a preferred example, the communications interface 280 provides wired data transfer by including conductive contacts that are configured to conductively couple to corresponding conductive contacts of mating communications interface of the other computing device, for example, to transfer data directly from the data storage 250(e.g., a bus connected to the data storage 250). In another example, the communications interface 280 is configured for wireless communication and includes appropriate hardware (e.g., coil, antenna, or semiconductor) for sending and / or receiving data according to any suitable protocol (e.g., Bluetooth), and such hardware may also function to harvest energy (e.g., RF energy) to replenish the power source 260 or another power source. In some embodiments, the wearable electronic device 100, the electronics 120, and the communications interface 280 thereof are configured to not transfer the motion data wirelessly (e.g., including no antennas or other devices by which the data may be transferred wirelessly).

[0040] The wearable electronic device 100 may be further configured to not provide outputs based on the motion sensing unit 230 or other sensors thereof to the user in real time and / or directly to the user. For example, the wearable electronic device 100 may not include a visual, audio, or other output device by which motion data or other information derived therefrom is communicated directly to the user. The wearable electronic device 100 may be further configured to not communicate with any other electronic devices or computing devices associated with the user, for example, the wearable electronic device 100 may be configured to communicate only with computing devices or systems (e.g., the analysis computing system 530, discussed in further detail below) specifically configured and / or authorized to do so (e.g., being associated with the same manufacturer).

[0041] The motion data may be further processed to provide the other data (e.g., processed motion data), for example, being processed as the resultant vector of three axes of motion data (e.g., acceleration in three perpendicular axes).

[0042] The timer may continue to provide the time indicators until subsequent data transfer between the data storage and a computing device. Thus, by continuing to operate the timer until subsequent data transfer allows for association of the time indicators with known date and time, either by transferring a known date and time to be stored in association with a time indicator on the wearable electronic device 100 or transferring the motion data or other data derived therefrom to the computing device to be stored in association with a known date and time.

[0043] The power source 260, as will described in further detail below, is configured to provide the wearable electronic device 100 with sufficient capacity to supply power over the useful life of the wearable electronic device 100. As a result, none of the patient, the prescriber, or other custodian (e.g., a distributor of the wearable electronic device 100) is required tomaintain or replace the power source 260. Furthermore, as described previously, physical access to the power source 260 may be hindered by requiring the body 110 to be damaged or the removal of a sacrificial component to gain physical access thereto. As a result, maintaining the power source 260 does not provide any barrier to use by the patient, the prescriber, or other custodian.

[0044] With further reference to FIG. 4 , the wearable electronic device 100 may be configured to implement a method 400 for motion data collection of a patient. As referenced above, the wearable electronic device 100 may be configured as a one-time use device in which case the wearable electronic device 100 is configured to sense, process, and store motion data in manners that reduce power consumption and storage requirements, as compared to other wearable devices, thereby reducing battery and storage sizes and costs, while still outputting data that is usable, or processable to be used, by the prescriber to assess patient activity over time, for example, to determine efficacy of pain management. More particularly, while human movement may be up to 12 Hz, the Nyquist- Shannon sampling theorem would require sampling be 24 Hz or more (i.e., two times 12 Hz), as is conventionally done with activity trackers that provide users relatively granular data. For example, one product called Motion Watch is understood to both sense and store motion data at 50 Hz. However, testing has shown that, in one embodiment of the wearable electronic device 100, as described herein, using statistical sampling with sensing movement at 1.6 Hz and storing movement at 1.0 Hz to have an R-squared value of 0.991 with data from the Motion Watch (Nyquist sampling at 50 Hz). Moreover, the embodiment tested had a dynamic range of approximately one-fourth that of the Motion Watch (i.e., + / - 2g vs. + / - 8g), while still achieving the aforementioned R-squared value, thereby allowing each piece of motion data to be stored more compactly (e.g., 8-bit vs. 12-bit), thereby reducing storage requirements. Moreover, the wearable electronic device 100 may not perform any Nyquist sampling and / or filtering of any data before storing as the motion data.

[0045] Referring to FIG. 4, the wearable electronic device 100 is generally configured to sense motion, process motion data, and store motion data, as described with respect to the method 400 of collecting motion data. The method 400 generally includes sensing 410 motion, processing 420 motion data, and storing 430 motion data.

[0046] The sensing 410 of the motion includes sensing motion with a motion sensor, such as those of a three-axis accelerometer of the motion sensing unit 230, and providing output motiondata at a sensing frequency. The sensing frequency may be less than 5 Hz, such as 2 Hz or less (e.g., approximately 1.6 Hz). The sensing 410 and / or storage of the motion data may further be performed within a dynamic range of + / - 4g or less (e.g., + / - 3g, such as approximately + / - 2g). The motion data may, for example, be output as motion vectors in each axis of the motion sensors. The output of the sensing 410 may be referred to as the motion data or the raw motion data.

[0047] The processing 420 of the motion data includes processing the motion data from the motion sensors for storage. The output of the processing 420 may be referred to as the processed or stored motion data. The processed motion data is preferably in 8-bit format.

[0048] The processing 420 may include converting 422 the motion data and may instead or additionally include downsampling 422 the motion data.

[0049] The converting 422 of the motion data includes processing multiple measurements from multiple motion sensors into a singular output, such as the resultant or root mean square of the raw motion data (e.g., motion vectors). The converting 422 may be performed by the motion sensing unit 230, or by transferring the motion data to the processor 240 which then performs the converting 422. The output of the converting 422 may be referred to as converted motion data, or as the processed motion data if no downsampling is to be performed. The converted motion data is preferably in 8-bit format.

[0050] The down sampling 424 includes downsampling the motion data (e.g., the raw or converted motion data) from the sensing frequency to a storage frequency that is lower than the sensing frequency. For example, the storage frequency may be 4 Hz or less, such as 2 Hz or less (e.g., 1 Hz). The downsampling 424 is performed with the processor 240. The output of the downsampling 424 may be referred to as the downsampled motion data.

[0051] The storing 430 of the motion data is performed with the storage 250. The processed motion data (e.g., the converted and / or downsampled data) is transferred to the storage and then stored by the storage 250 and may then be referred to the stored motion data. The stored motion data is stored in association with time indicators. The time indicators may be counted increments of a particular time interval (i.e., corresponding to the storage frequency) that are unassociated with real time, or may be real time. In the case of the counted increments, the stored motion data may be subsequently associated with real time in subsequent processing operations. As a result, the motion data may be stored with a storage frequency (e.g., at time indicators), for example, of2 Hz or less (e.g., 1 Hz or less), with a dynamic range of + / - 3g or less (e.g., + / - 2g or less), and / or as a single value representing multiple measurements.

[0052] The method 400 may further include subsequent processing 440 of the stored motion data, which may be performed with another computing device. The subsequent processing 440 includes aggregating the stored motion data over a time period, such as by averaging the motion data over the time period. The output of the further processing 440 may be referred to as aggregated motion data.

[0053] Referring to FIG. 5, a system 500 includes multiple of the wearable electronic devices 100 associated with different patients and an analyzing computing system 530. The analyzing computing system 530 is configured to receive and process the stored motion data from the wearable electronic devices 100 to produce and output patient activity metrics to prescribers (e.g., doctors), for example, to one or more prescriber computing systems 540 associated with the prescribers. The analyzing computing system 530 may be generally configured as described for the controller 346 (e.g., having a processor, memory, storage, and / input output device). For example, each of the users (i.e., the patients) may mail their wearable electronic devices 100 to a processing facility that includes the computing system 530 and to which the motion data is transmitted from the each of the wearable electronic devices 100. The analyzing computing system 530 then processes the motion data from each of the wearable electronic devices 100 to produce the one or more patient activity metrics.

[0054] The patient activity metrics normalized to an normalization period, such as a day-long period (i.e., 24 hours). The patient activity metrics may, for example, include a normalized activity metric that is a measure of magnitude of activity over the normalization period, a normalized active period that is a measure of hours of the normalization period during which a patient was active, and / or a normalized inactive period that is a measure of hours of the normalization period during which the patient was inactive. The metrics may further include sleep information (e.g., number and / or duration of sleep periods in a given day) that is derived from the motion data, which may be used in determining the other aggregated metrics. In processing the motion data to output the aggregated activity metrics, the motion data may also be analyzed to determine whether the wearable electronic device 100 was being worn by the user. In the case of the normalization period being a day, the patient metrics may be referred to as daily metrics (e.g., daily activity metric, daily active period, daily inactive period).

[0055] The activity metrics may be further analyzed and / or aggregated over longer periods of time, for example, by identifying trends of the normalized activity metrics or determining averages of the aggregated metrics over a given period, such as a week or total wear period over which the user was prescribed to or did actually wear the wearable electronic device. Moreover, a user may be prescribed multiples times to wear different ones of the wearable electronic devices 100, and the normalized metrics, trends, and / or aggregations thereof may be compared over the different periods that the user wore the different wearable electronic devices 100, so as to assess the activity of the patient over time (e.g., as they undergo palliative care).

[0056] The normalized activity metric is an indicator of the total activity over the normalization period, which may, for example, be an average or a summation of the motion data over a day. As referenced above, the motion data may be stored as a single number (e.g., the resultant vector of the three accelerometer readings) in association with a time indicator. The stored motion data may be processed to determine the normalized activity metric by determining an average or summation of the motion data, such as the average of the absolute value of the derivative of the motion data at each time indicator over the normalization period.

[0057] The analyzing computing system 530 may further determine whether the wearable electronic device 100 was being worn by the patient, for example, distinguishing the motion date between no movement over time (e.g., being set down on a stable surface), as compared to slight movements that may indicate that the patient is still wearing the device 100 but moving little (e.g., while sleeping). For example, the analyzing computing system 530 may determine that the wearable electronic device 100 is not being worn if the motion data is below a threshold (e.g., a stationary threshold) over a period that is above a stationary threshold duration (e.g., one minute) and determine aggregated wear or aggregated non-wear periods. The analyzing computing system 530 may output a normalized wear metric or a normalized non-wear metric, which is that amount of time during the aggregated period that the wearable device 100 was determined to be worn or not worn. Instead or additionally, the determination of whether the wearable device was being worn may be used in determining the normalized activity metric, for example, excluding that motion data from the calculated average (as described above) if the wearable electronic device 100 is determined to not be worn.

[0058] The normalized active period metric is an indicator of a duration of the normalization period during which the magnitude of motion (e.g., an average over a duration or epoch) isgreater than a threshold value, which may be referred to as the active threshold. The stored motion data from the wearable electronic device 100 may be processed to determine the normalized active period metric by taking the absolute value of the stored motion data or value derived therefrom (e.g., the derivative of the motion data), and determining according to the time indicators the duration that motion data that is greater than the active threshold. The normalized active period metric may be expressed in duration (e.g., hours and / or minutes) per day. The active threshold may be a fraction of a g and is intended to capture elevated activity.

[0059] The normalized inactive period metric is an indicator of a duration of the normalization period during which the magnitude of motion is less than a threshold value, which may be referred to as the inactive threshold. The stored motion data from the wearable electronic device 100 may be processed to determine the normalized inactive period metric by taking the absolute value of the stored motion data or value derived therefrom (e.g., the derivative of the motion data), and determining according to the time indicators the duration of that motion data that is greater less than the active threshold. The normalized inactive period may be expressed in duration (e.g., hours and / or minutes per day). The inactive threshold may be a fraction of a g that is lower than the active threshold and is intended to capture sedentary activity (e.g., sleep). The active and inactive thresholds are standardized for all users (i.e., not user adjustable), so as to ensure that prescribers (e.g., doctors) can easily interpret that metrics across multiple patients.

[0060] Referring to FIG. 6, a method 440 of the further processing of the motion data is provided. The method 440 generally includes receiving 642 the stored data from the wearable electronic device, determining 644 from the stored motion data when the wearable device was worn, determining 646 from the motion data one or more normalized metrics, determining 648 aggregated normalized metrics from the one or more normalized metrics, outputting 650 the normalized and / or aggregated normalized metrics, repeating 652 steps 642-650 for each additional wearable electronic device.

[0061] The receiving 642 is performed with the analysis computing system 530, for example, by transmitting the motion data the wearable device 100 to the analyzing computing system 530 via any suitable type of transmission, such as wired or wireless communication.

[0062] The determining 644 when the wearable device 100 was worn may be optional. In so doing, the motion data is analyzed, for example, by comparing the motion data to thresholds and / or duration thresholds.

[0063] The determining 646 of the normalized metrics includes determining one or more metrics according to successive normalization periods within the motion data, such as successive days day (i.e., 24 hours).

[0064] In one example, the determining 646 includes determining 646a a normalized activity metric, which includes averaging the absolute value of the derivative of the motion data over each of the successive normalization periods.

[0065] In another example, the determining 646 may instead or additionally include determining 646b a normalized activity period, which includes determining an amount of time that the magnitude of the motion data is above a predetermined threshold (e.g., an active threshold) during each of the successive normalization periods.

[0066] In another example, the determining 646 may instead or additionally include determining 646c a normalized activity period, which includes determining an amount of time that the magnitude motion data is below a predetermined threshold (e.g., an inactive threshold) during each of the successive normalization periods.

[0067] The determining 648 of the aggregated normalized metrics includes, for example, averaging one or all of the normalized metrics over the successive normalization periods. The determining 648 of the aggregated normalized metrics may further include combining and / or comparing (e.g., determining and / or identifying trends or changes) the normalized and / or aggregated metrics for those of a user when wearing a different one of the wearable electronic devices 100 over a different time period.

[0068] The outputting 650 of the normalized and / or aggregated metrics includes sending the normalized and / or aggregated metrics from the analyzation computing system 530 to the prescriber computing systems 540 associated with the prescribers for each of the users. A report may be sent and / or the data provided to the prescriber computing system 540 to be output by a user interface. The report and / or user interface may display the normalized metrics, trends, and / or aggregations for one total wear periods and / or across different total wear periods.

[0069] While the disclosure has been described in connection with certain embodiments, it is to be understood that the disclosure is not to be limited to the disclosed embodiments but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which scope is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures as is permitted under the law.

Claims

CLAIMSWhat is claimed is:

1. A wearable electronic device comprising: a motion sensing unit, and a data storage; wherein the wearable electronic device is configured to be worn by a human user; the motion sensing unit senses motion of the human user and outputs motion data according thereto; wherein data storage receives and stores the motion data; and wherein the wearable electronic device is configured to store the motion data at a storage frequency of less than 2 Hz.

2. The wearable electronic device according to claim 1, wherein the motion sensing unit is a three-axis accelerometer; wherein a resultant vector of outputs of the three-axis accelerometer is stored as the motion data; wherein the motion data is stored with a dynamic range of + / - 2g or less; wherein the motion data is stored in 8-bit format; and wherein the electronic device does not include an output device configured to output the motion data or values derived therefrom to the user.

3. The wearable electronic device according to claim 1, wherein the motion sensing unit is a three-axis accelerometer.

4. The wearable electronic device according to claim 3, wherein a resultant vector of outputs of the three-axis accelerometer is stored as the motion data.

5. The wearable electronic device according to any of the preceding claims, wherein the motion data is stored with a dynamic range of + / - 2g or less.

6. The wearable electronic device according to any of the preceding claims, wherein themotion data is stored in 8-bit format.

7. The wearable electronic device according to any of the preceding claims, wherein the electronic device does not include an output device configured to output the motion data or values derived therefrom to the user.

8. A system comprising: multiple of the wearable electronic devices according to any of the preceding claims; and a computing system configured to, for each of the wearable electronic devices: receive the motion data from the storage of the wearable electronic device; and generate one or more normalized metrics from the motion data for successive normalization periods; and output the normalized metrics to other computing systems associated with prescribers.

9. The system according to claim 8, wherein the normalization periods are 24 hours.

10. The system according to claim 9, wherein the normalized metrics include a normalized activity metric that is an average of absolute values of derivatives of the motion data.

11. The system according to any of claims 8-10, wherein the normalized metrics include a normalized active period metric during the normalization period in which magnitudes of the motion data is above an active threshold.

12. The system according to claim 11, wherein the normalized metrics include a normalized inactive period metric during the normalization period in which the magnitudes of the motion data is below an inactive threshold that is lower than the active threshold.

13. The system according to claim 8, wherein the computing system is further configured to output one or more aggregated metrics that is an average of the one or more normalized metrics over a total wear period.

14. The system according to claim 8, wherein the computing system is further configured to determine when the wearable electronic device is worn by the user.

15. The system according to claim 8, wherein the computing system is configured to associate the normalized metrics for one user over one total wear period of one of the wearable electronic devices with the same user over another total wear period with another of the wearable electronic devices.

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