DETECTION OF BIOMECHANICAL IMPROVEMENTS USING BODY-WEARABLE DEVICES

Abstract

Embodiments for detecting biomechanical impairments using body-worn devices are disclosed. An exemplary method comprises: obtaining a first set of sensor data, the location of the body-worn device, and a timestamp; determining a first set of fitness measurements based on the sensor data; predicting a baseline health profile based on the first set of fitness measurements; storing the baseline health profile, location, and timestamp; at a second time point following the timestamp: detecting that the body-worn device is at the location; obtaining a second set of sensor data from the sensors of the body-worn device; determining a second set of fitness measurements based on the second set of sensor data.Predicting a current health profile for the user based on the second set of fitness measurements; comparing the current health profile with the baseline health profile; and, in response to a result of the comparison, performing an action.

Description

TECHNICAL FIELD

[0001] This disclosure relates generally to health monitoring. BACKGROUND

[0002] Humans move their bodies and limbs in highly individual and repeatable patterns based on, for example, pelvic rotation, pelvic tilt, knee and hip flexion, knee and ankle interaction, and lateral pelvic movement. Alterations in these determinants can indicate health problems, such as injury or disease. Changes due to progressive disease or aging occur slowly over many months or years, making them difficult for the individual or their physician to detect during biannual or annual checkups.However, it is desirable to detect such biomechanical aging or disease processes over the long term, before they can be observed in a clinical setting, so that preventative treatments can be prescribed and tested for effectiveness by the affected person or the physician before they develop into more serious health problems. SUMMARY

[0003] Embodiments for detecting biomechanical impairments using wearable devices are disclosed. In some embodiments, a method comprises: obtaining a first set of sensor data, a location of the wearable device, and a timestamp; determining a first set of fitness metrics based on the sensor data; predicting a baseline health profile based on the first set of fitness metrics; storing the baseline health profile, the location, and the timestamp; at a second time following the timestamp: detecting that the wearable device is at the location; obtaining a second set of sensor data from the sensors of the wearable device; determining a second set of fitness metrics based on the second set of sensor data;Predicting a current health profile for the user based on the second set of fitness measurements; comparing the current health profile to the baseline health profile; and in response to a result of the comparison, performing an action.

[0004] In some embodiments, the sensors include inertial sensors, a heart rate sensor, and at least one in-ear device.

[0005] In some embodiments, predicting the baseline and current health profiles includes providing the first and second sets of fitness metrics as inputs to a machine learning model that is trained to predict an overall fitness score for the user.

[0006] In some embodiments, the machine learning model is trained using health profiles of other people at the location who have a similar health profile to the user.

[0007] In some embodiments, the machine learning model is trained on extended historical health profiles of the user.

[0008] In some embodiments, the first and second sets of fitness metrics include at least one of gait analysis, walking stability, or cardiorespiratory fitness.

[0009] In some embodiments, the action includes sending an alert message to the user.

[0010] Other embodiments relate to an apparatus, a system, and a computer-readable medium.

[0011] Certain embodiments described herein provide one or more of the following advantages. This disclosed system and method uses machine learning to generate a baseline user health profile over a specific period of time (e.g., one month) based on sensor measurements output by various sensors of a wearable device (e.g., smartphones, smartwatches, fitness devices, earbuds, head-worn devices, chest-worn devices). The user's baseline health profile may be associated with a specific geographic location and / or time. Detection of sudden or long-term changes in the user's baseline health profile may trigger a notification to the user (e.g.,a visual, audible, or haptic notification) indicating that the user's current health profile is deviating from the user's baseline health profile (e.g., worsening) and displays the specific metrics that caused the deviation (e.g., uneven gait, change in walking speed, breathing pattern, bone vibrations, internal sounds, bruxism, joint clicking). BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a block diagram of a system for detecting biomechanical impairments using wearable devices according to one or more embodiments. Fig. 2 is a flowchart of a process for detecting biomechanical impairments using wearable devices according to one or more embodiments. Fig. 3 is a block diagram of an apparatus architecture for implementing the features and processes described with reference to Fig. 1-2 are described. DETAILED DESCRIPTION

[0012] Fig. 1 is a flowchart of the process 100 for detecting biomechanical impairments using wearable devices, according to one or more embodiments. The process 100 includes determining whether a wearable device is available for recording (101). A wearable device may be, for example, a smartphone, a smartwatch, earbuds, a chest-mounted device, a head-mounted device (HMD), etc. The wearable device is available for recording when it is turned on and positioned on the user's body such that sensor measurements can be taken from one or more sensors of the wearable device. For example, earbuds are available for recording when they are turned on and inserted into the user's ear. A microphone in the earbud captures the user's internal body sounds.Another example is a heart rate sensor integrated into a smartwatch that is attached to the user's wrist and measures the user's heart rate. A wearable device not available for recording would be, for example, a device that is not on the user's body or that does not have a power supply. In some embodiments, a machine learning model is trained to detect whether a wearable device is on the user, such as detecting whether a smartphone is in a pocket.

[0013] If at least one wearable device is available for recording, sensor measurements are recorded 103 on the wearable device and stored on the wearable device and / or uploaded to a network storage device or to storage on a paired device (e.g., a smartwatch paired with a smartphone). Otherwise, the sensor measurements are not recorded 202. Some examples of sensor measurements include, but are not limited to, bone vibrations, internal body sounds (e.g., cracking of joints, breathing, teeth grinding, etc.), heart rate, body movements of the user (e.g., acceleration, angular rate, stride frequency), breathing sounds captured by an external microphone of the wearable device (e.g.,heavy breathing, breath holding, wheezing), electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, temperature, VO2max, and blood volume pulse (BVP) obtained from a photoplethysmography (PPG) sensor, etc. Various other metrics can be derived from the motion sensor data, including the user's walking behavior (e.g., falling and catching), walking speed, gait balance, etc.

[0014] The sensor data described above can be collected simultaneously at the same data rate or at different data rates. In addition to the sensor data, the user's location where the sensor data was obtained and a timestamp indicating the date and time the sensor data was obtained are also recorded and stored with the sensor data. The sensor data can be collected / measured continuously, periodically, and / or in response to a trigger event.

[0015] In some embodiments, the stored sensor data is input to a machine learning model 104 that is trained or configured to predict a health profile for the user. Some examples of machine learning models include, but are not limited to, deep learning networks (e.g., convolutional neural network (CNN)), k-nearest neighbor clustering (KNN), Naive Bayes, regression analysis (linear and logistic), decision trees, random forests, and support vector machines. In some embodiments, feature vectors are formed from the stored sensor data. The feature vectors are input to a machine learning model trained to predict a user health profile 106. In some embodiments, the machine learning model is trained using health profiles 105 of other people at the location who have a similar health profile to the user.In some embodiments, the machine learning model is trained on expanded historical health profiles of the user to increase the amount of training data.

[0016] In some embodiments, the machine learning model predicts a baseline health profile 106. This prediction would ideally occur when the user is healthy and injury-free. The baseline health profile 106 may include one or more fitness values ​​or metrics indicative of the user's fitness level. For example, a machine learning model may predict an overall assessment of the user's health based on a gait analysis of the user, as described in U.S. Patent Publication No. 2021 / 0393166, "Monitoring User Health Using Gait Analysis," published on December 23, 2021, and incorporated herein by reference in its entirety. In some embodiments, the machine learning model may predict a user's cardiorespiratory fitness, as described in co-pending U.S. Patent Application No.No. 17 / 985,098 for "Identifying Poor Cardiorespiratory Fitness Using Sensors of Wearable Devices," filed on November 11, 2022, and incorporated herein by reference in its entirety. In some embodiments, the machine learning model can predict walking stability, as described in U.S. Patent Publication No. 2023 / 0112071 A1 for "Assessing Fall Risk of Mobile Device User," published on April 13, 2023, and incorporated herein by reference in its entirety.

[0017] In some embodiments, a trained machine learning model is used to determine whether the wearable device is on a dominant or regressive leg of the user (e.g., a smartphone in the right or left front pants pocket) to determine whether the user is limping, which could be an indicator of ill-fitting shoes, strains, sprains, or an object such as a splinter lodged in the foot, balance issues, broken bones, etc.

[0018] Accordingly, the values ​​or measurements calculated by the aforementioned processes may comprise the user's baseline health profile 106. If these values ​​or measurements change from the baseline by a certain amount, a notification 107 (e.g., push notification, text message, display) may be sent to the user's wearable device or other device to alert the user of the change and indicate which value / measurement caused the change. In some embodiments, with the user's consent, the user's health profile 106 may be included in the user's medical record 108, where it may be accessible to healthcare professionals.

[0019] For example, in a first month, an initial set of sensor data is obtained from a user's smartwatch and earbuds while the user wears the devices. Sensors in the smartwatch measure motion data (e.g., shaking, vibration) and heart rate. Microphones on the smartwatch detect the user's breathing. A microphone in the earbud detects body sounds in the upper neck region. Using the sensor data, additional fitness metrics are derived, as described above, indicating the user's gait, walking stability, and cardiorespiratory fitness. The metrics are input into a machine learning model that predicts an overall fitness score for the user. The system searches for the user's health profile stored on the wearable device, the companion device, or on a network.Since this is the first instance, the system creates a new user health profile and labels it as the baseline health profile. The overall fitness score is included in the baseline health profile stored on the smartwatch and / or companion device and / or network device.

[0020] Additionally, a global navigation satellite system (GNSS) receiver (e.g., a global positioning system (GPS) receiver) on the smartwatch determines the smartwatch's location. The location and a timestamp are also stored in the basic health profile. Furthermore, an activity classifier on the wearable device provides an indication of the activity the user was engaged in when the sensor data was obtained (e.g., walking, jogging, cycling, driving).

[0021] The wearable device monitors the user's location (e.g., from GPS output) and time to determine when the user is at the same location where the baseline user profile was created, as well as a certain amount of time after the baseline user health profile was created (e.g., 1 month later). In some embodiments, a geofence is placed around the location, which triggers a second set of sensor data to be obtained when the user traverses it.

[0022] The second set of sensor data is obtained from the user's smartwatch and earbuds. Again, the second set of sensor data includes movement data, heart rate, respiration, and internal body sounds. The second set of sensor data is input into the machine learning model, which predicts a second overall fitness score. The second overall fitness score is compared to the first overall fitness score stored in the baseline user health profile. If the second overall fitness score is lower than the baseline overall fitness score by a specified amount, an action is taken, such as sending an alert to the user.

[0023] The alert may be displayed on the smartwatch or companion device, and / or a push notification or text message may be sent to the wearable device or other device. The alert may inform the user that their overall fitness has declined since the previous month. The current health profile becomes a new baseline health profile, which is saved so that it can be compared to the new health profile for the user calculated in the next month, and so on.

[0024] As described above, an activity classifier on the wearable device may determine the activity the user is performing at the time the sensor data is received. In some embodiments, the overall fitness measurement is not calculated if the activity the user is currently performing differs from the activity the user was performing when the baseline health profile was created in the previous month. An example activity classifier is described in U.S. Patent No. 8,892,391 for "Activity Detection," issued November 18, 2014, which is incorporated herein by reference in its entirety.

[0025] In some embodiments, the individual metrics that contributed to the overall fitness metric (e.g., a walking mobility metric, a cardiorespiratory metric) may be provided in the alert so the user can see which specific aspects of their health profile caused the overall fitness metric to deteriorate compared to the previous month's baseline fitness metric. In this example, the gate analysis metric indicates that the user fell and caught themselves, and the cardiorespiratory metric indicates a longer-than-usual recovery time after performing a physical activity. This information can encourage the user to focus on their health and provide useful health information that the user can share with their physician.

[0026] In some embodiments, the user can set the time interval between sensor measurements (e.g., 1 week, 1 month, 3 months, 6 months, 1 year, etc.) in a settings panel of the wearable device or through other suitable inputs. The user can also select a preference for receiving alerts (e.g., push notification, text message, etc.) and a desired format for a health profile summary (e.g., determine which measurements are displayed).

[0027] In some embodiments, the foregoing is part of a health-related mobile application. The user can request real-time analysis through the application to check, for example, whether a new pair of shoes fits well. For example, ill-fitting shoes may impair the user's gait. The user could also request that the application track their back health and physical improvement and posture in response to treatments. For example, track progress with a chiropractor's adjustment or balance while walking. In some embodiments, the feedback could be used to implement a weight loss / gain program.

[0028] Fig. 2 is a flowchart of a process 200 for detecting biomechanical impairments using wearable devices according to one or more embodiments. The process 200 may be described, for example, in the application described with reference to Fig. 3 described device architecture 300 can be implemented.

[0029] The process 200 includes, at a first time, obtaining a first set of sensor data from sensors of a wearable device, a first location of the wearable device, and a timestamp (201); determining a first set of fitness metrics based on the sensor data (202); predicting a baseline health profile for a user of the wearable device based on the first set of fitness measurements (203); storing the baseline health profile, the location, and the timestamp (204); at a second time following the timestamp: detecting that the wearable device is at the location, obtaining a second set of sensor data from sensors of a wearable device (205); determining a second set of fitness measurements based on the second set of sensor data (206);Predicting a current health profile for the wearable device user based on the second set of fitness data (207); comparing the current health profile with the baseline health profile (208); and in response to a result of the comparison, performing an action (209). Each of these steps was previously described with reference to [Figure 1]. Fig. 1 described.

[0030] In some embodiments, the sensors include inertial sensors, a heart rate sensor, and at least one in-ear device capable of measuring, for example, internal body noise in the upper neck region (e.g., crackling). In some embodiments, predicting the baseline and current health profiles includes providing the first and second sets of fitness metrics as inputs to a machine learning model that is trained to predict an overall fitness score for the user. The machine learning model may, for example, be a deep learning neural network trained on health profiles of other individuals of the same age and with a similar health profile. In some embodiments, the first and second sets of fitness metrics include at least one of gait analysis, walking stability, or cardiorespiratory fitness.In some embodiments, the action performed includes sending an alert to the user, which may be in the form of a push notification, a text message, a visual indicator, haptic feedback, audible feedback, etc. .

[0031] Fig. 3 is a block diagram of a device architecture 300 for implementing the features and processes described with reference to Fig. 1-2. The architecture 300 may include a memory interface 302, one or more hardware data processors, image processors, and / or processors 304, and peripheral interfaces 306. The memory interface 302, the one or more processors 304, and / or the peripheral interface 306 may be separate components or may be integrated into one or more integrated circuits. The system architecture 300 may be incorporated into any suitable electronic collision detection device, including, but not limited to, a smartwatch, a smartphone, a fitness band, and any other device that can be attached, worn, or held by a user.

[0032] Sensors, devices, and subsystems may be coupled to the peripheral interface 306 to provide multiple functionalities. For example, one or more motion sensors 310, a light sensor 312, and a proximity sensor 314 may be coupled to the peripheral interface 306 to support motion sensing (e.g., acceleration, rotation rates), illumination, and proximity functions of the wearable device. A location processor 315 may be coupled to the peripheral interface 306 to provide geo-positioning. In some implementations, the location processor 315 may be a GNSS receiver, such as a Global Positioning System (GPS) receiver. An electronic magnetometer 316 (e.g., an integrated circuit chip) may also be coupled to the peripheral interface 306 to provide data that can be used to determine the direction of magnetic north.The electronic magnetometer 316 can provide data to an electronic compass application. One or more motion sensors 310 can include one or more accelerometers and / or gyroscopes configured to determine changes in speed and direction of movement. A barometer 317 can be configured to measure atmospheric pressure (e.g., a pressure change within a vehicle). A biosignal sensor 320 can include one or more of a PPG sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an electromyogram (EMG) sensor, a mechanomyogram (MMG) sensor (e.g.,piezo-resistive sensor) for measuring muscle activity / contractions, an electrooculography (EOG) sensor, a galvanic skin response (GSR) sensor, a magnetoencephalogram (MEG) sensor, and / or one or more other suitable sensors configured to measure biosignals.

[0033] Communication functions may be supported by wireless communication subsystems 324, which may include radio frequency (RF) receivers and transmitters (or transceivers) and / or optical (e.g., infrared) receivers and transmitters. The particular design and implementation of the communication subsystem 324 may depend on the communication network(s) over which a mobile device is to operate. For example, the architecture 300 may include communication subsystems 324 configured to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi™ network, and a Bluetooth™ network. In particular, the wireless communication subsystems 324 may include hosting protocols so that the collision device can be configured as a base station for other wireless devices.

[0034] An audio subsystem 326 may be coupled to a speaker 328 and a microphone 330 to support voice-assisted functions such as voice recognition, voice replication, digital recording, and telephony functions. The audio subsystem 326 may be configured to receive voice commands from the user.

[0035] The I / O subsystem 340 may include a touch surface controller 342 and / or one or more other input controllers 344. The touch surface controller 342 may be coupled to a touch surface 346. The touch surface 346 and the touch surface controller 342 may, for example, detect contact and movement or a discontinuity thereof using any of a variety of touch-based technologies, including, but not limited to, capacitance, resistance, infrared, and surface acoustic wave-based technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch surface 346. The touch surface 346 may, for example, include a touchscreen or the digital crown of a smartwatch. The I / O subsystem 340 may include a haptic machine or device for providing haptic feedback (e.g.,vibrations) in response to commands from the processor 304. In one embodiment, the touch surface 346 may be a pressure-sensitive surface.

[0036] One or more input controls 344 may be coupled to other input / control devices 348, such as one or more buttons, toggle switches, a thumbwheel, an infrared port, and a USB port. The one or more buttons (not shown) may include a volume up / down button for adjusting the volume of the speaker 328 and / or the microphone 330. The touch surface 346 or other controls 344 (e.g., a button) may include or be coupled to fingerprint identification circuitry for use with a fingerprint authentication application to authenticate a user based on their fingerprint(s).

[0037] In one implementation, pressing the button for a first duration may unlock touch surface 546; and pressing the button for a second duration longer than the first duration may turn power on or off to the mobile device. The user may be able to customize a functionality of one or more of the buttons. Touch surface 346 may also be used, for example, to implement virtual or soft keys.

[0038] In some implementations, the mobile device can play recorded audio or video files, such as MP3, AAC, and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player. Other input / output and control devices can also be used.

[0039] The memory interface 302 may be coupled to a memory 350. The memory 350 may include high-speed random access memory and / or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and / or flash memory (e.g., NAND, NOR). The memory 350 may store the operating system 352, such as the iOS operating system developed by Apple Inc., Cupertino, California. The operating system 352 may include instructions for handling basic system services and performing hardware-dependent tasks. In some implementations, the operating system 352 may include a kernel (e.g., a UNIX kernel).

[0040] The memory 350 may also store communication instructions 354 to support communicating with one or more additional devices, one or more computers, and / or one or more servers, for example, instructions for implementing a software stack for wired or wireless communications with other devices.The memory 350 may include graphical user interface instructions 356 to support graphical user interface processing; sensor processing instructions 358 to support sensor-related processing and sensor-related functions; phone instructions 360 to support phone-related processes and functions; electronic messaging instructions 362 to support processes and functions related to electronic messaging; web browsing instructions 364 to support processes and functions related to web browsing; media processing instructions 366 to support processes and functions related to media processing; GNSS / location instructions 368 to support generic GNSS and location-related processes and instructions; and instructions 370 that support the instructions described with reference to FIG. Fig. 1-2. The memory 350 further includes other application instructions 372, including, but not limited to, instructions for applications that implement the Fig. 1 implement the system 100 shown.

[0041] Each of the above instructions and applications may correspond to a set of instructions for performing one or more of the functions described above. These instructions do not need to be implemented as separate software programs, sequences, or modules. Memory 350 may include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and / or software, including one or more signal processing circuits and / or one or more application-specific integrated circuits.

[0042] While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features described in this specification in connection with separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in connection with a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination.Furthermore, although features may be described above as being effective in certain combinations and may even be initially claimed as such, in some cases one or more features from a claimed combination may be carved out of the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

[0043] Likewise, although operations are illustrated in the drawings in a particular order, this should not be understood to mean that such operations must be performed in the particular order shown or in sequential order, or that all of the illustrated operations must be performed to achieve the desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of the various system components in the embodiments described above should not be understood to mean that such separation is necessary in all embodiments, and it should be understood that the described program components and systems may generally be integrated together into a single software product or packaged into multiple software products.

[0044] As described above, some aspects of the subject matter of this description involve collecting data and using available data from various sources to enhance services a mobile device can provide to a user. The present disclosure contemplates that, in some cases, this collected data may identify a particular location or address based on device usage. Such personal information may include location-based data, addresses, subscriber account identifiers, or other identifying information.

[0045] This disclosure further assumes that entities responsible for collecting, analyzing, disclosing, transmitting, storing, or otherwise using such personal data adhere to best data protection regulations and / or practices. In particular, such entities should implement and consistently use data protection policies and practices that are generally recognized as meeting or exceeding industry or regulatory requirements for maintaining and protecting the confidentiality of personal data. For example, personal data should be collected from users for legitimate and appropriate uses by the entity and should not be shared or sold outside of those legitimate uses. Furthermore, such collection should only occur after the informed consent of the users has been obtained.In addition, such entities would take all necessary steps to protect and secure access to such personal data and ensure that others who have access to the personal data adhere to their data protection policies and procedures. Furthermore, such entities may submit to third-party evaluation to confirm that they adhere to generally accepted data protection policies and practices.

[0046] In the case of advertising delivery services, the present disclosure also contemplates embodiments in which users selectively block the use of or access to personal data. That is, the present disclosure contemplates that hardware and / or software elements may be provided to prevent or block access to such personal data. For example, in the case of advertising delivery services, the present technology may be configured to allow users to select whether to participate ("opt in") or decline ("opt out") in the collection of personal data during registration for services.

[0047] Although the present disclosure broadly covers the use of personal information data to implement one or more of the various disclosed embodiments, the present disclosure also contemplates that the various embodiments may be implemented without the need for access to such personal information data. That is, the various embodiments of the present technology will not be rendered inoperable due to the absence of all or a portion of such personal information data.For example, content may be selected and delivered to users by inferring preferences based on non-personal data or a mere minimum amount of personal information, such as the content requested by a user's associated device, other non-personal information available to the content delivery services, or publicly available information. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 17 / 985,098 f

[0016] US 8,892,391 f

[0024]

Claims

[1] Method comprising: at a first point in time: Obtaining, with at least one processor, a first set of sensor data from sensors of a wearable device, a location of the wearable device, and a timestamp; determining, with the at least one processor, a first set of fitness measurements based on the sensor data; predicting, with the at least one processor, a baseline health profile for a user of the wearable device based on the first set of fitness measurements; Storing, with the at least one processor, the basic health profile, the location and the timestamp; at a second point in time following the timestamp: Detecting, with the at least one processor, that the wearable device is at the location; Obtaining, with at least one processor, a second set of sensor data from the sensors of the wearable device; determining, with the at least one processor, a second set of fitness measurements based on the second set of sensor data; predicting, with the at least one processor, a current health profile for the user of the wearable device based on the second set of fitness measurements; Comparing, with the at least one processor, the current health profile with the base health profile; and in response to a result of comparing, performing an action. [2] The method of claim 1, wherein the sensors include inertial sensors, a heart rate sensor, and at least one in-ear device. [3] The method of claim 1, wherein predicting the baseline and current health profiles includes providing the first and second sets of fitness measurements as inputs to a machine learning model that is trained to predict an overall fitness score for the user. [4] The method of claim 3, wherein the machine learning model is trained using health profiles of other people at the location who have a similar health profile to the user. [5] The method of claim 3, wherein the machine learning model is trained on extended historical health profiles of the user. [6] The method of claim 1, wherein the first and second sets of fitness measures include at least one of gait analysis, walking stability, or cardiorespiratory fitness. [7] The method of claim 1, wherein the action includes sending an alert message to the user. [8] System comprising: at least one processor; A memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform any of the above methods according to claims 1 to 7. [9] A computer-readable medium having stored thereon instructions which, when executed by at least one processor, cause the at least one processor to perform any of the above methods according to claims 1 to 7.

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