Detecting biomechanical damage using wearable devices

Abstract

The disclosure relates to detecting biomechanical damage using a wearable device. An example method includes obtaining a first set of sensor data, a location of the wearable device, and a timestamp; determining a first set of health indicators based on the sensor data; predicting a baseline health profile based on the first set of health condition indicators; storing the baseline health profile, location, and timestamp; at a second time after the timestamp: detecting that the wearable device is located at the location; acquiring a second set of sensor data from a sensor of the wearable device; determining a second set of health indicators based on the second set of sensor data; predicting a current health profile of the user based on the second set of health condition indicators; comparing the current health profile to the baseline health profile; and performing an action in response to a result of the comparison.

Description

Technical Field

[0001] The present disclosure relates generally to health monitoring. Background Art

[0002] Individuals move their bodies and limbs in highly unique and repeatable patterns, such as based on pelvic rotation, pelvic tilt, knee and hip flexion, knee and ankle interaction, and lateral pelvic displacement. Changes in these determinants can indicate health problems, such as injuries or diseases. Changes due to progressive diseases or aging can occur slowly over months or years, making it difficult for individuals or their doctors to observe these changes during semi-annual or annual examinations. However, it is desirable to detect them for a long time before such biomechanical aging or disease processes are observed in a clinical setting, so that before they develop into more serious health problems, individuals or doctors can prescribe preventive treatment prescriptions and evaluate the effectiveness of the preventive treatment prescriptions. Summary of the invention

[0003] Embodiments of detecting biomechanical injuries using a wearable device are disclosed herein. In some embodiments, a method includes: obtaining a first set of sensor data, a location of a wearable device, and a timestamp; determining a first set of health indicators based on the sensor data; predicting a baseline health profile based on the first set of health indicators; storing the baseline health profile, the location, and the timestamp; at a second time after the timestamp: detecting that the wearable device is located at the location; obtaining a second set of sensor data from a sensor of the wearable device; determining a second set of health indicators based on the second set of sensor data; predicting a current health profile of a user based on the second set of health indicators; comparing the current health profile to the baseline health profile; and performing an action in response to the result of the comparison.

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

[0005] In some embodiments, predicting the baseline health profile and the current health profile includes providing the first set of health status indicators and the second set of health status indicators as input to a machine learning model that is trained to predict an overall health status score of the user.

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

[0007] In some embodiments, the machine learning model is trained on the user's enhanced historical health profile.

[0008] In some embodiments, the first set of health indicators and the second set of health indicators include at least one of gait analysis, walking stability, or cardiopulmonary fitness.

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

[0010] Other embodiments relate to apparatus, systems, and computer-readable media.

[0011] Specific embodiments described herein provide one or more of the following advantages. The systems and methods of the present disclosure use machine learning to generate a baseline user health profile that changes over time (e.g., one month) based on sensor measurements output by various sensors of a wearable device (e.g., a smartphone, a smart watch, a fitness device, a headset, a head-mounted device, a chest-mounted device). The user baseline health profile can be associated with a specific geographic location and / or time. Detection of a sudden or long-term change in the user's baseline health profile can trigger a notification to the user (e.g., a visual, audio, or tactile notification) indicating that the user's current health profile deviates (e.g., deteriorates) from the user's baseline health profile, as well as specific indicators that cause the deviation (e.g., uneven gait, changes in walking speed, breathing patterns, bone vibrations, internal sounds, bruxism, joint cracking). BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a block diagram of a system for detecting biomechanical injuries using a wearable device according to one or more embodiments.

[0013] Figure 2 is a flow chart of a process for detecting biomechanical injuries using a wearable device according to one or more embodiments.

[0014] Figure 3 It is used to implement reference Figure 1 to Figure 2 A block diagram of the device architecture that describes the features and processes. DETAILED DESCRIPTION

[0015] Figure 1is a flow chart of a process 100 for detecting biomechanical injuries using a wearable device according to one or more embodiments. The process 100 includes determining whether a wearable device is available for recording (101). For example, the wearable device can be a smartphone, a smart watch, a headset, a chest-mounted device, a head-mounted device (HMD), etc. If the wearable device is powered on and positioned on the user, the wearable device can be used for recording so that one or more sensors of the wearable device can make sensor measurements. For example, if the headset is powered on and inserted into the user's ear, the headset can be used for recording. The microphone in the headset captures sounds inside the user. Another example is a heart rate sensor embedded in a smart watch attached to the user's wrist, which is used to measure the user's heart rate. For example, a wearable device that is not available for recording refers to a wearable device that is not on the user or is powered off. In some embodiments, a machine learning model is trained to detect whether the wearable device is on the user, such as detecting whether the smartphone is in a trouser pocket.

[0016] If at least one wearable device is available for recording, the sensor measurements are recorded 103 on the wearable device and stored on the wearable device and / or uploaded to a network storage device, or stored 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, body sounds (e.g., joint cracking, breathing, teeth grinding, etc.), heart rate, user body movements (e.g., acceleration, rotation rate, step frequency step length), breathing noise captured by an external microphone of the wearable device (e.g., heavy breathing, breath holding, gasping), electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, temperature, VO2max, and blood volume pulse (BVP) obtained from a photoplethysmography (PPG) sensor, etc. Various other measurements or indicators can be derived from the motion sensor data, including, but not limited to, the user's walking pattern (e.g., falling and stabilizing), walking speed, gait balance, etc.

[0017] The sensor data may be captured simultaneously at the same data rate or at different data rates. In addition to the sensor data, the user's location at which the sensor data was acquired and a timestamp indicating the date and time the sensor data was acquired are also recorded and stored with the sensor data. The sensor data may be captured / measured continuously, captured / measured periodically, and / or captured / measured in response to a triggering event.

[0018] In some embodiments, the stored sensor data is input into a machine learning model 104 that is trained or configured to predict the user's health profile. Some examples of machine learning models include, but are not limited to, deep learning networks (e.g., convolutional neural networks (CNNs)), k-nearest neighbor clustering (KNN), naive Bayes, regression analysis (linear and logical), 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 into a machine learning model that is trained to predict the user's health profile 106. In some embodiments, the machine learning model is trained using the health profiles 105 of other individuals at the location who have similar health profiles to the user. In some embodiments, the machine learning model is trained on the user's enhanced historical health profile to increase the amount of training data.

[0019] In some embodiments, the machine learning model predicts a baseline health profile 106. Ideally, this prediction occurs when the user is healthy and not injured. The baseline health profile 106 may include one or more health scores or indicators indicating the user's health status. For example, the machine learning model can predict the user's overall health score based on the user's gait analysis, as described in U.S. Patent Publication No. 2021 / 0393166, "Monitoring User Health Using Gait Analysis," published on December 23, 2021, the entire contents of which are incorporated herein by reference. In some embodiments, the machine learning model can predict the user's cardiopulmonary health, as described in co-pending U.S. Patent Application No. 17 / 985,098, "Identifying Poor Cardiorespiratory Fitness Using Sensors of Wearable devices," filed on November 11, 2022, the entire contents of which are incorporated herein by reference. In some embodiments, the machine learning model can predict walking stability, as described in U.S. Patent Publication No. 2023 / 0112071 A1, “Assessing Fall Risk of Mobile Device User,” published on April 13, 2023, the entire contents of which are incorporated herein by reference.

[0020] In some embodiments, a trained machine learning model is used to determine whether the wearable device is on the user's dominant or inferior leg (e.g., a smartphone in the right front pocket or the left front pocket) to determine whether the user is limping, which may be an indicator of ill-fitting shoes, a strain, sprain or object such as debris stuck in the foot, imbalance, a fracture, etc.

[0021] Thus, the scores or indicators calculated by the above process can include the user's baseline health profile 106. If these scores or indicators change by a specified amount from the baseline, a notification 107 (e.g., push notification, text message, display) can be sent to the user's wearable device or other device to alert the user of the change and indicate which score / indicator caused the change. In some embodiments, with the user's permission, the user's health profile 106 can be included in the user's medical record 108, where a health professional can access the user's health profile.

[0022] For example, in the first month, when the user wears the device, the user's smartwatch and headphones acquire the first set of sensor data. Sensors in the smartwatch measure motion data (e.g., shake, vibration) and heart rate. The microphone on the smartwatch captures the user's breathing. The microphone in the headphones captures internal body sounds from the top of the neck. As described above, the sensor data is used to derive additional health indicators that indicate the user's gait, walking stability, and cardiopulmonary health. These indicators are input into a machine learning model that predicts the user's overall health score. The system searches for the user's health profile stored on the wearable device, a companion device, or the network. Since this is the first instance, the system creates a new user health profile and marks it as a baseline health profile. The overall health score is included in the baseline health profile stored on the smartwatch and / or companion device and / or network storage.

[0023] In addition, a global navigation satellite system (GNSS) receiver (e.g., a global positioning system (GPS) receiver) on the smartwatch determines the location of the smartwatch. The location and timestamp are also stored in the baseline health profile. In addition, an activity classifier on the wearable device provides an indication of the activity the user was engaged in when the sensor data was acquired (e.g., walking, jogging, bicycling, driving).

[0024] The wearable device monitors the user's location (e.g., from GPS output) and time to determine when the user is in the same location where the baseline user profile was created, and a threshold amount of time after the baseline user health profile was created (e.g., 1 month later). In some embodiments, a geo-fence is set around the location, which triggers the acquisition of a second set of sensor data when the user crosses it.

[0025] The second set of sensor data is acquired through the user's smart watch and headphones. Similarly, the second set of sensor data includes motion data, heart rate breathing, and body sounds. The second set of sensor data is input into a machine learning model that predicts a second overall health score. The second overall health score is compared to the stored first overall health score stored in the baseline user health profile. If the second overall health score is lower than the baseline overall health score by a specified amount, an action is performed, such as sending an alert message to the user.

[0026] An alert message may be displayed on a smartwatch or companion device, and / or a push notification or text message may be sent to the wearable or other device. The alert message may inform the user that their overall health has deteriorated since last month. The current health profile becomes the new stored baseline health profile so that it can be compared to the user's calculated new health profile for next month, and so on.

[0027] As described above, an activity classifier on the wearable device can determine the activity that the user is engaging in when the sensor data is acquired. In some embodiments, if the activity that the user is currently engaging in is different from the activity that the user was engaging in when the baseline health profile was created last month, then the overall health indicator is not calculated. An example activity classifier is described in U.S. Patent No. 8,892,391, “Activity Detection,” issued on November 18, 2014, which is incorporated herein by reference in its entirety.

[0028] In some embodiments, individual indicators that contribute to the overall health score (e.g., walking flexibility indicators, cardiopulmonary indicators) can be provided in the alert message so that the user can see which specific aspects of their health profile have caused the overall health score to deteriorate compared to the baseline overall health score of the previous month. In this example, the door analysis indicator indicates that the user is falling and stabilizing himself, and the cardiopulmonary indicator indicates that the recovery time after performing physical activity is longer than usual. This information can encourage users to pay attention to their own health and provide useful health information that users can share with their doctors.

[0029] 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 the settings pane of the wearable device or through other suitable input. The user can also select preferences for receiving alert messages (e.g., push notification, text message, etc.) and the desired format of the health profile summary (e.g., determining which indicators are displayed).

[0030] In some embodiments, the above content is part of a health-related mobile application. A user can request real-time analysis through the application to check whether, for example, a new pair of shoes fits. For example, ill-fitting shoes may affect the user's walking gait. Users can also request the application to track their spinal health, physical improvement, and posture in response to treatment. For example, tracking the progress of adjustments by a chiropractor or walking balance. In some embodiments, feedback can be used to implement a weight loss / weight gain plan.

[0031] Figure 2 is a flow chart of a process 200 for detecting biomechanical injuries using a wearable device according to one or more embodiments. The process 200 may be described in, for example, Figure 3 The described device architecture 300 is implemented.

[0032] 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 health indicators based on the sensor data (202); predicting a baseline health profile of a user of the wearable device based on the first set of health indicators (203); storing the baseline health profile, location, and timestamp (204); at a second time after the timestamp: detecting that the wearable device is located at the location, obtaining a second set of sensor data from sensors of the wearable device (205); determining a second set of health indicators based on the second set of sensor data (206); predicting a current health profile of the user of the wearable device based on the second set of health data (207); comparing the current health profile with the baseline health profile (208); and performing an action in response to the comparison result (209). Previous reference Figure 1 Describe each of these steps.

[0033] In some embodiments, the sensor includes an inertial sensor, a heart rate sensor, and at least one in-ear device that can measure, for example, body sounds (e.g., cracks) at the top of the neck. In some embodiments, predicting a baseline health profile and a current health profile includes providing a first set of health status indicators and a second set of health status indicators as inputs to a machine learning model that is trained to predict the user's overall health status score. For example, the machine learning model can be a deep learning neural network that is trained based on the health profiles of other individuals of the same age and similar health profiles. In some embodiments, the first set of health status indicators and the second set of health status indicators include at least one of gait analysis, walking stability, or cardiopulmonary health. In some embodiments, the action taken includes sending an alert notification to the user, which can be in the form of a push notification, a text message, a visual display, tactile feedback, audio feedback, and the like.

[0034] Figure 3 It is used to implement reference Figure 1 to Figure 2 300. The system architecture 300 may include a memory interface 302, one or more hardware data processors, image processors, and / or processors 304, and a peripheral device interface 306. The memory interface 302, one or more processors 304, and / or the peripheral device interface 306 may be separate components or may be integrated into one or more integrated circuits. The system architecture 300 may be included in any suitable electronic device for collision detection, including but not limited to: smart watches, smartphones, fitness bands, and any other device that can be attached, worn, or held by a user.

[0035] Sensors, devices, and subsystems may be coupled to the peripheral device interface 306 to provide multiple functions. For example, one or more motion sensors 310, light sensors 312, and proximity sensors 314 may be coupled to the peripheral device interface 306 to facilitate motion sensing (e.g., acceleration, rotation rate), lighting, and proximity functions of the wearable device. A location processor 315 may be connected to the peripheral device interface 306 to provide geolocation. In some specific 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 connected to the peripheral device interface 306 to provide data that can be used to determine the direction of magnetic north. The electronic magnetometer 316 may provide data to an electronic compass application. The motion sensor 310 may include one or more accelerometers and / or gyroscopes, which are configured to determine changes in speed and direction of movement. The barometer 317 may be configured to measure atmospheric pressure (e.g., pressure changes inside a vehicle). The biosignal sensor 320 may be one or more of: a PPG sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an electromyogram (EMG) sensor, a myoelectric (MMG) sensor for measuring muscle activity / contraction (e.g., a piezoresistive sensor), an electrooculogram (EOG) sensor, a galvanic skin response (GSR) sensor, a magnetoencephalogram (MEG) sensor, and / or other suitable sensors configured to measure biosignals.

[0036] Communication functions may be facilitated 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 specific design and implementation of communication subsystems 324 may depend on the communication network or networks over which the mobile device is expected to operate. For example, architecture 300 may include a device designed to communicate over a GSM network, a GPRS network, an EDGE network, a WiFi network, or a wireless network.™ Network and Bluetooth ™ Network-operated communication subsystem 324. Specifically, the wireless communication subsystem 324 may include a host protocol so that the collision device can be configured as a base station for other wireless devices.

[0037] The audio subsystem 326 may be coupled to a speaker 328 and a microphone 330 to facilitate voice-enabled functions such as voice recognition, voice replication, digital recording, and telephony functions. The audio subsystem 326 may be configured to receive voice commands from a user.

[0038] The I / O subsystem 340 may include a touch surface controller 342 and / or 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 detect contact and movement or interruption thereof, for example, using any of a variety of touch-sensitive technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave 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 include, for example, a touch screen or a digital crown for a smart watch. The I / O subsystem 340 may include a haptic engine or device for providing tactile feedback (e.g., vibration) in response to commands from the processor 304. In an embodiment, the touch surface 346 may be a pressure-sensitive surface.

[0039] Other input controllers 344 may be coupled to other input / control devices 348, such as one or more buttons, rocker switches, thumbwheels, infrared ports, and USB ports. One or more buttons (not shown) may include up / down buttons for volume control of speaker 328 and / or microphone 330. Touch surface 346 or other controllers 344 (e.g., buttons) may include or be coupled to fingerprint identification circuitry for use with fingerprint authentication applications to authenticate a user based on the user's fingerprint.

[0040] In one implementation, pressing a button for a first duration may unlock the touch surface 546, and pressing the button for a second duration longer than the first duration may turn the power of the mobile device on or off. The user can customize the functionality of one or more buttons. For example, the touch surface 346 may also be used to implement virtual or soft buttons.

[0041] In some implementations, the mobile device can present recorded audio files and / 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.

[0042] 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 an operating system 352, such as the iOS operating system developed by Apple Inc. of Cupertino, California. The operating system 352 may include instructions for handling basic system services and for performing hardware-related tasks. In some implementations, the operating system 352 may include a kernel (e.g., a UNIX kernel).

[0043] The memory 350 may also store communication instructions 354 that facilitate communication with one or more additional devices, one or more computers, and / or one or more servers, such as, for example, instructions of a software stack for implementing wired or wireless communication with other devices. The memory 350 may include graphical user interface instructions 356 that facilitate graphical user interface processing; sensor processing instructions 358 that facilitate sensor-related processing and functions; phone instructions 360 that facilitate phone-related processes and functions; electronic messaging instructions 362 that facilitate electronic messaging-related processes and functions; web browsing instructions 364 that facilitate web browsing-related processes and functions; media processing instructions 366 that facilitate media processing-related processes and functions; GNSS / location instructions 368 that facilitate general GNSS and location-related processes and instructions; and reference instructions 369 that facilitate general GNSS and location-related processes and instructions. Figure 1 to Figure 2 The memory 350 also includes other application program instructions 372, which include but are not limited to instructions for implementing Figure 1 The system 100 is shown as an application program.

[0044] Each of the instructions and applications identified above may correspond to an instruction set for performing one or more of the functions described above. These instructions need not be implemented as separate software programs, processes, or modules. Memory 350 may include additional instructions or fewer instructions. In addition, various functions of the mobile device may be performed in hardware and / or software, including in one or more signal processing and / or application specific integrated circuits.

[0045] Although this specification includes many specific implementation details, these specific implementation details should not be understood as limitations on the scope of any invention or possible content to be protected, but should be understood as descriptions of features specific to a particular embodiment of a particular invention. Certain features described in the context of different embodiments in this specification may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in the form of any suitable sub-combination. In addition, although some features may be described above as working with certain combinations and even initially claimed in this way, one or more features of the claimed combination may be removed from the combination in some cases, and the claimed combination may involve a sub-combination or a variation of the sub-combination.

[0046] Similarly, although the operations are shown in a specific order in the accompanying drawings, this should not be understood as requiring such operations to be performed in a sequential order or in the specific order shown, or requiring the execution of all the operations shown to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. In addition, the division of each system component in the above-mentioned embodiment should not be understood as requiring such division in all embodiments, and it should be understood that the program components and the system can be generally integrated together in a single software product or encapsulated in multiple software products.

[0047] As described above, some aspects of the subject matter of this specification include the collection and use of data from various sources to improve the services that mobile devices can provide to users. This disclosure contemplates that, in some cases, the collected data may identify a specific location or address based on device usage. Such personal information data may include location-based data, addresses, subscriber account identifiers, or other identifying information.

[0048] The disclosure also contemplates that entities responsible for the collection, analysis, disclosure, transmission, storage or other purposes of such personal information data will comply with established privacy policies and / or privacy practices. Specifically, such entities should implement and adhere to the use of privacy policies and practices that are recognized as meeting or exceeding the industry or government requirements for maintaining the privacy and security of personal information data. For example, personal information from users should be collected for the legal and reasonable purposes of the entity, and not shared or sold outside these legal purposes. In addition, such collection should only be carried out after the user's informed consent. In addition, such entities should take any necessary steps to safeguard and protect access to such personal information data, and ensure that others who can access personal information data comply with their privacy policies and procedures. In addition, such entities can subject themselves to third-party assessments to prove that they comply with widely accepted privacy policies and practices.

[0049] In the case of an advertisement delivery service, the present disclosure also contemplates implementations in which users selectively block the use or access of personal information data. That is, the present disclosure contemplates providing hardware elements and / or software elements to prevent or block access to such personal information data. For example, in the case of an advertisement delivery service, the technology of the present invention may be configured to allow a user to choose to "opt in" or "opt out" of participating in the collection of personal information data during registration for the service.

[0050] Thus, while 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 various embodiments may also be implemented without access to such personal information data. That is, various embodiments of the present technology will not fail to function properly due to the lack of all or a portion of such personal information data. For example, content may be selected and delivered to a user by inferring preferences based on non-personal information data or an absolute minimum amount of personal information, such as content requested by a device associated with the user, other non-personal information available to a content delivery service, or publicly available information.

Claims

1. A method comprising: At the first time: acquiring, using at least one processor, a first set of sensor data from a sensor of a wearable device, a location of the wearable device, and a timestamp; determining, with the at least one processor, a first set of health status metrics based on the sensor data; predicting, with the at least one processor, a baseline health profile of a user of the wearable device based on the first set of health metrics; storing, with the at least one processor, the baseline health profile, the location, and the timestamp; At a second time after said timestamp: detecting, using the at least one processor, that the wearable device is located at the location; acquiring, using at least one processor, a second set of sensor data from a sensor of the wearable device; determining, with the at least one processor, a second set of health metrics based on the second set of sensor data; predicting, with the at least one processor, a current health profile of the user of the wearable device based on the second set of health metrics; comparing, with the at least one processor, the current health profile to the baseline health profile; as well as An action is performed in response to a result of the comparison.

2. The method of claim 1, wherein the sensors include an inertial sensor, a heart rate sensor, and at least one in-ear device.

3. A method according to claim 1, wherein predicting the baseline health profile and the current health profile includes providing the first set of health status metrics and the second set of health status metrics as input to a machine learning model, and the machine learning model is trained to predict the overall health status score of the user.

4. The method of claim 3, wherein the machine learning model is trained using health profiles of other individuals at the location who have similar health profiles as the user.

5. The method of claim 3, wherein the machine learning model is trained on an enhanced historical health profile of the user. 6 . The method of claim 1 , wherein the first set of health metrics and the second set of health metrics include at least one of gait analysis, walking stability, or cardiorespiratory fitness. The method of claim 1 , wherein the action comprises sending an alert notification to the user.

8. A system comprising: at least one processor; A memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform any of the aforementioned methods according to claims 1 to 7.

9. A computer-readable medium having instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform any of the methods described in claims 1 to 7.

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