Motion data processing method and system

Abstract

One or more embodiments of the present specification relate to a motion data processing method and system, the method comprising: acquiring motion data of a user at different frequency, the motion data comprising at least an electromyographic signal; determining a physiological state of the user based on the motion data; and determining a recommended stride frequency of the user based on the physiological state.

Description

A motion data processing method and system Technical Field

[0001] This specification relates to the technical field of data acquisition and processing, and in particular to a motion data processing method and system. Background Art

[0002] As people pay more and more attention to their physical health, scientific running becomes more and more important. The cadence and stride length during running can be used to measure whether people's running is scientific. Generally speaking, stride length is closely related to personal height, leg length, joint flexibility, and core strength. In comparison, cadence is easier to change through training, thereby improving the exercise effect during running. The current method of determining cadence is usually to use the standard cadence of runners (for example, 180 steps per minute) as the recommended running cadence for the general population. Taking into account the differences in physical fitness between the general population and athletes, as well as the differences in physical fitness between the general population, the cadence of runners may not be suitable for every general population.

[0003] Therefore, it is particularly important to determine the cadence that is suitable for users to help them run more safely and scientifically.

[0004] Summary of the Invention

[0005] An embodiment of the present specification provides a motion data processing method, including: obtaining motion data of a user at different cadences, the motion data including at least an electromyographic signal; determining the user's physiological state based on the motion data; and determining the user's recommended cadence based on the physiological state.

[0006] In some embodiments, obtaining the user's motion data at different cadences includes: determining a target speed interval; and obtaining the motion data at different cadences within the target speed interval based on the target speed interval.

[0007] In some embodiments, the physiological state includes muscle efficiency, muscle tension and muscle fatigue, and determining the recommended cadence of the user based on the physiological state includes: determining the correspondence between the physiological state and cadence of the user based on at least one of the muscle efficiency, muscle tension and muscle fatigue; and determining the recommended cadence of the user based on the correspondence.

[0008] In some embodiments, determining the physiological state of the user based on the motion data includes: determining at least one of the muscle efficiency, the muscle tension, and the muscle fatigue of the user based on the electromyographic signal.

[0009] In some embodiments, the motion data includes an electrocardiogram signal and / or a posture signal, and determining the recommended cadence of the user based on the physiological state includes: determining the correspondence between the physiological state and the cadence of the user based on the electrocardiogram signal and / or the posture signal; and determining the recommended cadence of the user based on the correspondence.

[0010] In some embodiments, determining the user's physiological state based on the motion data includes: determining the user's physiological state based on the electrocardiogram signal and / or posture signal, wherein the physiological state includes the risk of injury from the user's motion.

[0011] In some embodiments, obtaining the user's motion data at different cadences includes: obtaining the user's real-time cadence and real-time motion data; dividing the real-time cadence into multiple cadence intervals; and determining the motion data within different cadence intervals from the real-time motion data based on the cadence intervals.

[0012] In some embodiments, the real-time cadence is obtained through an inertial sensor, or the real-time cadence is obtained from the electromyographic signal.

[0013] In some embodiments, the method further includes: feeding back the recommended cadence to the user.

[0014] In some embodiments, the feeding back the recommended cadence to the user includes: obtaining the user's real-time cadence; determining the user's cadence adjustment trend based on the frequency difference between the real-time cadence and the recommended cadence; and guiding the user based on the cadence adjustment trend until the user's cadence is the same as the recommended cadence.

[0015] In some embodiments, the feeding back the recommended cadence to the user includes: feeding back a beat that is the same as the recommended cadence to the user based on a preset feedback method.

[0016] In some embodiments, the preset feedback method includes at least one of voice feedback, vibration feedback, light feedback, and display feedback.

[0017] In some embodiments, the feeding back the recommended cadence to the user includes: stimulating a body part of the user based on a biofeedback method to feed back the recommended cadence to the user, wherein the stimulation has a stimulation frequency, and the stimulation frequency is the same as the recommended cadence.

[0018] In some embodiments, obtaining the user's motion data at different cadences includes: obtaining multiple sets of motion data of the user at multiple preset cadences, each set of the multiple sets of motion data corresponding to one of the multiple preset cadences.

[0019] In some embodiments, obtaining multiple sets of motion data of the user at multiple preset step frequencies includes: obtaining physiological information of the user, the physiological information including body information and motion information; and determining the multiple sets of motion data of the user at the multiple preset step frequencies based on the physiological information.

[0020] An embodiment of this specification also provides a motion data processing system, including: an acquisition module for acquiring motion data of a user at different cadences, wherein the motion data includes at least electromyographic signals; a processing module for determining the physiological state of the user based on the motion data; and a recommendation module for determining a recommended cadence of the user based on the physiological state.

[0021] In some embodiments, the system further includes a feedback module configured to provide the user with feedback of the recommended cadence.

[0022] An embodiment of this specification also provides a wearable device, comprising: a wearable body, on which is provided at least one sensor, the sensor being used to obtain motion data of a user; and a processor, configured to execute the motion data processing method described in any embodiment of this specification.

[0023] In some embodiments, the wearable body is further provided with electrodes that contact the user's skin, and the electrodes are used to stimulate parts of the user's body. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0025] FIG1 is an exemplary scenario diagram of a motion data processing system according to some embodiments of the present specification;

[0026] FIG2 is an exemplary module diagram of a motion data processing device according to some embodiments of this specification;

[0027] FIG3 is an exemplary flow chart of a method for processing motion data according to some embodiments of this specification;

[0028] FIG4 is an exemplary flow chart of a method for acquiring motion data according to some embodiments of this specification;

[0029] FIG5 is an exemplary flowchart of determining a recommended cadence according to some embodiments of this specification;

[0030] FIG6 is an exemplary flowchart of a method for providing feedback and recommending cadence according to some embodiments of this specification. DETAILED DESCRIPTION

[0031] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0032] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0033] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0034] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0035] FIG1 is an exemplary scenario diagram of a motion data processing system according to some embodiments of the present specification. As shown in FIG1 , the motion data processing system 100 may include a processing device 110, a network 120, a storage device 130, a terminal device 140, and a data acquisition device 150. The various components in the motion data processing system 100 can be connected in a variety of ways. For example, the data acquisition device 150 can be connected to the storage device 130 and / or the processing device 110 via the network 120, or it can be directly connected to the storage device 130 and / or the processing device 110. For another example, the storage device 130 can be directly connected to the processing device 110 or connected via the network 120. For another example, the terminal device 140 can be connected to the storage device 130 and / or the processing device 110 via the network 120, or it can be directly connected to the storage device 130 and / or the processing device 110. In some embodiments, the motion data processing system 100 may further include a wearable device 160, and the data acquisition device 150 is disposed on the wearable device 160 (e.g., a top 160-1, pants 160-2, or a belt 160-3). The wearable device 160 is connected to various components of the motion data processing system 100 via the data acquisition device 150.

[0036] In some embodiments, the motion data processing system 100 can determine a recommended cadence for a user while running by implementing the methods and / or processes disclosed herein. For example, when a user wears the wearable device 160 while running, the data acquisition device 150 can be attached to the user's skin to collect motion data of the user while running. The processing device 110 can directly or indirectly obtain the motion data (e.g., electromyographic signals, posture signals, electrocardiogram signals, etc.) from the data acquisition device 150 and determine the user's physiological state based on the motion data, thereby determining the user's recommended cadence for running.

[0037] The processing device 110 can process data and / or information obtained from the data acquisition device 150, the storage device 130, the terminal device 140, and / or other components of the motion data processing system 100. In some embodiments, the processing device 110 can obtain the user's motion data from any one or more of the data acquisition device 150, the storage device 130, or the terminal device 140, and process the motion data to determine the user's recommended cadence. In some embodiments, the processing device 110 can retrieve pre-stored computer instructions from the storage device 130 and execute the computer instructions to implement the motion data processing methods described herein.

[0038] In some embodiments, the processing device 110 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 110 may be local or remote. For example, the processing device 110 may access information and / or data from the data acquisition device 150, the storage device 130, and / or the terminal device 140 via the network 120. For another example, the processing device 110 may be directly connected to the data acquisition device 150, the storage device 130, and / or the terminal device 140 to access information and / or data. In some embodiments, the processing device 110 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud cloud, a multi-cloud, or the like, or any combination thereof.

[0039] The network 120 can connect the various components of the motion data processing system 100 and / or connect the motion data processing system 100 with external resources. The network 120 can enable the various components of the motion data processing system 100 to communicate with each other, as well as with other parts outside the motion data processing system 100, to promote the exchange of data and / or information. For example, the processing device 110 can obtain motion data from the data acquisition device 150 and / or the storage device 130 through the network 120. For another example, the processing device 110 can control the terminal device 140 through the network 120 to provide the user with a recommended cadence. Exemplary feedback methods may include at least one of voice feedback, vibration feedback, light feedback, display feedback, etc.

[0040] In some embodiments, the network 120 can be any form of wired or wireless network, or any combination thereof. By way of example only, the network 120 can include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 120 can include one or more network entry and exit points. For example, the network 120 can include wired or wireless network entry and exit points, such as base stations and / or internet exchange points 120-1, 120-2, ..., through which one or more components of the motion data processing system 100 can be connected to the network 120 to exchange data and / or information. For example, motion data, the user's physiological state, and other data can be transmitted through the network 120.

[0041] The storage device 130 can store data, instructions and / or any other information. In some embodiments, the storage device 130 can store data obtained from the data acquisition device 150, the processing device 110 and / or the terminal device 140. For example, the storage device 130 can store motion data collected by the data acquisition device 150. In some embodiments, the storage device 130 can store data and / or instructions used by the processing device 110 to execute or use to complete the exemplary methods described in this specification. In some embodiments, the storage device 130 may include a large-capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. Exemplary large-capacity memories may include magnetic disks, optical disks, solid-state disks, etc. In some embodiments, the storage device 130 can be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof.

[0042] In some embodiments, the storage device 130 can be connected to the network 120 to communicate with at least one other component in the sports data processing system 100 (e.g., the data acquisition device 150, the processing device 110, the terminal device 140). At least one component in the sports data processing system 100 can access data, instructions, or other information stored in the storage device 130 via the network 120. In some embodiments, the storage device 130 can be directly connected to or communicate with one or more components in the sports data processing system 100 (e.g., the data acquisition device 150, the terminal device 140). In some embodiments, the storage device 130 can be part of the data acquisition device 150 and / or the processing device 110.

[0043] The terminal device 140 can receive, send and / or display data. The data received by the terminal device 140 may include data collected by the data acquisition device 150, data stored in the storage device 130, the recommended cadence generated by the processing device 110, etc. For example, the data received and / or displayed by the terminal device 140 may include motion data collected by the data acquisition device 150, the user's physiological state data obtained by processing the motion data by the processing device 110, the recommended cadence determined by the processing device 110 based on the physiological state, etc. The data sent by the terminal device 140 may include user input data and instructions, etc. For example, the terminal device 140 can send the operation instructions input by the user to the data acquisition device 150 through the network 120 to control the data acquisition device 150 to perform corresponding data acquisition (such as electromyographic signals, electrocardiographic signals, posture signals, etc.).

[0044] In some embodiments, the terminal device 140 may include a mobile device 141, a tablet computer 142, a laptop computer 143, etc., or any combination thereof. In some embodiments, the mobile device 141 may include a mobile phone, a smart mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a personal digital assistant (PDA), a gaming device, a navigation device, a POS device, etc., or any combination thereof. In some embodiments, the virtual reality device and / or the augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, etc., or any combination thereof. In some embodiments, the processing device 110 may be part of the terminal device 140.

[0045] It should be noted that the motion data processing system 100 may not include the terminal device 140, and the functions or some of the functions of the terminal device 140 may be performed by the wearable device 160. For example, the wearable device 160 may control the data acquisition device 150 to perform corresponding data acquisition. For another example, the data displayed to the user by the terminal device 140 (such as the recommended cadence) may be executed by the wearable device 160 (e.g., the wearable device 160 provides the user with the recommended cadence feedback through electrical stimulation).

[0046] The data acquisition device 150 may be a device for collecting motion data from a user. Motion data may refer to signals generated by a user while running. Exemplary motion data may include one or more of electromyographic signals, posture signals, electrocardiographic signals, respiratory signals, sweat signals, and mechanical signals. In some embodiments, the data acquisition device 150 may include an electromyographic data acquisition device, a posture data acquisition device, and a mechanical data acquisition device. In some embodiments, the electromyographic data acquisition device may include one or more electrodes. For example, the electromyographic data acquisition device may include multiple electrodes, which may be located at different locations on the wearable device 160 to conform to different parts of the user (e.g., chest, back, elbows, legs, abdomen, wrists, etc.), thereby collecting electromyographic signals from different parts of the user. In some embodiments, the posture data acquisition device may include a velocity sensor, an inertial sensor (e.g., an accelerometer, an angular velocity sensor (e.g., a gyroscope), etc.), an optical sensor (e.g., an optical distance sensor, a video / image collector), an acoustic distance sensor, a tension sensor, etc., or any combination thereof. In some embodiments, the mechanical data acquisition device may include a pressure sensor. For example, pressure sensors may be located at different parts of the user to collect pressure signals from different parts of the user. In some embodiments, the data acquisition device 150 may also include an electrocardiogram (ECG) data acquisition device, a respiratory data acquisition device, a sweat data acquisition device, and the like. For example, the ECG data acquisition device may include multiple electrodes, which may be adapted to fit different parts of the user (e.g., fit the user's abdomen via the waist belt device 160-3) to collect the user's ECG signals. For another example, the respiratory data acquisition device may include a respiratory rate sensor, a flow sensor, and the like, respectively adapted to detect signals such as the user's respiratory rate and gas flow during exercise. For another example, the sweat data acquisition device may include multiple electrodes in contact with the user's skin, adapted to detect the user's sweat flow, analyze sweat composition, and the like. In some embodiments, the data acquisition device 150 may have an independent power supply, which may transmit the collected data to other components (e.g., the processing device 110, the storage device 130, and the terminal device 140) via wired or wireless means (e.g., Bluetooth, WiFi, etc.).

[0047] Wearable device 160 refers to clothing or equipment with wearable functions. In some embodiments, wearable device 160 may include but is not limited to a top device 160-1, a pants device 160-2, a belt device 160-3, and shoes 160-4. In some embodiments, one or more data acquisition devices 150 may be provided in the wearable device 160 to collect motion data from different parts of the body. When a user wears the wearable device 160 and exercises, the data acquisition device 150 may contact the user's body parts to collect motion data. For example, the data acquisition device 150 (e.g., electrodes) on the wearable device 160 may contact multiple parts of the human body, such as the calf, thigh, buttocks, waist, chest, shoulder, etc., to obtain electromyographic signals from the parts. For another example, the data acquisition device 150 (e.g., inertial sensor) on the wearable device 160 may contact multiple limbs (e.g., upper arms, lower arms, thighs, lower legs, etc.) or joints (e.g., knee joints, ankle joints, elbow joints, etc.) of the human body to obtain posture signals from the corresponding parts. In some embodiments, the upper garment device 160-1, the trousers device 160-2, the belt device 160-3 and the shoes 160-4 can serve as the wearable body of the wearable device 160, and the data acquisition device 150 is provided on the wearable body.

[0048] It should be noted that the wearable device 160 is not limited to the top device 160-1, pants device 160-2, belt device 160-3, and shoe device 160-4 shown in Figure 1, and may also include other devices, such as wristband devices, gloves, etc., which are not specifically limited here. It is understood that the user wearing the wearable device 160 for running can be a runner, a sports equipment tester, etc. The number of users can be one or more.

[0049] The description about motion data processing system 100 is intended to be illustrative, rather than limiting the scope of this specification. Many substitutions, modifications, and variations will be apparent to those of ordinary skill in the art. It will be appreciated that, after understanding the principles of the system, those skilled in the art may, without departing from this principle, combine the various modules in any manner, or form subsystems connected to other modules. The features, structures, methods, and other characteristics of the exemplary embodiments described in this specification can be combined in various ways to obtain additional and / or alternative exemplary embodiments. These changes are all within the scope of protection of this specification.

[0050] FIG2 is an exemplary module diagram of a motion data processing device according to some embodiments of this specification. In some embodiments, the motion data processing device 200 shown in FIG2 can be applied to the motion data processing system 100 shown in FIG1 in the form of software and / or hardware. For example, it can be configured in the form of software and / or hardware to the processing device 130 and / or the terminal device 140 to determine the user's recommended cadence based on the user's motion data. In some embodiments, the motion data processing device 200 may include an acquisition module 210, a processing module 220, a recommendation module 230, and a feedback module 240.

[0051] Acquisition module 210 can be used to acquire motion data of the user at different cadences. For example, acquisition module 210 can acquire motion data from any one or more of signal acquisition device 150, storage device 130, or terminal device 140. In some embodiments, motion data may include electromyographic signals, posture signals, electrocardiographic signals, respiratory signals, sweat signals, mechanical signals, etc.

[0052] The processing module 220 can be used to determine the user's physiological state based on the motion data. In some embodiments, the physiological state may include muscle efficiency, muscle tension, muscle fatigue, sports injury risk, etc. In some embodiments, the processing module 220 can determine at least one of the user's muscle efficiency, muscle tension, and muscle fatigue based on the electromyographic signal to determine the user's physiological state. In some embodiments, the processing module 220 can determine the user's injury risk due to exercise based on the electrocardiogram signal and / or posture signal to determine the user's physiological state.

[0053] Recommendation module 230 can be used to determine a user's recommended cadence based on their physiological state. In some embodiments, recommendation module 230 can determine a user's recommended cadence based on a correlation between the user's physiological state and cadence. For example, recommendation module 230 can determine a recommended cadence based on a correlation between muscle efficiency, muscle tension, and muscle fatigue and cadence. For another example, recommendation module 230 can determine a recommended cadence based on a correlation between sports injury risk and cadence.

[0054] The feedback module 240 can be used to provide the user with a recommended cadence. In some embodiments, the feedback module 240 can provide the user with a beat that is consistent with the recommended cadence based on a preset feedback method (e.g., voice feedback, vibration feedback, etc.). In some embodiments, the feedback module 240 can guide the user to adjust their cadence based on the user's real-time cadence until the user's cadence matches the recommended cadence. In some embodiments, the feedback module 240 can provide real-time feedback. The user can view or receive feedback results in real time via a terminal device (e.g., a mobile phone or watch). In some embodiments, the feedback module 240 can also provide non-real-time feedback. The user can view exercise statistics generated after the exercise via a terminal device (e.g., a mobile phone, watch, or computer). In some embodiments, the feedback module 240 provides the user with a recommended cadence. The feedback module 240 can also be omitted, with the feedback function performed by components of other modules (e.g., the acquisition module 210). For more details about the various modules of the motion data processing device 200, please refer to Figures 3-6 of this specification and the related descriptions.

[0055] It should be noted that the above description of the motion data processing device 200 and its modules is for convenience only and does not limit this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of the device and / or modules, may arbitrarily combine the modules or form submodules connected to other modules without departing from such principles. For example, the feedback module 240 may be omitted or replaced with other devices or modules. Such variations are all within the scope of protection of this specification.

[0056] FIG3 is an exemplary flow chart of a motion data processing method according to some embodiments of this specification. In some embodiments, process 300 can be performed by processing logic, which can include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. In some embodiments, one or more operations in process 300 of the motion data processing method shown in FIG3 can be implemented by the processing device 110 and / or the terminal device 140 shown in FIG1. ​​For example, process 300 can be stored in the storage device 130 in the form of instructions and called and / or executed by the processing device 110 and / or the terminal device 140. As shown in FIG3, process 300 may include:

[0057] Step 310: Acquire motion data of the user at different cadences, where the motion data at least includes electromyographic signals.

[0058] In some embodiments, step 310 can be performed by acquisition module 210. In some embodiments, motion data can be data generated by the user during running. Motion data can be used to characterize the state of the user during running (e.g., physiological state). In some embodiments, motion data can include electromyographic signals. Electromyographic signals can be bioelectric signals generated by muscles during exercise. Electromyographic signals can be used to determine the physiological state of the user during running (e.g., muscle efficiency, muscle tension). In some embodiments, when the user wears a wearable device, electromyographic signals can be collected by a data acquisition device (e.g., one or more electrodes) fitted with the user. For example, one or more electrodes can be fitted with muscles in different parts of the user (e.g., thigh, calf, waist, buttocks) to collect electromyographic signals of corresponding muscle parts.

[0059] In some embodiments, motion data may also include posture signals and electrocardiogram (ECG) signals. Posture signals may include information such as the angles, velocities, and accelerations of various joints, or the Euler angles, angular velocities, and angular accelerations of various body parts. In some embodiments, posture signals may be used to characterize the user's current physiological state (e.g., risk of sports injury) during exercise. In some embodiments, posture signals may be collected by a data acquisition device (e.g., a posture signal acquisition device). Exemplary posture signal acquisition devices may include velocity sensors, inertial sensors (e.g., acceleration sensors, angular velocity sensors (e.g., gyroscopes), etc.), optical sensors (e.g., optical distance sensors, video / image collectors), acoustic distance sensors, tension sensors, etc., or any combination thereof. ECG signals may refer to signals used to represent the user's heart activity. In some embodiments, ECG signals may be collected by an ECG signal acquisition device. For example, the ECG signal acquisition device may include multiple electrodes that may be adapted to fit different parts of the user, such as the abdomen, to collect ECG signals from the subject to be evaluated. For more information on posture signals and ECG signals, please refer to FIG. 5 of the embodiments of this specification and the related description thereof.

[0060] In other embodiments, the motion data may also include respiratory signals, mechanical signals, sweat signals, etc., or any combination thereof. A respiratory signal may refer to a signal indicating the user's breathing condition. In some embodiments, the respiratory signal may be collected by a respiratory signal acquisition device. For example, the respiratory signal acquisition device may include a respiratory rate sensor, a flow sensor, etc., respectively used to detect the respiratory rate, gas flow rate, and other data of the subject to be evaluated during exercise. A sweat signal may refer to a signal indicating the user's sweating condition. In some embodiments, the sweat signal may be collected by a sweat signal acquisition device. For example, the sweat signal acquisition device may include multiple electrodes in contact with the user's skin, used to detect the user's sweat flow rate or analyze the sweat composition. A mechanical signal may refer to the corresponding force applied to the user's joints, and the mechanical signal may be used to characterize the user's sports injury risk (e.g., pressure at the ankle, pressure at the knee, etc.). In some embodiments, the mechanical signal may be obtained by a mechanical sensor. For example, the mechanical sensor may include a pressure sensor, and pressure signals at different parts of the user may be obtained based on the pressure sensor as the user's mechanical signal. In some embodiments, the mechanical signal may be calculated based on the posture signal and the electromyographic signal. In some embodiments, electromyographic signals, electrocardiographic signals, posture signals, breathing signals, sweat signals, etc. or any combination thereof can be used to characterize the user's physiological state during running (e.g., muscle efficiency, muscle fatigue state, sports injury risk, etc.).

[0061] In some embodiments, the processing device 110 may directly obtain the motion data from a data acquisition device (e.g., data acquisition device 150). In some embodiments, the motion data may be stored in a storage device (e.g., storage device 130), and the processing device 110 may obtain the motion data from the storage device.

[0062] In some embodiments, multiple sets of motion data for a user at multiple preset cadences can be obtained. Each of the multiple preset cadences can be within a specific frequency range (also called a step count range), and each set of motion data corresponds to one of the multiple preset cadences. In some embodiments, the cadence can be the number of steps taken per unit time (e.g., per minute). By way of example only, the cadence of a professional runner can be approximately 180 steps per minute. In some embodiments, each preset cadence can be a pre-set cadence within a specific frequency range. The frequency ranges corresponding to the multiple preset cadences are different. In some embodiments, the frequency ranges of the preset cadences can be set by default. In some embodiments, the frequency ranges of the preset cadences can also be personalized based on the needs of different users. The set preset cadences and / or their corresponding frequency ranges can be stored in a storage device. In some embodiments, the multiple preset cadences can include a first preset cadence, a second preset cadence, ..., and an Nth preset cadence, where the first preset cadence, the second preset cadence, ..., and the Nth preset cadence each correspond to a different frequency range. As an example only, the first preset cadence may be located in or correspond to a frequency range of 100 to 110 steps per minute, the second preset cadence may be located in or correspond to a frequency range of 110 to 120 steps per minute, ..., and the Nth preset cadence may be located in or correspond to a frequency range of 180 to 190 steps per minute. In some embodiments, motion data at each of the multiple preset cadences may be acquired separately.

[0063] In some embodiments, multiple sets of motion data at multiple preset cadences can be obtained while the user is running. For example, when the user is running at a first preset cadence, the user's first motion data at the first preset cadence can be obtained; then, the user adjusts the cadence to a second preset cadence, and obtains the second motion data at the second preset cadence, ..., and similarly, the user's Nth motion data at the Nth preset cadence can be obtained. In some embodiments, multiple sets of motion data at different preset cadences can also be obtained based on the user's historical motion records (e.g., cadence records, motion data records corresponding to the cadence records). In some embodiments, each of the multiple sets of motion data can characterize the user's physiological state at the corresponding preset cadence.

[0064] In some embodiments, the preset cadence can be fed back to the user via feedback (e.g., voice, vibration, etc.) so that the user can follow the preset cadence to generate motion data at the preset cadence. For example, the preset cadence can be fed back to the user via voice, and a wearable device or terminal device (e.g., a mobile phone) can play a voice rhythm with a frequency that is the same as the preset cadence. The user runs following the voice rhythm, and during the running process, a data acquisition device is used to obtain the user's running motion data. This motion data is the motion data generated at the preset cadence.

[0065] In some embodiments, obtaining multiple sets of motion data for a user at multiple preset cadences may include obtaining physiological information of the user and determining the multiple sets of motion data for the user at the multiple preset cadences based on the physiological information. In some embodiments, the physiological information of the user may include body information and motion information. Body information refers to information related to the user's physical condition or characteristics. In some embodiments, the body information may include, but is not limited to, one or more of gender, age, height, weight, and body composition information. By way of example only, body composition information may include body fat percentage, muscle mass, skeletal muscle mass, bone mass, subcutaneous fat, and body water content. Motion information refers to information related to the user's exercise history. Motion information may include exercise habits and athletic performance. By way of example only, exercise habits may include, but is not limited to, exercise frequency, exercise type, and exercise duration. Physiological information corresponding to different users may be stored in a storage device. In some embodiments, physiological information of different users (e.g., test subjects for obtaining multiple sets of motion data) may be pre-collected and stored in a storage device. When obtaining multiple sets of motion data for a user at multiple preset cadences, the user's physiological information may be obtained from the storage device, and the multiple sets of motion data for the user at the multiple preset cadences may be determined based on the user's physiological information. For example, the testers may include user 1, user 2, ..., user m, and the physiological information of m users is stored in a storage device. When obtaining multiple sets of motion data of user 1 at multiple preset cadences, the physiological data of user 1 can be first obtained from the storage device, and based on the physiological data of user 1, multiple sets of motion data (for example, electromyographic signals, posture signals, electrocardiogram signals, etc.) of user 1 at different preset cadences can be determined. In a similar manner, multiple sets of motion data of different users (that is, users with different physiological information) at different preset cadences can be determined respectively. In some embodiments, the motion data of users with different physiological information at the same preset cadence may be different, and the physiological information of the user can affect the motion data generated by the user during exercise, thereby affecting the physiological state of the user. In some embodiments, the relationship between the cadence and physiological state of the user under different conditions (for example, different physiological information) can be determined based on the physiological information of the user. For details, please refer to the description elsewhere in this specification (for example, step 330).

[0066] In some embodiments, the user's motion data at different cadences may also be obtained based on other methods. For example, the user's real-time motion data may be obtained based on the user's real-time cadence. For details, see FIG4 and its related description.

[0067] In some embodiments, the user's movement speed is related to the cadence, and the cadence can be used as an indicator to evaluate the movement speed. Generally speaking, the movement speed can be approximately expressed as the product of the cadence and the stride. Therefore, when the user's movement speed remains stable (i.e., uniform motion or near uniform motion) or is within a certain range, the user's movement data is obtained within the current speed or speed range. The movement data obtained in this case can be used as a relatively stable measurement standard to determine the cadence. In some embodiments, obtaining the user's movement data at different cadences may include: determining a target speed interval; and obtaining movement data at different cadences within the target speed interval based on the target speed interval.

[0068] A speed interval can be a range of values ​​for a user's running speed during exercise. A target speed interval refers to a pre-set speed interval with a certain speed range. In some embodiments, the speed range defined by the target speed interval can be relatively small, meaning that within the target speed interval, running speed varies little. When the user's exercise speed remains within the target speed interval, the exercise data acquired based on the target speed interval can serve as a relatively stable metric for determining cadence. For example, if a user exercises at a first preset cadence within the target speed interval, the exercise data acquired is first exercise data; if a user exercises at a second preset cadence within the target speed interval, the exercise data acquired is second exercise data; and so on; if a user exercises at an Nth preset cadence within the target speed interval, the exercise data acquired is Nth exercise data. The differences between the first, second, ..., and Nth exercise data are primarily influenced by cadence, while exercise speed can be ignored. In some embodiments, when the user's exercise speed is within the target speed interval, the user exercises at the preset cadence, and the exercise data acquired by the data acquisition device is valid. This exercise data corresponds to the preset cadence and can be used for subsequent analysis and processing (e.g., to determine the user's physiological state). If the user's speed exceeds the target speed range, the data collection device will not collect any motion data, or the collected motion data will be invalid, regardless of whether the user is exercising at the preset cadence. Invalid motion data will not be used for subsequent analysis and processing. In some embodiments, an appropriate target speed range can be set based on actual conditions (e.g., different users, road conditions) and application scenarios. This specification does not specifically limit the range of the target speed range.

[0069] By reasonably setting the range of the target speed range, the impact of different movement speeds at different synchronous frequencies on the acquired motion data can be reduced, thereby ensuring the accuracy of the motion data.

[0070] In some embodiments, to reduce the discrepancy in motion data caused by different speeds at different cadences, motion data of the user at different cadences but the same speed can be obtained. In some embodiments, motion data of the user at different cadences and different speeds can be obtained. In this case, the user's speed can be monitored and the effect of different speeds can be compensated in the resulting motion data.

[0071] Step 320: Determine the user's physiological state based on the motion data.

[0072] In some embodiments, step 320 can be performed by the processing module 220. In some embodiments, the physiological state can be used to characterize the physiological condition and its changing state during the user's running process. In some embodiments, the physiological state may include muscle efficiency, muscle fatigue (also called muscle fatigue state) and muscle tension. In some embodiments, when a user runs at different cadences, the muscle efficiency, muscle fatigue state and muscle tension will be different, thereby making the user's exercise efficiency (for example, running performance, fitness effect, etc.) different. In some embodiments, the user's physiological state can be determined based on the electromyographic signal. For example, the user's physiological state can be determined based on the characteristic value of the electromyographic signal. The characteristic value of the electromyographic signal can be a characteristic parameter in the electromyographic signal that reflects the user's physiological state. Exemplary characteristic parameters may include the amplitude of the electromyographic signal, the absolute value of the amplitude, the average value of the amplitude (or absolute value), the maximum value, the minimum value, the median frequency, the peak frequency, the total force, the electromyographic power, the foot contact time, the average frequency, etc.

[0073] In some embodiments, it is possible to determine whether the user's muscles are in a fatigue state based on the electromyographic signals in the motion data. For example, the total force value of the leg muscles in the fatigue state is greater than the total force value of the leg muscles in the non-fatigue state. The total force value can be characterized by the sum of the amplitudes of the electromyographic signals (for example, the sum of the absolute values ​​of the amplitudes, the sum of the multiple powers of the amplitudes, the sum of the multiple amplitudes, etc.) within a preset time period (for example, a running cycle, the time period when the foot touches the ground, the time period when the foot is in the air, etc.). At this time, the sum of the amplitudes of the electromyographic signals can be used as a characteristic parameter to determine whether the user's muscles are in a fatigue state. For another example, the electromyographic power of the leg muscles in the fatigue state is greater than the electromyographic power of the leg muscles in the non-fatigue state. Electromyographic power can refer to the sum of the amplitudes of the electromyographic signals per unit time. At this time, the electromyographic power of the electromyographic signals can be used as a characteristic parameter to determine whether the user's muscles are in a fatigue state. For another example, in a non-fatigued state, some leg muscles relax when the leg is airborne, and the corresponding EMG signal amplitude can be the minimum value within a cycle. However, when entering a fatigued state, these leg muscles tighten when the leg is airborne, which can be manifested, for example, by an increase in the EMG signal amplitude when the leg is airborne. In this case, the EMG signal amplitude of the leg muscles when the leg is airborne can be used as a characteristic parameter to determine whether the user's muscles are in a fatigued state. For another example, in a non-fatigued state, some leg muscles tighten when the foot strikes the ground, and the corresponding EMG signal amplitude can be the maximum value within a cycle. However, in a fatigued state, the muscle strength of these leg muscles increases abnormally when the foot strikes the ground, which can be manifested, for example, by the EMG signal amplitude increasing at a rate greater than a speed threshold. In this case, the EMG signal amplitude increasing rate when the foot strikes the ground can be used as a characteristic parameter to determine whether the user's muscles are in a fatigued state. For another example, when a leg muscle transitions from a non-fatigued state to a fatigued state, the average frequency, median frequency, and other parameters of the leg muscle decrease. The average frequency can be the ratio of the amplitude of the electromyographic signal at each frequency to the sum of the weighted values ​​(for example, the amplitude at each frequency multiplied by the corresponding frequency, the multiple powers of the amplitude at each frequency, the multiple values ​​of the amplitude at each frequency, etc.) and the sum of the values ​​to the sum of the amplitudes at each frequency. At this time, the average frequency of the electromyographic signal can be used as a characteristic parameter to determine whether the user's muscles are in a fatigue state. For another example, when performing the same running action, the muscle strength of the leg muscles in a fatigued state is greater than the muscle strength in a non-fatigued state. And as the degree of fatigue increases, the muscle strength increases further. When the muscle strength is too great, the leg muscles may be damaged due to excessive fatigue. Thus, the fatigue state of the muscle can be judged based on the intensity change of the electromyographic signal. Specifically, the electromyographic signal can be segmented according to multiple time windows divided by time periods, and the average value of the amplitude (for example, the absolute value of the amplitude) of each segment of the electromyographic signal is used as the characteristic value of each segment of the electromyographic signal, thereby determining multiple characteristic values ​​within multiple time periods.The amplitude average value can generally reflect the average strength of each electromyographic signal in each time period, so that the change in the average strength of the electromyographic signal during running can be judged based on multiple characteristic values, and the fatigue state of the muscle can be characterized thereby.

[0074] As the running time increases, the leg muscles gradually enter a fatigue state, and muscle efficiency gradually decreases. Muscle efficiency can refer to the muscle output required for maintaining the same athletic ability. In certain embodiments, the higher the muscle output required for maintaining the same athletic ability, the lower the muscle efficiency. In certain embodiments, muscle efficiency can be determined according to the integral value of the electromyographic signal in a certain interval (for example, in a specific time period). In certain embodiments, muscle efficiency can also be determined according to the mean value of the electromyographic signal amplitude in a certain interval.

[0075] In some embodiments, the tension of the user's muscles can be determined based on the electromyographic signal. The tension of the muscle can refer to the degree of muscle relaxation during exercise. During the user's running process, the leg muscles can be in periodic motion, wherein when the leg is in the air, part of the leg muscles (e.g., calf muscles) relax, and the amplitude of the corresponding electromyographic signal can be the minimum value within a cycle; and when the leg (i.e., foot) touches the ground, part of the leg muscles tighten, and the amplitude of the corresponding electromyographic signal can be the maximum value within a cycle. Thus, the force of the leg muscles can be judged based on the amplitude (e.g., minimum value, maximum value) of the electromyographic signal as a characteristic parameter, thereby determining the tension state of the leg muscles. For example, the electromyographic signal can be segmented according to multiple time windows divided by time periods, and the minimum value of the amplitude (e.g., absolute value of amplitude) of each segment of the electromyographic signal is used as the characteristic value of each segment of the electromyographic signal, thereby determining multiple characteristic values ​​within multiple time periods. The minimum amplitude value can reflect the minimum intensity of each electromyographic signal in each time period, so that the change in the minimum intensity of the electromyographic signal during running can be determined based on multiple characteristic values, and the degree of muscle tension can be characterized thereby.

[0076] Step 330: Determine a recommended cadence for the user based on the physiological state.

[0077] In some embodiments, step 330 may be performed by the recommendation module 230. In some embodiments, when a user runs at different cadences, the user's muscle fatigue state, muscle efficiency, and muscle tension are different, which may cause the user's exercise efficiency to be different. The correspondence between the user's physiological state and cadence can be determined based on at least one of muscle efficiency, muscle tension, and fatigue state, and the user's recommended cadence can be determined based on the correspondence. When running at the recommended cadence, the user's exercise efficiency can be improved (for example, muscle efficiency is in the optimal state, and muscle fatigue state and tension are within the target range), while also preventing the user from sports injuries and ensuring the scientific nature and safety of the exercise.

[0078] In some embodiments, the correspondence between a user's physiological state and cadence, determined based on muscle fatigue, muscle efficiency, and / or muscle tension, can also be referred to as a first correspondence. In some embodiments, the first correspondence can include a correspondence between muscle efficiency and cadence. In some embodiments, a correspondence between muscle efficiency and multiple preset cadences can be determined. In some embodiments, the myoelectric signal at the first preset cadence can be analyzed and processed (e.g., by calculating an eigenvalue) to obtain the muscle efficiency at the first preset cadence (denoted as the first muscle efficiency), thereby determining the correspondence between the first preset cadence and the first muscle efficiency. That is, the first muscle efficiency corresponds to the first preset cadence. Similarly, the correspondence between the second preset cadence and the second muscle efficiency, the correspondence between the third preset cadence and the third muscle efficiency, and so on, and the correspondence between the Nth preset cadence and the Nth muscle efficiency can be determined. In some embodiments, a correspondence between muscle efficiency and real-time cadence can also be determined. In some embodiments, the user's real-time cadence and the real-time myoelectric signal corresponding to the real-time cadence can be obtained, and the real-time myoelectric signal can be analyzed and processed to obtain the real-time muscle efficiency at the real-time cadence.

[0079] In some embodiments, a user's recommended cadence may be determined based on the correspondence between muscle efficiency and cadence. In some embodiments, one or more cadences may be determined from different cadences as the recommended cadence based on the correspondence between muscle efficiency and cadence. For example, one or more cadences from different cadences (e.g., multiple preset cadences) corresponding to higher muscle efficiency (or lower muscle fatigue levels) may be determined as the recommended cadence.

[0080] In some embodiments, the first correspondence may include a correspondence between muscle tension and cadence. In some embodiments, a correspondence between muscle tension and multiple preset cadences can be determined. For illustrative purposes only, the electromyographic signal at a first preset cadence can be analyzed and processed to obtain the muscle tension at the first preset cadence (referred to as the first muscle tension), thereby determining a correspondence between the first preset cadence and the first muscle tension. That is, the first muscle tension corresponds to the first preset cadence. Similarly, a correspondence can be determined between the second preset cadence and the second muscle tension, the third preset cadence and the third muscle tension, and so on, and the Nth preset cadence and the Nth muscle tension. In some embodiments, a correspondence between muscle tension and real-time cadence can also be determined. In some embodiments, a real-time cadence of a user and a real-time electromyographic signal corresponding to the real-time cadence can be obtained, and the real-time electromyographic signal can be analyzed and processed to obtain the real-time muscle tension at the real-time cadence.

[0081] In some embodiments, a user's recommended cadence can be determined based on the correspondence between muscle tension and cadence. In some embodiments, one or more cadences can be determined from different cadences as the recommended cadence based on the correspondence between muscle tension and cadence. For example, one or more cadences from different cadences (e.g., multiple preset cadences) that are within the human tolerance range and correspond to high muscle tension can be determined as the recommended cadence.

[0082] In some embodiments, the first correspondence may include a correspondence between muscle fatigue and step frequency. In some embodiments, the correspondence between muscle fatigue and multiple preset step frequencies can be determined. As an exemplary illustration only, the myoelectric signal under the first preset step frequency can be analyzed and processed to obtain muscle fatigue under the first preset step frequency (recorded as the first muscle fatigue degree) to determine the correspondence between the first preset step frequency and the first muscle fatigue. That is, the first muscle fatigue degree corresponds to the first preset step frequency. Similarly, the correspondence between the second preset step frequency and the second muscle fatigue can be determined, ..., the correspondence between the Nth preset step frequency and the Nth muscle fatigue. In some embodiments, the correspondence between muscle fatigue and real-time step frequency can also be determined. In some embodiments, the user's real-time step frequency and the real-time electromyographic signal corresponding to the real-time step frequency can be obtained, and the real-time muscle fatigue under the real-time step frequency can be obtained by analyzing and processing the real-time electromyographic signal.

[0083] In some embodiments, a user's recommended cadence can be determined based on the correspondence between muscle fatigue and cadence. In some embodiments, one or more cadences can be determined from different cadences as the recommended cadence based on the correspondence between muscle fatigue and cadence. In some embodiments, the user's recommended cadence can also be determined by comprehensively considering the correspondence between muscle efficiency and cadence, the correspondence between muscle tension and cadence, and the correspondence between muscle fatigue and cadence.

[0084] In some embodiments, the correspondence between the user's cadence and physiological state can be determined based on the relationship determination model. In some embodiments, the input of the relationship determination model may include but is not limited to cadence. The output of the relationship model may be the correspondence between cadence and physiological state (for example, muscle fatigue, muscle tension, muscle efficiency, sports injury risk, etc.). In some embodiments, the relationship determination model may be a trained machine learning model. In some embodiments, the relationship determination model may be pre-trained by a processing device and stored in a storage device, and the processing device may access the storage device to obtain the relationship determination model. In some embodiments, the machine learning model may include one or more of a linear classification model (LR), a support vector machine model (SVM), a naive Bayes model (NB), a K-nearest neighbor model (KNN), a decision tree model (DT), an integrated model (RF / GDBT, etc.), etc.

[0085] In some embodiments, the relationship determination model can be trained based on sample information. The sample information may include different preset cadences for different users. In some embodiments, when cadence is used as sample information, the user's physiological state can be monitored while obtaining the cadence. In some embodiments, when training a machine learning model, the sample information can be labeled. For example, when a user exercises at a first preset cadence, their body exhibits a first physiological state, and the first preset cadence can be labeled as "first preset cadence corresponds to first physiological state"; when a user exercises at a second preset cadence, their body exhibits a second physiological state, and the second preset cadence can be labeled as "second preset cadence corresponds to second physiological state." Different preset cadences correspond to different physiological states. Using the labeled sample information (i.e., each labeled preset cadence) as input to train the machine learning model, a relationship determination model can be obtained. When the corresponding cadence (which may include the real-time cadence) is input into the relationship determination model, the relationship between the corresponding cadence and the physiological state can be output.

[0086] In some embodiments, to improve the accuracy of the correspondence between the preset cadence and physiological state, the user's physiological information can also be used as input to train the machine learning model. This allows for the correspondence between cadence and physiological state under different conditions (e.g., different physiological information). Furthermore, a recommended cadence for the user can be determined based on this correspondence, thereby making the recommended cadence more suitable for the user. In some embodiments, the recommended cadence (also called the initial recommended cadence) for the target training task can be estimated based on the information actually input by the user. The user first exercises at the initial recommended cadence, and during this exercise, the user's real-time motion data can be collected. The motion control system can iteratively update the relationship determination model based on this real-time motion data, so that the relationship determination model outputs a correspondence that is more in line with actual needs. Based on this correspondence, a recommended cadence that is more accurately aligned with the user's real-time motion state (i.e., the iteratively updated recommended cadence) can be re-determined. In some embodiments, the motion data processing system can collect more motion data accumulated by the user during long-term exercise, thereby making the recommended cadence recommended by the motion data processing system more reasonable and more in line with the user's own exercise habits.

[0087] In some embodiments, the user's physiological state and recommended cadence may also be determined by other means or methods. For example, the user's physiological state and recommended cadence may be determined based on electrocardiogram signals and / or posture signals. For details, please refer to FIG5 and its related description. In some embodiments, the user's recommended cadence may be determined based on one or any combination of the correspondence between muscle efficiency and cadence, the correspondence between muscle tension and cadence, and other physiological states (e.g., sports injury risk) and cadence. At this point, the recommended cadence may be determined by comprehensively considering the correspondence between the physiological state and cadence determined based on various motion data such as electromyographic signals, posture signals, electrocardiogram signals, and respiratory signals.

[0088] In some embodiments, a recommended cadence can be determined based on a user's historical exercise history. In some embodiments, a recommended cadence can be determined based on historical electromyographic signals. For example, a recommended cadence for the current user can be determined based on a historical cadence corresponding to muscle fatigue determined based on historical electromyographic signals. As just one example, the recommended cadence can be less than or slightly less than the historical cadence. For another example, the historical recommended cadence can be combined with the current electromyographic signal to determine the recommended cadence for the current user.

[0089] In some embodiments, the recommended cadence can be determined in combination with the road environment (e.g., uphill, downhill) and the user's personal physical condition (e.g., historical exercise records). For example, when a user is running on a sloped road section or a treadmill, in order to prevent knee injuries, the recommended cadence for the sloped road section can be appropriately smaller than the recommended cadence for a flat road section with the same physiological state. In some embodiments, when the system does not record the user's real-time physical condition or situation (e.g., road environment), an initial cadence can be recommended to the user (determined based on the user's historical exercise records or based on the running data of other people). After the user exercises at the initial cadence for a period of time, the user's physiological state is determined based on the exercise data within this time interval, and the recommended cadence is then determined based on the physiological state.

[0090] Step 340: Feedback the recommended cadence to the user.

[0091] In some embodiments, step 340 may be performed by feedback module 240. In some embodiments, after determining the user's recommended cadence based on steps 310, 320, and 330, the recommended cadence may be fed back to the user, and the user may adjust their cadence according to the recommended cadence until it matches the recommended cadence, thereby improving exercise efficiency.

[0092] In some embodiments, a tempo equal to the recommended cadence can be fed back to the user based on a preset feedback method. The user adjusts their cadence based on the recommended cadence to match the recommended cadence, and the user can then run at the recommended cadence. In some embodiments, the preset feedback method can include at least one of voice feedback, vibration feedback, light feedback, and display feedback. In some embodiments, a tempo equal to the recommended cadence can be fed back to the user based on voice feedback. For example, a device with a sound output function in the motion data processing system (e.g., a mobile phone, watch, or headphones) can play a voice message that matches the recommended cadence, and the user can adjust their cadence based on the voice message to match or substantially match the recommended cadence. In some embodiments, a tempo equal to the recommended cadence can be fed back to the user based on vibration feedback. For example, a device capable of generating vibrations in the motion data processing system (e.g., a mobile phone, watch) can vibrate at the recommended cadence. In some embodiments, a tempo equal to the recommended cadence can be fed back to the user based on light feedback. For example, an electronic device in the motion data processing system (e.g., a mobile phone, watch) can flash lights at a frequency equal to the recommended cadence. In some embodiments, the same tempo as the recommended cadence may be fed back to the user based on a display feedback method. For example, an electronic device (eg, a mobile phone) in a motion data processing system may display the recommended cadence.

[0093] In some embodiments, a recommended cadence can be fed back to the user through a virtual reality device and / or an augmented reality device (e.g., AR glasses). For example, a user can wear AR glasses while running, and the recommended cadence can be displayed in the AR glasses in the form of data, and the user can adjust their cadence based on the recommended cadence data. Feedback of the recommended cadence to the user through AR glasses can make the user more clearly aware of the recommended cadence, while also reducing the user's dependence on mobile phones, watches, and bracelets, and alleviating the physical burden on the user when running.

[0094] In some embodiments, a recommended cadence may also be fed back to the user based on a biofeedback method (e.g., electrical stimulation). In some embodiments, stimulation may be performed on a part of the user's body based on biofeedback to feed back a recommended cadence to the user, wherein the stimulation performed on the part of the user's body may have a stimulation frequency, and the stimulation frequency is the same as the recommended cadence. In some embodiments, electrodes may be provided on a wearable body of the wearable device (e.g., a top, pants, belt, etc.), and the electrodes are in contact with human skin when the user wears the wearable device. The processing device may control the electrodes to stimulate the part of the user's body based on the stimulation frequency (i.e., the recommended cadence).

[0095] Users can adjust their real-time cadence according to the recommended cadence so that the real-time cadence is consistent with the recommended cadence and run at the recommended cadence. This allows them to exercise in a more scientific and efficient manner, thereby improving exercise efficiency.

[0096] It should be noted that the above description of process 300 is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 300 under the guidance of this specification. For example, the user's motion data is not limited to the above-mentioned electromyographic signals, posture signals, and electrocardiographic signals, but can also be other physiological parameter signals, such as temperature signals, humidity signals, blood oxygen concentration, respiratory rate, etc. The physiological parameter signals involved in human body movement can be regarded as motion data in the embodiments of this specification. For another example, step 340 can be omitted. However, these modifications and changes are still within the scope of this specification.

[0097] FIG4 is an exemplary flow chart of a method for acquiring motion data according to some embodiments of this specification. In some embodiments, process 400 may be performed by acquisition module 210. Process 400 may include:

[0098] Step 410: Acquire the user's real-time cadence and real-time motion data.

[0099] In some embodiments, when a user wears a wearable device and runs, the acquisition module 210 can obtain the user's real-time cadence and real-time motion data. In some embodiments, the user's real-time cadence may change in different time periods during the running process. For example, the real-time cadence may have a trend of gradually increasing or decreasing. Just as an exemplary description, when the user first starts running, the real-time cadence may be high; after running for a period of time, the real-time cadence will gradually decrease. For another example, the user's real-time cadence may also be sometimes high and sometimes low. As a result, the motion data generated by the user running at different real-time cadences, that is, the real-time motion data corresponding to the real-time cadence, is also different. At this time, the user's real-time cadence and real-time motion data can be obtained.

[0100] In some embodiments, the real-time cadence can be obtained by a data acquisition device. In some embodiments, the real-time cadence can be obtained by an inertial sensor. As an exemplary illustration only, running is a periodic motion, and the running motion is composed of a plurality of running movements repeated periodically. The signal collected by the inertial sensor (for example, a posture sensor) also corresponds to a periodic signal (or a similar periodic signal). The user completing a running movement can be regarded as completing a periodic movement, thereby obtaining the real-time cadence based on the inertial sensor. In some embodiments, the real-time cadence can also be extracted from the electromyographic signal. For example, when the user is running, the periodic running movement will generate a periodically changing electromyographic signal. By extracting the period of the electromyographic signal, the real-time cadence can be obtained.

[0101] Step 420: Divide the real-time cadence into a plurality of cadence intervals.

[0102] In some embodiments, the real-time cadence can be divided into multiple cadence intervals according to multiple cadence ranges, with each cadence interval corresponding to a cadence range. For example, multiple cadence ranges can include 150 steps / minute-160 steps / minute, 160 steps / minute-170 steps / minute, 170 steps / minute-180 steps / minute, 180 steps / minute-190 steps / minute, and 200 steps / minute-210 steps / minute. According to these cadence ranges, the real-time cadence is divided into five corresponding cadence intervals. Each cadence interval has a corresponding cadence. That is, the cadence of the first cadence interval is between 140 steps / minute and 150 steps / minute, ..., and the cadence of the fifth cadence interval is between 200 steps / minute and 210 steps / minute. In some embodiments, in order to ensure that the difference in real-time motion data within a single cadence interval is small, to ensure the accuracy of motion data under different cadences, the cadence range can be set to be smaller. In some embodiments, the difference between the upper and lower limits of the cadence range can be less than 10 (in steps / minute). In some embodiments, the difference between the upper limit and the lower limit of the preset range may be less than 8. In some embodiments, the difference between the upper limit and the lower limit of the preset range may be less than 6. In some embodiments, the difference between the upper limit and the lower limit of the preset range may be less than 5. In some embodiments, the difference between the upper limit and the lower limit of the preset range may be less than 3. In some embodiments, the preset range may be set according to actual needs and is not specifically limited in the embodiments of this specification.

[0103] Step 430: Based on the cadence interval, determine the motion data in different cadence intervals from the real-time motion data.

[0104] In some embodiments, the real-time motion data for different real-time cadences differ. For example, when the real-time cadence changes significantly, the real-time motion data also changes significantly; when the real-time cadence changes slightly, the real-time motion data also changes slightly. In some embodiments, motion data corresponding to different cadence intervals can be determined. For example, at least a portion of the real-time motion data corresponding to the same cadence interval can be pooled together as the motion data corresponding to that cadence interval. By way of example only, the motion speed corresponding to the real-time cadence may or may not be within the target speed interval (i.e., when the data acquisition device acquires the real-time cadence, the user's motion speed may or may not be within the target speed interval). If the motion speeds corresponding to the real-time cadences are all within the target speed interval, the real-time motion data corresponding to the same cadence interval can be pooled together as the motion data corresponding to that cadence interval. If the motion speeds corresponding to the real-time cadences are partially within the target speed interval, the real-time cadences corresponding to the motion speeds within the target speed interval can be first filtered out, and then the real-time motion data corresponding to the filtered real-time cadences that are within the same cadence interval can be pooled together as the motion data corresponding to that cadence interval.

[0105] In some embodiments, acquiring motion data based on real-time cadence can offer greater flexibility than acquiring motion data based on a preset cadence. This is because acquiring motion data based on a preset cadence requires the user to maintain their cadence within a preset cadence range. If the user's cadence exceeds this range, the accuracy of the motion data will be affected. However, acquiring motion data based on real-time cadence eliminates the need to control the user's cadence, allowing the user to freely exercise at varying cadences.

[0106] It should be noted that the above description of process 400 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and variations to process 400 under the guidance of this specification. However, such modifications and variations are still within the scope of this specification.

[0107] FIG5 is an exemplary flow chart of determining a recommended cadence according to some embodiments of this specification. In some embodiments, process 500 may be performed by determination module 220. Process 500 may include:

[0108] Step 510: Determine the correspondence between the user's physiological state and cadence based on the electrocardiogram signal and / or the posture signal.

[0109] In some embodiments, the user's physiological state may also include the risk of sports injuries. When a user is running, incorrect movements, such as incorrect running posture, inappropriate running cadence, or an excessively fast heart rate, not only fail to achieve the desired exercise efficiency, but may also cause damage to the body. In some embodiments, the risk of sports injuries may include posture injury risk. Posture injury risk may include injury errors, compensation errors, efficiency errors, symmetry errors, etc., or any combination thereof. An injury error may refer to a movement error that may cause damage to the body. A compensation error may refer to an error in using non-target parts (e.g., knees or ankles) to assist in exerting force. An efficiency error may refer to an arm swing or stride range that is too large or too small during running, causing the target part to be at a suboptimal level of activation. A symmetry error may refer to a situation where two symmetrical parts of the body (e.g., bilaterally symmetrical, front-to-back symmetrical) exert unbalanced force. In some embodiments, the risk of sports injuries may include an injury level. By way of example only, the injury risk level may include severe, moderate, mild, etc.

[0110] In some embodiments, the user's sports injury risk can be determined based on the posture signal. The posture signal may include information such as the Euler angle, velocity, acceleration of each joint, or the angular velocity, angular acceleration of each body part. In some embodiments, the posture signal can be used to characterize the technical accuracy of the user's current motion (e.g., joint angle, force sequence, etc.), thereby determining the sports injury risk (e.g., knee injury, ankle injury). In some embodiments, the posture signal can be collected by a posture signal acquisition device. An exemplary posture signal acquisition device may include a velocity sensor, an inertial sensor (e.g., an acceleration sensor, an angular velocity sensor (e.g., a gyroscope), etc.), an optical sensor (e.g., an optical distance sensor, a video / image collector), an acoustic distance sensor, a tension sensor, etc., or any combination thereof.

[0111] In some embodiments, the risk of sports injury may include the risk of heart injury. As the cadence increases, the user's heart rate will gradually increase. When the heart rate is higher than the threshold heart rate, the risk of heart injury may occur. In some embodiments, the user's heart injury risk can be determined based on the electrocardiogram signal. The electrocardiogram signal may refer to a signal that represents the user's heart activity. In some embodiments, the electrocardiogram signal can be collected by an electrocardiogram signal acquisition device. For example, the electrocardiogram signal acquisition device may include a plurality of electrodes, and the plurality of electrodes can be used to fit with different parts of the object to be evaluated (for example, through) to collect the electrocardiogram signal of the object to be evaluated.

[0112] In some embodiments, the correspondence between the physiological state determined based on the electrocardiogram signal and / or the posture signal and the cadence may also be referred to as a second correspondence. The second correspondence may include a correspondence between the posture injury risk and the cadence. In some embodiments, the correspondence between the posture injury risk and a plurality of preset cadences may be determined. In some embodiments, the posture signal under the first preset cadence may be processed (e.g., eigenvalue processing) to obtain the posture injury risk under the first preset cadence (referred to as the first posture injury risk), thereby determining the correspondence between the first preset cadence and the first posture injury risk. That is, the first posture injury risk corresponds to the first preset cadence. Similarly, the correspondence between the second preset cadence and the second posture injury risk, the correspondence between the third preset cadence and the third posture injury risk, ..., the correspondence between the Nth preset cadence and the Nth posture injury risk may be determined. In some embodiments, the correspondence between the posture injury risk and the real-time cadence may also be determined.

[0113] In some embodiments, the second correspondence may include a correspondence between the risk of heart damage and the cadence. In some embodiments, the correspondence between the user's risk of heart damage and the cadence can be determined based on the electrocardiogram signal. In some embodiments, the correspondence between the risk of heart damage and multiple preset cadences can be determined. In some embodiments, the electrocardiogram signal at the first preset cadence can be processed to obtain the risk of heart damage at the first preset cadence (recorded as the first heart damage risk), that is, the first heart rate damage risk corresponds to the first preset cadence. Similarly, the correspondence between the second preset cadence and the second heart damage risk, the correspondence between the third preset cadence and the third heart damage risk, ..., the correspondence between the Nth preset cadence and the Nth heart damage risk can be determined. In some embodiments, the correspondence between the heart damage risk and the real-time cadence can also be determined.

[0114] Step 520: Determine the recommended cadence of the user based on the corresponding relationship.

[0115] In some embodiments, a user's recommended cadence can be determined based on the relationship between sports injury risk and cadence. For example, the cadence with the lowest sports injury risk can be determined as the recommended cadence. In some embodiments, a cadence can be determined as the recommended cadence from among different cadences based on the relationship between postural sports injury risk and cadence. In some embodiments, a cadence can be determined as the recommended cadence from among different cadences based on the relationship between cardiac injury risk and cadence.

[0116] It should be noted that, in other embodiments, the user's recommended cadence may be determined by combining other motion data, such as the correspondence between the physiological state determined by breathing signals, pressure signals, sweat signals, etc. and the cadence.

[0117] In some embodiments, the user will have a certain breathing rhythm when running, for example, a breath every few steps. The user's breathing rhythm can also affect the user's physiological state. In some embodiments, a strain sensor (or contact impedance) can be set on the waistband of the wearable device. When the user breathes, the dimensions of the waist and abdomen will change, and the signal value collected by the strain sensor will also change accordingly. In some embodiments, the strain sensor can be used to detect the user's breathing rate when running, and the breathing rhythm can be adjusted according to the breathing rate to help the user exercise more scientifically.

[0118] It should be noted that the above description of process 500 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and variations to process 500 under the guidance of this specification. However, such modifications and variations are still within the scope of this specification.

[0119] FIG6 is an exemplary flow chart of a method for providing feedback and recommending cadence according to some embodiments of this specification. In some embodiments, process 600 may be performed by recommendation module 240. Process 600 may include:

[0120] Step 610: Obtain the real-time cadence of the user.

[0121] In some embodiments, real-time cadence may refer to the user's cadence at the current moment while running. In some embodiments, real-time cadence may be obtained via a data acquisition device. In some embodiments, real-time cadence may be obtained via an inertial sensor. For more information on how to obtain cadence using a data acquisition device, please refer to other parts of this specification, such as Figure 4 and its related description, and will not be repeated here.

[0122] Step 620: Determine a cadence adjustment trend of the user based on the frequency difference between the real-time cadence and the recommended cadence.

[0123] In some embodiments, there is usually a frequency difference between the user's real-time cadence and the recommended cadence. For example, if the user's real-time cadence is large and the real-time cadence is greater than the recommended cadence, the frequency difference between the real-time cadence and the recommended cadence is positive. For another example, if the user's real-time cadence is small and the real-time cadence is less than the recommended cadence, the frequency difference between the real-time cadence and the recommended cadence is negative. In some embodiments, the cadence adjustment trend may include a frequency decreasing trend and a frequency increasing trend. When the frequency difference between the real-time cadence and the recommended cadence is positive, the user's cadence adjustment trend may be determined to be a frequency decreasing trend; when the frequency difference between the real-time cadence and the recommended cadence is negative, the user's cadence adjustment trend may be determined to be a frequency increasing trend. In some embodiments, the cadence adjustment trend may also include a frequency unchanged trend, in which case the real-time cadence is the same as the recommended cadence.

[0124] Step 630: Guide the user based on the cadence adjustment trend until the user's cadence is the same as the recommended cadence.

[0125] In some embodiments, the user can adjust their real-time cadence based on the recommended cadence to bring it into line with the recommended cadence. In some embodiments, when the cadence adjustment trend indicates a decreasing frequency, the user can be guided to gradually decrease their real-time cadence until it matches the recommended cadence. When the cadence adjustment trend indicates an increasing frequency, the user can be guided to gradually increase their real-time cadence until it matches the recommended cadence. In some embodiments, when the cadence adjustment trend indicates a constant frequency, the real-time cadence is the same as the recommended cadence, and the user does not need to adjust their cadence.

[0126] In some embodiments, when guiding the user to adjust the cadence based on the cadence adjustment trend, a dynamic beat with a gradually changing frequency can be fed back to the user based on a feedback method (e.g., voice feedback, vibration feedback, light feedback, display feedback, biofeedback, etc.). The frequency change trend of the dynamic beat is the same as the cadence adjustment trend. For example, when the cadence adjustment trend is a frequency reduction trend, the starting frequency of the dynamic beat is the user's real-time cadence, the target frequency of the dynamic beat is the recommended cadence, and the frequency of the dynamic beat gradually decreases from the starting frequency to the target frequency. For another example, when the cadence adjustment trend is a frequency increase trend, the starting frequency of the dynamic beat is the user's real-time cadence, the target frequency of the dynamic beat is the recommended cadence, and the frequency of the dynamic beat gradually increases from the starting frequency to the target frequency. In some embodiments, the frequency adjustment process of the dynamic beat can be a uniform change. In some embodiments, the frequency adjustment process of the dynamic beat can also be a non-uniform change.

[0127] Guiding users to adjust their cadence based on cadence adjustment trends can make the process of users adjusting their cadence more scientific and healthy.

[0128] It should be noted that the above description of process 600 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and variations to process 600 under the guidance of this specification. However, such modifications and variations are still within the scope of this specification.

[0129] In some embodiments, the recommended cadence described in the embodiments of this specification can be adjusted in real time based on the user's running state. In some embodiments, after the user adjusts their cadence based on the recommended cadence until it matches the recommended cadence, the user can continue running at the recommended cadence. While running at the recommended cadence, the data acquisition device can continue to collect the user's motion data. After the user has run at the recommended cadence for a certain period of time, if the motion data collected by the data acquisition device indicates that the user's physiological state can continue to improve, the recommended cadence can be adjusted or re-determined based on the user's current physiological state. For example, if the user's current physiological state is good and there is room for improvement, such as if their muscles are not fatigued and their muscle efficiency can be improved, this indicates that the user's cadence can be increased based on the recommended cadence. In this case, the recommended cadence can be re-determined or updated based on the user's current physiological state. For another example, if the user's current physiological state is poor, such as if their heart rate is too high or their muscles are fatigued for a long time, this indicates that the user's cadence can be reduced based on the recommended cadence. In this case, the recommended cadence can be re-determined or updated based on the user's current physiological state. By adjusting the recommended cadence in real time, the user's running efficiency can be further improved.

[0130] As described above, the motion data processing method described in Figures 3-6 first determines the user's physiological state based on the user's motion data at different cadences, and then determines the user's recommended cadence based on the physiological state. In some embodiments, to reduce motion data processing and calculation time and more quickly determine the user's recommended cadence, the physiological state determination step can be omitted, and the user's recommended cadence can be determined directly based on the motion data. This allows the user to learn their recommended cadence in a shorter time, thereby further improving exercise efficiency.

[0131] In some embodiments, the user's recommended cadence can be determined based on motion data. In some embodiments, the user's recommended cadence can be determined based on a cadence recommendation model. In some embodiments, the input of the cadence recommendation model may include, but is not limited to, various types of motion data at a preset cadence, for example, one or any combination of electromyographic signals, posture signals, electrocardiographic signals, respiratory signals, and pressure signals. The output of the cadence recommendation model may be a recommended cadence. In some embodiments, the cadence recommendation model may be a trained machine learning model. In some embodiments, the cadence recommendation model may be pre-trained by a processing device and stored in a storage device, and the processing device may access the storage device to obtain the cadence recommendation model.

[0132] In some embodiments, a cadence recommendation model can be trained based on sample information. The sample information can include motion data from professionals (e.g., fitness trainers) and / or non-professionals during exercise. In some embodiments, the sample information can also include the user's own historical motion data. For example, after a user wears a wearable device, they perform multiple running exercises with different cadences and use a data acquisition device to acquire motion data. This motion data can serve as historical motion data. When motion data is used as sample information, the user's physiological state can be monitored while acquiring the motion data. In some embodiments, the motion data in the sample information can be a processed signal (e.g., segmented, glitched, and converted). In some embodiments, the motion data can be used as input to a machine learning model to train the machine learning model. In some embodiments, feature information corresponding to the motion data can also be used as input to train the machine learning model. For example, the frequency and amplitude information of the electromyographic signal can be used as input to the machine learning model. For another example, the angular velocity, angular velocity direction / angular velocity acceleration value of the posture signal can be used as input to the machine learning model. In some embodiments, the machine learning model may include one or more of a linear classification model (LR), a support vector machine model (SVM), a naive Bayes model (NB), a K-nearest neighbor model (KNN), a decision tree model (DT), an integrated model (RF / GDBT, etc.), etc. In some embodiments, when training the machine learning model, the sample information of different motion data may be labeled. Taking the motion data as electromyographic signals as an example, the electromyographic signals generated by the user exercising at the first step frequency can be labeled as "first step frequency"; the electromyographic signals generated by the user exercising at the second step frequency can be labeled as "second step frequency". Different electromyographic signals correspond to different physiological states. The labeled sample information (i.e., each labeled electromyographic signal) is used as the input of the machine learning model to train the machine learning model, and a step frequency recommendation model for determining the recommended step frequency can be obtained. When the motion data and / or corresponding feature information are input into the machine learning model, the corresponding recommended step frequency can be output.

[0133] In some embodiments, when acquiring sample information (i.e., motion data at different frequencies) used to train the machine learning model, the user's motion speed can be the same. In some embodiments, when acquiring sample information (i.e., motion data at different frequencies) used to train the machine learning model, the user's motion speed can also be different. In this case, the motion speed can be monitored while acquiring the motion data, and the impact of the different motion speeds can be compensated during the subsequent data processing process.

[0134] In some embodiments, to improve the accuracy of the recommended cadence, the processing device can determine the user's recommended cadence based on two or more of the following signals: posture signals, electromyographic signals, mechanical signals, electrocardiographic signals, respiratory signals, sweat signals, etc. That is, posture signals, electromyographic signals, mechanical signals, electrocardiographic signals, respiratory signals, sweat signals, etc., as well as one or more of the user's movement speed, etc., can be used as inputs to the machine learning model to train the machine learning model, so that the recommended cadence output by the machine learning model is more suitable for the user. In some embodiments, in actual applications, the recommended cadence under the target training task (also called the initial recommended cadence) can be estimated based on the information input by the user. The user first exercises at the initial recommended cadence, and the user's actual movement data can be collected during this exercise. The motion data processing system can iteratively update the cadence recommendation model based on the actual movement data, so that the recommended cadence output by the cadence recommendation model is more accurate. In some embodiments, during the long-term use of the user, the motion data processing system can collect more motion data, thereby making the recommended cadence recommended by the motion data processing system more reasonable.

[0135] In some embodiments, embodiments of this specification also provide a wearable device. The wearable device may include a wearable body having at least one sensor disposed thereon, the sensor being used to obtain motion data of a user; and a processor capable of executing the motion data processing method described in Figures 3-6. In some embodiments, the wearable body may include, but is not limited to, one or more of a top, pants, belt, socks, knee pads, wrist guards, and the like. The sensor may include one or more of a myoelectric sensor (one or more electrodes), a posture sensor (e.g., a velocity sensor, an inertial sensor, an angular velocity sensor, etc.), a pressure sensor, and the like. When a user wears the wearable device while running, the sensor contacts a part of the user's body, thereby collecting motion data from the user during exercise. For example, a myoelectric sensor may include one or more electrodes. When one or more electrodes are located on a wearable device at a location corresponding to a muscle, the sensor may collect myoelectric signals from the muscle at the corresponding location. For another example, when one or more electrodes are located on a wearable device at a location corresponding to the abdomen, the sensor may collect electrocardiogram (ECG) signals (or respiratory signals) from the user during exercise. For another example, a posture sensor located on a wearable device at a location corresponding to a joint, such as a knee, elbow, or ankle, may collect posture signals from the user during exercise. For more information about the wearable device and the motion data processing method executed by the processor, please refer to the relevant descriptions of Figures 1 to 6.

[0136] In some embodiments, electrodes provided on the wearable body that contact the user's skin can also be used to stimulate parts of the user's body. In some embodiments, the electrodes can be used to stimulate parts of the user's body to provide the user with a recommended cadence. In some embodiments, the frequency at which the electrodes stimulate the body part (i.e., the stimulation frequency) can be the same as the recommended cadence, and the user adjusts their real-time cadence based on the stimulation received until their cadence matches the stimulation frequency. In some embodiments, the electrodes can also stimulate parts of the user's body at a dynamic stimulation frequency, with the dynamic stimulation frequency changing in the same direction as the user's cadence adjustment. For example, when the cadence adjustment trend is a decreasing frequency trend, the starting frequency of the dynamic stimulation frequency can be the user's real-time cadence, the target frequency of the dynamic stimulation frequency can be the recommended cadence, and the dynamic stimulation frequency gradually decreases from the starting frequency to the target frequency. During this process, the user's cadence remains consistent with the dynamic stimulation frequency, and the user's cadence gradually adjusts to the recommended cadence following the dynamic stimulation frequency. In some embodiments, the dynamic stimulation frequency adjustment process can vary uniformly. In some embodiments, the dynamic stimulation frequency adjustment process can also vary non-uniformly.

[0137] The basic concepts have been described above. It will be apparent to those skilled in the art that the detailed disclosure above is merely illustrative and does not limit the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to the present application. Such modifications, improvements, and amendments are suggested in the present application and remain within the spirit and scope of the exemplary embodiments of the present application.

[0138] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0139] In addition, it will be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0140] A computer storage medium may include a propagated data signal embodying the computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, or any suitable combination thereof. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transfer the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of these.

[0141] The computer program code required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or as a separate software package on the user's computer, or partly on the user's computer and partly on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0142] In addition, unless expressly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing processing device or mobile device.

[0143] Similarly, it should be noted that, in order to simplify the presentation of this application and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not mean that the subject matter of this application requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0144] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0145] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this application, as well as documents (currently or subsequently attached to this application) that limit the broadest scope of the claims of this application. It should be noted that if the descriptions, definitions, and / or use of terms in the accompanying materials of this application are inconsistent or conflicting with the content of this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0146] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other variations may also fall within the scope of this application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this application may be considered consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly introduced and described in this application.

Claims

1. A motion data processing method, comprising: Acquiring motion data of the user at different cadences, wherein the motion data includes at least electromyographic signals; determining a physiological state of the user based on the motion data; as well as A recommended cadence for the user is determined based on the physiological state.

2. The method according to claim 1, wherein Acquiring the user's motion data at different cadences includes: Determine the target speed range; Motion data at different frequencies within the target speed interval is acquired based on the target speed interval.

3. The method according to claim 1, wherein The physiological state includes muscle efficiency, muscle tension, and muscle fatigue, and determining the recommended cadence of the user based on the physiological state includes: determining a correspondence between the user's physiological state and cadence based on at least one of the muscle efficiency, the muscle tension, and the muscle fatigue; and The recommended cadence of the user is determined based on the corresponding relationship.

4. The method according to claim 3, wherein: The determining the physiological state of the user based on the motion data includes: At least one of the muscle efficiency, the muscle tension, and the muscle fatigue of the user is determined based on the electromyographic signal.

5. The method according to claim 1, wherein The motion data includes an electrocardiogram signal and / or a posture signal, and determining the recommended cadence of the user based on the physiological state includes: Determining a correspondence between the user's physiological state and cadence based on the electrocardiogram signal and / or the posture signal; and The recommended cadence of the user is determined based on the corresponding relationship.

6. The method according to claim 5, wherein: The determining the physiological state of the user based on the motion data includes: The physiological state of the user is determined based on the electrocardiogram signal and / or the posture signal, where the physiological state includes the injury risk of the user's movement.

7. The method according to claim 1, wherein The obtaining of the user's motion data at different cadences includes: Obtaining the user's real-time cadence and real-time motion data; Dividing the real-time cadence into a plurality of cadence intervals; Based on the cadence intervals, the motion data in different cadence intervals are determined from the real-time motion data.

8. The method according to claim 7, wherein: The real-time cadence is obtained through an inertial sensor, or the real-time cadence is obtained from the electromyographic signal.

9. The method according to claim 1, wherein: The method further includes: feeding back the recommended cadence to the user.

10. The method according to claim 9, wherein: Feedback of the recommended cadence to the user includes: Obtaining the user's real-time cadence; determining a cadence adjustment trend of the user based on a frequency difference between the real-time cadence and the recommended cadence; The user is guided based on the cadence adjustment trend until the user's cadence is the same as the recommended cadence.

11. The method according to claim 9, wherein Feedback of the recommended cadence to the user includes: Feedback a tempo that is the same as the recommended cadence to the user based on a preset feedback method.

12. The method according to claim 11, wherein The preset feedback method includes at least one of voice feedback, vibration feedback, light feedback, and display feedback.

13. The method according to claim 9, wherein: Feedback of the recommended cadence to the user includes: The user's body part is stimulated based on a biofeedback method to feed back the recommended cadence to the user, wherein the stimulation has a stimulation frequency, and the stimulation frequency is the same as the recommended cadence.

14. The method according to claim 1, wherein The obtaining of the user's motion data at different cadences includes: A plurality of sets of motion data of the user at a plurality of preset cadences are obtained, wherein each set of the plurality of sets of motion data corresponds to one of the plurality of preset cadences.

15. The method according to claim 14, wherein The obtaining of multiple sets of motion data of the user at multiple preset step frequencies includes: Acquiring physiological information of the user, wherein the physiological information includes body information and movement information; The multiple sets of motion data of the user at the multiple preset step frequencies are determined based on the physiological information.

16. A motion data processing system comprising: An acquisition module, configured to acquire motion data of the user at different cadences, wherein the motion data includes at least electromyographic signals; a processing module, configured to determine a physiological state of the user based on the motion data; A recommendation module is configured to determine a recommended cadence for the user based on the physiological state.

17. The system according to claim 16, wherein: Acquiring the user's motion data at different cadences includes: Determine the target speed range; Motion data at different frequencies within the target speed interval is acquired based on the target speed interval.

18. The system according to claim 16, wherein: The physiological state includes muscle efficiency, muscle tension, and muscle fatigue, and determining the recommended cadence of the user based on the physiological state includes: determining a correspondence between the user's physiological state and cadence based on at least one of the muscle efficiency, the muscle tension, and the muscle fatigue; and The recommended cadence of the user is determined based on the corresponding relationship.

19. The system according to claim 18, wherein: The determining the physiological state of the user based on the motion data includes: At least one of the muscle efficiency, the muscle tension, and the muscle fatigue of the user is determined based on the electromyographic signal.

20. The system of claim 16, wherein: The motion data includes an electrocardiogram signal and / or a posture signal, and determining the recommended cadence of the user based on the physiological state includes: Determining a correspondence between the user's physiological state and cadence based on the electrocardiogram signal and / or the posture signal; and The recommended cadence of the user is determined based on the corresponding relationship.

21. The system of claim 20, wherein: The determining the physiological state of the user based on the motion data includes: The physiological state of the user is determined based on the electrocardiogram signal and / or the posture signal, where the physiological state includes the injury risk of the user's movement.

22. The system of claim 16, wherein: The obtaining of the user's motion data at different cadences includes: Obtaining the user's real-time cadence and real-time motion data; Dividing the real-time cadence into a plurality of cadence intervals; Based on the cadence intervals, the motion data in different cadence intervals are determined from the real-time motion data.

23. The system of claim 22, wherein: The real-time cadence is obtained through an inertial sensor, or the real-time cadence is obtained from the electromyographic signal.

24. The system of claim 16, wherein: The system further includes a feedback module configured to provide the user with feedback on the recommended cadence.

25. The system of claim 24, wherein: Feedback of the recommended cadence to the user includes: Obtaining the user's real-time cadence; determining a cadence adjustment trend of the user based on a frequency difference between the real-time cadence and the recommended cadence; The user is guided based on the cadence adjustment trend until the user's cadence is the same as the recommended cadence.

26. The system of claim 24, wherein: Feedback of the recommended cadence to the user includes: Feedback a tempo that is the same as the recommended cadence to the user based on a preset feedback method.

27. The system of claim 26, wherein: The preset feedback method includes at least one of voice feedback, vibration feedback, light feedback, and display feedback.

28. The system of claim 24, wherein: Feedback of the recommended cadence to the user includes: The user's body part is stimulated based on a biofeedback method to feed back the recommended cadence to the user, wherein the stimulation has a stimulation frequency, and the stimulation frequency is the same as the recommended cadence.

29. The system of claim 16, wherein: The obtaining of the user's motion data at different cadences includes: A plurality of sets of motion data of the user at a plurality of preset cadences are obtained, wherein each set of the plurality of sets of motion data corresponds to one of the plurality of preset cadences.

30. The system of claim 29, wherein: The obtaining of multiple sets of motion data of the user at multiple preset step frequencies includes: Acquiring physiological information of the user, wherein the physiological information includes body information and movement information; The multiple sets of motion data of the user at the multiple preset step frequencies are determined based on the physiological information.

31. A wearable device comprising: A wearable body, wherein at least one sensor is provided on the wearable body, and the sensor is used to obtain the user's motion data; as well as A processor configured to execute the method according to any one of claims 1 to 15.

32. The wearable device according to claim 31, wherein: The wearable body is also provided with electrodes that contact the user's skin, and the electrodes are used to stimulate parts of the user's body.

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