Method and system for calibrating athletic data

Abstract

The motion data calibration method and system of the present application converts the motion data of a user during exercise from three-dimensional posture data in three mutually orthogonal coordinate axes into two-dimensional data in a target coordinate system, i.e., posture data in a horizontal plane and posture data in a vertical plane, and divides the motion of the user during exercise into horizontal motion and vertical motion, thereby avoiding data differences due to different orientations of the user. The method and system can eliminate the influence of the user's orientation on the motion data. Therefore, the method and system can calibrate the motion data without requiring a calibration operation by the user.

Description

[Technical Field]

[0001] The present application relates to the field of wearable device technology, and in particular to a method and system for calibrating athletic data. [Background technology]

[0002] As people's interest in scientific exercise and physical health grows, exercise monitoring devices have undergone rapid development. Currently, exercise monitoring devices mainly use sensors to monitor users' motion parameters during exercise. Prior art exercise monitoring devices require users to perform a series of calibration movements (e.g., stretching both arms horizontally in front of them, stretching both arms horizontally to their sides, etc.) to calibrate the sensor coordinates so that the exercise monitoring device can convert posture data in the sensor coordinate system into posture data in the human body coordinate system according to the calibration movements. Each time the user adjusts the position of the exercise monitoring device, the coordinate calibration must be repeated; otherwise, the calculation results will be affected, resulting in a poor user experience. Therefore, there is a need to provide an athletic data calibration method and system that can calibrate athletic data without requiring any calibration action by the user. Summary of the Invention [Problem to be solved by the invention]

[0003] The present application provides an athletic data calibration method and system that can calibrate athletic data without requiring any calibration action by the user. [Means for solving the problem]

[0004] According to a first aspect, the present application provides a motion data calibration method, the method including the steps of: acquiring motion data of a user during motion, the motion data including at least one posture signal corresponding to at least one measurement position on the user's body, each posture signal of the at least one posture signal including three-dimensional posture data in a specific coordinate system of the corresponding measurement position; creating a target coordinate system including three mutually orthogonal coordinate axes, namely an X-axis, a Y-axis, and a Z-axis; and converting each of the posture signals into two-dimensional posture data in the target coordinate system.

[0005] In some embodiments, the each attitude signal comprises data measured by an attitude sensor, and the coordinate system comprises a coordinate system in which the attitude sensor is located.

[0006] In some embodiments, the attitude sensor includes at least one of an acceleration sensor, an angle sensor, and a magnetic sensor.

[0007] In some embodiments, the respective attitude signals include data measured by an image sensor, and the intrinsic coordinate system includes a coordinate system in which the image sensor is located.

[0008] In some embodiments, the three-dimensional attitude data includes angle data and angular velocity data in three mutually orthogonal coordinate axes.

[0009] In some embodiments, the step of converting each of the attitude signals into two-dimensional attitude data in the target coordinate system includes the steps of: acquiring a pre-stored transformation relationship between the target coordinate system and the intrinsic coordinate system; converting each of the attitude signals into three-dimensional motion data in the target coordinate system, including at least angular velocity data about the X-axis, angular velocity data about the Y-axis, and angular velocity data about the Z-axis, based on the transformation relationship; and converting the three-dimensional motion data in the target coordinate system into the two-dimensional attitude data in the target coordinate system.

[0010] In some embodiments, the Z axis of the target coordinate system is in the opposite direction to the vertical direction in which gravitational acceleration occurs.

[0011] In some embodiments, the two-dimensional attitude data in the target coordinate system includes horizontal attitude data including horizontal angle data and horizontal angular velocity data when moving in a horizontal plane perpendicular to the Z axis, and vertical attitude data including vertical angle data and vertical angular velocity data when moving in an arbitrary vertical plane perpendicular to the horizontal plane.

[0012] In some embodiments, the aforementioned step of converting the three-dimensional movement data in the target coordinate system into the two-dimensional posture data in the target coordinate system includes the steps of converting the angular velocity data on the X-axis and the angular velocity data on the Y-axis into the vertical angular velocity data using a vector law; performing time integration on the vertical angular velocity data based on times corresponding to the start and end positions of the user during the exercise to obtain the vertical angle data; using the angular velocity data on the Z-axis as the horizontal angular velocity data; and performing time integration on the horizontal angular velocity data based on times corresponding to the start and end positions of the user during the exercise to obtain the horizontal angle data.

[0013] In some embodiments, the motion data calibration method further comprises determining a relative motion between the at least one measured position based on the two-dimensional attitude data corresponding to each of the attitude signals.

[0014] According to a second aspect, the present specification further provides a movement data calibration system, the system including at least one storage medium and at least one processor, the at least one storage medium storing at least one instruction set for calibrating movement data, the at least one processor being communicatively connected to the at least one storage medium, wherein when the movement data calibration system is operating, the at least one processor reads the at least one instruction set and executes the movement data calibration method described in the first aspect of the present specification.

[0015] As can be seen from the above technical solution, the motion data calibration method and system according to the present application converts a user's motion data during exercise from three-dimensional posture data along three mutually orthogonal coordinate axes into two-dimensional data in a target coordinate system, i.e., posture data in a horizontal plane and posture data in a vertical plane, thereby dividing the user's motion during exercise into horizontal and vertical movements, thereby avoiding data discrepancies due to different user orientations. The method and system can eliminate the influence of the user's orientation on the motion data. Therefore, the method and system can calibrate the motion data without requiring any calibration action by the user.

[0016] The present application is further illustrated by exemplary embodiments, which are not limiting and in which like reference numerals represent like structures, and are illustrated in detail in the drawings. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a schematic diagram of an application scenario of an exercise monitoring system according to some embodiments of the present application; [Figure 2] FIG. 1 is a schematic diagram of exemplary hardware and / or software for a wearable device according to some embodiments of the present application. [Figure 3] FIG. 1 is a schematic diagram of exemplary hardware and / or software for a computing device according to some embodiments of the present application. [Figure 4] FIG. 1 is an exemplary structural diagram of a wearable device according to some embodiments of the present application. [Figure 5] 1 is an exemplary flowchart of an exercise monitoring method according to some embodiments of the present application. [Figure 6] 1 is an exemplary flowchart of a method for calibrating athletic data according to some embodiments of the present application. [Figure 7] FIG. 2 is a schematic diagram of a target coordinate system according to some embodiments of the present application. [Figure 8] 1 is an exemplary flowchart of conversion to two-dimensional pose data according to some embodiments of the present application. [Figure 9] FIG. 2 is a diagram of a coordinate system for a user during exercise according to some embodiments of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0018] In order to more clearly explain the technical solutions of the embodiments of the present application, the drawings necessary for the description of the embodiments will be briefly described below. Obviously, the drawings described below are merely examples or parts of the embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios based on these drawings without any creative effort. Unless otherwise clear from the context or otherwise described, the same symbols in the drawings represent the same structures or operations.

[0019] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are ways of distinguishing between various components, elements, parts, portions, or assemblies at different levels, however, other terms may be used in place of the terms if they achieve the same purpose.

[0020] As set forth in this application and the claims, unless the context clearly indicates an exceptional circumstance, terms such as "a," "one," "one kind," and / or "the" do not specifically refer to the singular but may include the plural. In general, the terms "comprise" and "including" only suggest the inclusion of explicitly identified steps and elements; these steps and elements do not constitute an exclusive enumeration, and a method or apparatus may include other steps or elements.

[0021] In this application, the operations performed by the system according to the embodiment of the application are described using flowcharts. It should be understood that the preceding and subsequent operations are not necessarily performed in exact order. Conversely, the steps may be processed in reverse order or simultaneously. Additionally, other operations may be added to these steps, or one or more operations may be deleted from these steps.

[0022] The present application provides an exercise monitoring system that can acquire a user's motion signals during exercise, where the motion signals include at least an electromyographic signal, a posture signal, an electrocardiographic signal, a respiratory rate signal, etc. The system can monitor the user's exercise based on at least characteristic information corresponding to the electromyographic signal or characteristic information corresponding to the posture signal. For example, the system can determine the user's motion type, number of motions, whether the motion was good or bad, motion duration, or physiological parameter information when the user performs the motion, based on frequency information and amplitude information corresponding to the electromyographic signal, as well as angular velocity, angular velocity direction, angular velocity value, angle, displacement information, stress, etc. corresponding to the posture signal. In some embodiments, the exercise monitoring system can also provide fitness guidance to the user by generating feedback on the user's fitness behavior based on an analysis of the user's fitness behavior. For example, if the user's fitness behavior is not standard, the exercise monitoring system can provide the user with prompt information (e.g., audio prompt, vibration prompt, current stimulation, etc.). The exercise monitoring system can be applied to wearable devices (e.g., clothing, wristbands, helmets), medical testing equipment (e.g., electromyography), fitness equipment, etc., and by acquiring the user's movement signals during exercise, the exercise monitoring system can accurately monitor and provide feedback on the user's movements without the involvement of experts, thereby improving the user's fitness efficiency and reducing the user's fitness costs.

[0023] 1 is a schematic diagram of an application scenario of an exercise monitoring system according to some embodiments of the present application. As shown in FIG. 1, exercise monitoring system 100 may include a processing device 110, a network 120, a wearable device 130, and a mobile terminal device 140. In some embodiments, exercise monitoring system 100 may further include an exercise data calibration system 180. Exercise monitoring system 100 can acquire motion signals (e.g., electromyographic signals, posture signals, electrocardiographic signals, respiratory rate signals, etc.) representing a user's exercise behavior, and monitor and provide feedback on the user's behavior during exercise based on the user's motion signals.

[0024] For example, the exercise monitoring system 100 can monitor and provide feedback to a user's movements during fitness. When a user wears the wearable device 130 and performs fitness exercises, the wearable device 130 can acquire the user's movement signals. The processing device 110 or the mobile terminal device 140 can monitor the user's movements by receiving and analyzing the user's movement signals and determining whether the user's fitness movements are standard. Specifically, monitoring the user's movements may include determining the movement type, number of movements, whether the movement is good or bad, the movement duration, or physiological parameter information when the user performs the movement. Furthermore, the exercise monitoring system 100 can provide fitness guidance to the user by generating feedback on the user's fitness movements based on the analysis results of the user's fitness movements.

[0025] Further, for example, exercise monitoring system 100 can monitor and provide feedback on a user's movements while running. For example, when a user wears wearable device 130 and performs a running exercise, exercise monitoring system 100 can monitor whether the user's running movements are normal, whether the running time meets health standards, etc. If the user's running time is too long or the running movements are incorrect, the fitness equipment can provide feedback on the user's exercise status to warn the user that they need to adjust their running movements or running time.

[0026] In some embodiments, the processing device 110 may be used to process information and / or data related to a user's movement. For example, the processing device 110 may receive a user's movement signal (e.g., an electromyographic signal, a posture signal, an electrocardiographic signal, a respiratory rate signal, etc.) and further extract feature information corresponding to the movement signal (e.g., feature information corresponding to the electromyographic signal in the movement signal, feature information corresponding to the posture signal). In some embodiments, the processing device 110 may perform specific signal processing, such as signal segmentation, signal preprocessing (e.g., signal correction processing, filtering processing, etc.), on the electromyographic signal or the posture signal collected by the wearable device 130. In some embodiments, the processing device 110 may also determine whether the user's movement is correct based on the user's movement signal. For example, the processing device 110 may determine whether the user's movement is correct based on feature information (e.g., amplitude information, frequency information, etc.) corresponding to the electromyographic signal. For example, the processing device 110 may determine whether the user's movement is correct based on feature information corresponding to the posture signal (e.g., angular velocity, angular velocity direction, angular velocity acceleration, angle, displacement information, stress, etc.). For example, the processing device 110 may determine whether the user's movement is correct based on feature information corresponding to the electromyographic signal and feature information corresponding to the posture signal. In some embodiments, the processing device 110 may also determine whether the user's physiological parameter information during exercise meets a health standard. In some embodiments, the processing device 110 may also issue a corresponding command to provide feedback on the user's exercise status. For example, when a user runs, if the exercise monitoring system 100 monitors that the user's running time is too long, the processing device 110 may issue a command to the mobile terminal device 140 to warn the user to adjust the running time.It should be noted that the feature information corresponding to the posture signal is not limited to the above-mentioned angular velocity, angular velocity direction, angular velocity acceleration, angle, displacement information, stress, etc., but may be other feature information, and any parameter information that can be used to indicate the occurrence of movement relative to the user's body may be the feature information corresponding to the posture signal. For example, if the posture sensor is a strain sensor, the flexion angle and flexion direction of the user's joint can be obtained by measuring the magnitude of the resistance of the strain sensor, which changes with the tensile length.

[0027] In some embodiments, processing device 110 may be local or remote. For example, processing device 110 may access information and / or materials stored on wearable device 130 and / or mobile terminal device 140 via network 120. In some embodiments, processing device 110 may be directly connected to wearable device 130 and / or mobile terminal device 140 to access information and / or materials stored thereon. For example, processing device 110 may be located within wearable device 130 and interact with mobile terminal device 140 via network 120. For further example, processing device 110 may be located within mobile terminal device 140 and interact with wearable device 130 via a network. In some embodiments, processing device 110 may run on a cloud platform.

[0028] In some embodiments, processing device 110 can process data and / or information related to exercise monitoring to perform one or more functions described herein. In some embodiments, processing device 110 can acquire a user's motion signal during exercise collected by wearable device 130. In some embodiments, processing device 110 can send control commands to wearable device 130 or mobile terminal device 140. The control commands can control the on / off state of wearable device 130 and its respective sensors, and can also control mobile terminal device 140 to output display information. In some embodiments, processing device 110 may include one or more sub-processing devices (e.g., single-core processing devices or multi-core processing devices).

[0029] Network 120 may facilitate the exchange of data and / or information within athletic monitoring system 100. In some embodiments, one or more components in athletic monitoring system 100 may transmit data and / or information to other components in athletic monitoring system 100 via network 120. For example, motion signals collected by wearable device 130 may be transmitted to processing device 110 via network 120. Also, for example, confirmation results regarding the motion signals from processing device 110 may be transmitted to mobile terminal device 140 via network 120. In some embodiments, network 120 may be any type of wired or wireless network.

[0030] Wearable device 130 refers to clothing or equipment that can be worn. In some embodiments, wearable device 130 may include, but is not limited to, outerwear device 130-1, pants device 130-2, wristband device 130-3, and shoes 130-4. In some embodiments, wearable device 130 may include multiple sensors. The sensors can acquire various motion signals of a user during exercise (e.g., electromyography (EMG) signals, posture signals, temperature information, heart rate, electrocardiogram signals, etc.). In some embodiments, the sensors may include, but are not limited to, one or more of an electromyography (EMG) sensor, a posture sensor, a temperature sensor, a humidity sensor, an electrocardiogram (ECG) sensor, a blood oxygen saturation sensor, a Hall sensor, a skin potential sensor, a rotation sensor, etc. For example, an electromyography (EMG) sensor that fits against the user's skin and can collect the user's EMG signals during exercise may be installed at a position on outerwear device 130-1 corresponding to a person's muscle position (e.g., biceps, triceps, latissimus dorsi, oblique muscles, etc.). For example, an electrocardiogram sensor capable of collecting electrocardiogram signals of the user may be provided at a position on the outer garment device 130-1 corresponding to the vicinity of the person's left pectoral muscle. Furthermore, for example, a posture sensor capable of collecting postural signals of the user may be provided at a position on the pants device 130-2 corresponding to the person's muscles (e.g., gluteus maximus, vastus lateralis, vastus medialis, gastrocnemius, etc.). In some embodiments, the wearable device 130 may also provide feedback on the user's movements. For example, if the movement of a certain body part is abnormal when the user exercises, the electromyogram sensor corresponding to the body part may generate a stimulation signal (e.g., a current stimulation or a percussion signal) to alert the user.

[0031] It should be noted that the wearable device 130 is not limited to the outerwear device 130-1, the pants device 130-2, the wristband device 130-3 and the shoe device 130-4 shown in FIG. 1, but may further include other devices that can be used for exercise monitoring, such as helmet devices, knee pad devices, etc., and is not limited in this specification, and any device that can use the exercise monitoring method included in this application is within the protection scope of this application. In some embodiments, the mobile terminal device 140 can acquire information or data from the exercise monitoring system 100. In some embodiments, the mobile terminal device 140 can receive exercise data processed by the processing device 110 and provide feedback, such as an exercise record, based on the processed exercise data. Exemplary feedback methods may include, but are not limited to, audio presentation, image presentation, video display, text presentation, and the like. In some embodiments, the user can acquire a record of their exercise activity through the mobile terminal device 140. For example, the mobile terminal device 140 may be connected to the wearable device 130 via the network 120 (e.g., via a wired connection or a wireless connection), and the user can acquire a record of their exercise activity through the mobile terminal device 140, which can then be transmitted to the processing device 110 by the mobile terminal device 140. In some embodiments, the mobile terminal device 140 may include one or any combination of a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, and the like. In some embodiments, mobile device 140-1 may include a mobile phone, a smart home device, a smart mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof. In some embodiments, a smart home device may include a smart appliance controller, a smart monitoring device, a smart television, a smart camera, etc., or any combination thereof. In some embodiments, a smart mobile device may include a smartphone, a personal digital assistant (PDA), a gaming device, a navigation device, a point-of-sale device, etc., or any combination thereof. In some embodiments, a virtual reality device and / or an augmented reality device may include a virtual reality helmet, virtual reality glasses, a virtual reality eye mask, an augmented reality helmet, augmented reality glasses, an augmented reality eye mask, etc., or any combination thereof.

[0032] In some embodiments, the motion monitoring system 100 may further include a motion data calibration system 180. The motion data calibration system 180 may be used to process motion data related to a user's motion and may perform the motion data calibration method described herein. Specifically, the motion data calibration system 180 receives motion data of a user during motion and converts the motion data from three-dimensional posture data in three mutually orthogonal coordinate axes into two-dimensional posture data in a target coordinate system, i.e., posture data in a horizontal plane and posture data in a vertical plane, thereby separating the user's motion during motion into horizontal and vertical movements and avoiding data differences due to different user orientations. The motion data calibration system 180 can eliminate the influence of the user's orientation on the motion data and calibrate the motion data without requiring a calibration operation by the user. The motion data may be three-dimensional posture data of a measurement position on the user's body during motion. The motion data and the motion data calibration method will be described in detail later.

[0033] In some embodiments, athletic data calibration system 180 may be integrated on processing device 110. In some embodiments, athletic data calibration system 180 may also be integrated on mobile terminal device 140. In some embodiments, athletic data calibration system 180 may also exist independently of processing device 110 and mobile terminal device 140. Athletic data calibration system 180 is communicatively coupled to processing device 110, wearable device 130, and mobile terminal device 140 to transmit and exchange information and / or data. In some embodiments, athletic data calibration system 180 may access information and / or material stored on processing device 110, wearable device 130, and / or mobile terminal device 140 via network 120. In some embodiments, wearable device 130 may be directly coupled to processing device 110 and / or mobile terminal device 140 to access information and / or material stored thereon. For example, athletic data calibration system 180 may be located in processing device 110 and may interact with wearable device 130 and mobile terminal device 140 via network 120. For further example, athletic data calibration system 180 may be located in mobile terminal device 140 and may interact with processing device 110 and wearable device 130 via the network. In some embodiments, athletic data calibration system 180 may run on a cloud platform and interact with processing device 110, wearable device 130, and mobile terminal device 140 via the network.

[0034] For ease of explanation, in the following description, we will use the example where the movement data calibration system 180 is located in the processing device 110 .

[0035] In some embodiments, athletic monitoring system 100 may further include a database. The database may store information (e.g., preset threshold conditions, etc.) and / or instructions (e.g., feedback instructions). In some embodiments, the database may store information obtained from wearable device 130 and / or mobile terminal device 140. In some embodiments, the database may store information and / or instructions executed or used by processing device 110 to perform the example methods described herein. In some embodiments, the database may be connected to network 120 to communicate with one or more components of athletic monitoring system 100 (e.g., processing device 110, wearable device 130, mobile terminal device 140, etc.). One or more components of athletic monitoring system 100 may access the information or instructions stored in the database via network 120. In some embodiments, the database may be directly connected to or in communication with one or more components in athletic monitoring system 100. In some embodiments, the database may be part of processing device 110.

[0036] 2 is a schematic diagram of exemplary hardware and / or software of a wearable device 130 according to some embodiments of the present application. As shown in FIG. 2, the wearable device 130 may include an acquisition module 210, a processing module 220 (also referred to as a processor), a control module 230 (also referred to as a main controller, MCU, or controller), a communication module 240, a power supply module 250, and an input / output module 260.

[0037] The acquisition module 210 may be used to acquire a motion signal of a user during exercise. In some embodiments, the acquisition module 210 may include a sensor unit, which may be used to acquire one or more motion signals of a user during exercise. In some embodiments, the sensor unit may include, but is not limited to, one or more of an electromyography (EMG) sensor, a posture sensor, an electrocardiogram (ECG) sensor, a respiration sensor, a temperature sensor, a humidity sensor, an inertial sensor, a blood oxygen saturation sensor, a Hall sensor, an electrodermal activity sensor, a rotation sensor, etc. In some embodiments, the motion signal may include one or more of an electromyography (EMG) signal, a posture signal, an electrocardiogram (ECG) signal, a respiration rate, a temperature signal, a humidity signal, etc. The sensor unit may be disposed at different positions on the wearable device 130 depending on the type of motion signal to be acquired. For example, in some embodiments, an electromyography (EMG) sensor (also referred to as an electrode element) may be disposed at a muscle position of a person, and the EMG sensor may be configured to collect an EMG signal of a user during exercise. The EMG signal and its corresponding characteristic information (e.g., frequency information, amplitude information, etc.) may reflect the state of the user's muscles during exercise. The posture sensors may be placed at different positions on the person (e.g., positions on the wearable device 130 corresponding to the trunk, limbs, and joints), and the posture sensors may be configured to collect posture signals of the user during exercise. The posture signals and their corresponding feature information (e.g., angular velocity direction, angular velocity value, angular velocity acceleration value, angle, displacement information, stress, etc.) may reflect the user's posture during exercise. The electrocardiogram sensor may be placed at a position around the person's chest, and the electrocardiogram sensor may be configured to collect electrocardiogram data of the user during exercise. The respiration sensor may be placed at a position around the person's chest, and the respiration sensor may be configured to collect respiration data (e.g., respiration rate, respiration amplitude, etc.) of the user during exercise. The temperature sensor may be configured to collect temperature data (e.g., body surface temperature) of the user during exercise. The humidity sensor may be configured to collect humidity data of the user's external environment during exercise.

[0038] The processing module 220 can process data from the acquisition module 210, the control module 230, the communication module 240, the power supply module 250, and / or the input / output module 260. For example, the processing module 220 can process motion signals from the acquisition module 210 during the user's exercise. In some embodiments, the processing module 220 can perform preprocessing on the motion signals (e.g., electromyographic signals, posture signals) acquired by the acquisition module 210. For example, the processing module 220 can perform segmentation processing on the electromyographic signals or posture signals of the user during exercise. For example, the processing module 220 can improve the quality of the electromyographic signals by preprocessing (e.g., filtering processing, signal correction processing) the electromyographic signals of the user during exercise. For example, the processing module 220 can determine feature information corresponding to the posture signals based on the posture signals of the user during exercise. In some embodiments, the processing module 220 can process commands or operations from the input / output module 260. In some embodiments, the processed data can be stored in a memory or a hard disk. In some embodiments, processing module 220 may transmit the data processed thereby to one or more components in athletic activity monitoring system 100 via communication module 240 or network 120. For example, processing module 220 may send the results of the user athletic activity monitoring to control module 230, which may perform subsequent operations or instructions based on the action determination results.

[0039] The control module 230 may be connected to other modules in the wearable device 130. In some embodiments, the control module 230 may control the operating states of other modules in the wearable device 130. For example, the control module 230 may control the power supply state (e.g., normal mode, power saving mode) and power supply time of the power supply module 250. For example, the control module 230 may control the input / output module 260 based on the user's motion determination result, and may further control the mobile terminal device 140 to send exercise feedback results to the user. If there is a problem with the user's motion during exercise (e.g., the motion is nonstandard), the control module 230 may control the input / output module 260 and may further control the mobile terminal device 140 to provide feedback to the user, so that the user can understand their exercise status in real time and adjust their motion. In some embodiments, the control module 230 may further control one or more sensors or other modules in the acquisition module 210 to provide feedback to the person. For example, if a user exerts excessive force on a muscle while exercising, the control module 230 can control the electrode module at that muscle location to provide electrical stimulation to the user, alerting the user to adjust their movements in real time.

[0040] In some embodiments, the communication module 240 may be used to exchange information or data. In some embodiments, the communication module 240 may be used for communication between internal components of the wearable device 130. For example, the acquisition module 210 may transmit a user's motion signals (e.g., electromyographic signals, postural signals, etc.) to the communication module 240, which may then transmit the motion signals to the processing module 220. In some embodiments, the communication module 240 may also be used for communication between the wearable device 130 and components in the athletic monitoring system 100. For example, the communication module 240 may transmit status information (e.g., on / off status) of the wearable device 130 to the processing device 110, which may then monitor the wearable device 130 based on the status information. The communication module 240 may use wired, wireless, or mixed wired / wireless technologies.

[0041] In some embodiments, power supply module 250 may provide power to other components in athletic activity monitoring system 100 .

[0042] The input / output module 260 can acquire, transmit, and send signals. The input / output module 260 can be connected to or communicate with other components in the athletic monitoring system 100. The other components in the athletic monitoring system 100 can be connected to or communicate with the input / output module 260.

[0043] It should be noted that the above description of the exercise monitoring system 100 and its modules is merely for convenience of explanation and does not limit one or more embodiments of the present application to the scope of the described embodiments. Those skilled in the art will understand that, after understanding the principles of the system, they may arbitrarily combine the modules, form subsystems and connect them to other modules, or omit one or more modules without departing from the principles. For example, the acquisition module 210 and the processing module 220 may be a single module that can acquire and process the user's motion signals. For example, the processing module 220 may be integrated into the processing device 110 rather than being installed in the wearable device 130. All such modifications are within the scope of protection of one or more embodiments of the present application.

[0044] 3 is a schematic diagram of exemplary hardware and / or software of a computing device 300 according to some embodiments of the present application. In some embodiments, processing device 110 and / or mobile terminal device 140 may be implemented on computing device 300. In some embodiments, athletic data calibration system 180 may be implemented on computing device 300. As shown in FIG. 3 , computing device 300 may include an internal communication bus 310, at least one processor 320, at least one storage medium, a communication port 350, an input / output interface 360, and a user interface 380.

[0045] The internal communication bus 310 may facilitate data communication between components in the computing device 300. For example, at least one processor 320 may transmit data to other hardware, such as at least one storage medium or input / output port 360, via the internal communication bus 310. In some embodiments, the internal communication bus 310 may be an Industry Standard Architecture (ISA) bus, an Extended Industry Standard Architecture (EISA) bus, a Video Electronics Standards Association (VESA) bus, a Peripheral Component Interconnect (PCI) bus, or the like. In some embodiments, the internal communication bus 310 may be used to connect modules (e.g., acquisition module 210, processing module 220, control module 230, communication module 240, input / output module 260) in the athletic monitoring system 100 shown in FIG. 1 .

[0046] At least one storage medium of the computing device 300 may include a data storage device. The data storage device may be a non-transitory storage medium or a transitory storage medium. For example, the data storage device may include one or more of a read-only memory (ROM) 330, a random access memory (RAM) 340, a hard disk 370, etc. Exemplary ROMs may include masked ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), optical disk ROM (CD-ROM), and digital versatile disk ROM, etc. Exemplary RAMs may include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero capacitor RAM (Z-RAM), etc. The storage medium may store data / information obtained from any other component of the athletic monitoring system 100. The storage medium further includes at least one instruction set stored in the data storage device. The instructions may be computer program code, which may include programs, routines, objects, components, data structures, processes, modules, etc. that perform athletic data calibration methods according to the present disclosure. In some embodiments, the storage medium of computing device 300 may be located in wearable device 130 or in processing device 110.

[0047] At least one processor 320 may be communicatively connected to at least one storage medium. The at least one processor 320 is configured to execute the at least one instruction set. When the computing device 300 is in operation, the at least one processor 320 reads the at least one instruction set and executes the computing instructions (program code) according to the instructions of the at least one instruction set to perform the functions of the athletic monitoring system 100 described herein. The processor 320 may execute all steps included in a data processing method. The computing instructions may include programs, objects, components, data structures, processes, modules, and functions (the functions refer to specific functions described herein). For example, the processor 320 may process motion signals (e.g., electromyographic signals, posture signals) of a user during exercise acquired from the wearable device 130 and / or the mobile terminal device 140 of the athletic monitoring system 100 and monitor the user's athletic movements based on the motion signals of the user during exercise. For example, processor 320 may be used to process motion data of a user during exercise acquired from wearable device 130 and / or mobile terminal device 140 of exercise monitoring system 100, perform the motion data calibration method described herein according to instructions in the at least one instruction set, and convert the motion data into two-dimensional posture data. In some embodiments, processor 320 may include a microcontroller, a microprocessor, a reduced instruction set computer (RISC), a special-purpose integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced reduced instruction set computer (ARM), a programmable logic device, any circuit or processor capable of performing one or more functions, or any combination thereof.For purposes of illustration only, the computing device 300 in FIG. 3 is illustrated with only one processor 320, however, it should be noted that the computing device 300 in the present application may also include multiple processors 320.

[0048] Hard disk 370 may be used to store information and data generated by or received from processing device 110. For example, hard disk 370 may store user identification information for a user. In some embodiments, hard disk 370 may be located within processing device 110 or within wearable device 130.

[0049] User interface 380 may facilitate interaction and information exchange between computing device 300 and a user. In some embodiments, user interface 380 may be used to present to a user athletic records generated by athletic monitoring system 100. In some embodiments, user interface 380 may include a physical display, such as a display with speakers, an LCD display, an LED display, an OLED display, an electronic ink display (E-Ink), etc.

[0050] Input / output interface 360 ​​may be used for inputting or outputting signals, data, or information. In some embodiments, input / output interface 360 ​​may enable a user to interact with athletic monitoring system 100.

[0051] FIG. 4 is an exemplary structural diagram of a wearable device according to some embodiments of the present application. To further explain the wearable device, a jacket will be taken as an example. As shown in FIG. 4 , the wearable device 400 may include a jacket 410. The jacket 410 may include a jacket base 4110, at least one jacket processing module 4120, at least one jacket feedback module 4130, at least one jacket acquisition module 4140, etc. The jacket base 4110 may refer to clothing worn on a person's upper body. In some embodiments, the jacket base 4110 may include a short-sleeved T-shirt, a long-sleeved T-shirt, a dress shirt, a jacket, etc. The at least one jacket processing module 4120 and the at least one jacket acquisition module 4140 may be arranged in areas of the jacket base 4110 that fit different parts of the person. The at least one outerwear feedback module 4130 may be disposed at any position on the outerwear base 4110, and the at least one outerwear feedback module 4130 may be configured to provide feedback on the user's upper body motion state information. Exemplary feedback methods may include, but are not limited to, audio feedback, text feedback, pressure feedback, and current stimulation. In some embodiments, the at least one outerwear acquisition module 4140 may include, but is not limited to, one or more of a posture sensor, an electrocardiogram sensor, an electromyogram sensor, a temperature sensor, a humidity sensor, an inertial sensor, an acid / base sensor, an acoustic transducer, and the like. The sensors in the outerwear acquisition module 4140 may be disposed at different positions on the user's body depending on the signal to be measured. For example, if the posture sensor is intended to acquire a posture signal during a user's exercise, it may be disposed at a position on the outerwear base 4110 corresponding to a person's torso, arms, or joints. For example, if the posture sensor is intended to acquire a myoelectric signal during a user's exercise, it may be disposed near the user's muscle to be measured. In some embodiments, the attitude sensor may include, but is not limited to, a three-axis acceleration sensor, a three-axis angular velocity sensor, a magnetic sensor, or any combination thereof. For example, one attitude sensor may include a three-axis acceleration sensor and a three-axis angular velocity sensor.In some embodiments, the posture sensor may further include a strain sensor. The strain sensor may be a sensor based on strain caused by deformation of an object under force. In some embodiments, the strain sensor may include, but is not limited to, one or more of a strain gauge load cell, a strain gauge pressure sensor, a strain gauge torque sensor, a strain gauge displacement sensor, a strain gauge acceleration sensor, etc. For example, the strain sensor may be installed at the position of a user's joint, and the bending angle and bending direction of the user's joint can be obtained by measuring the magnitude of the resistance of the strain sensor that changes with the pulling length. It should be noted that the jacket 410 may further include other modules, such as a power supply module, a communication module, an input / output module, etc., in addition to the above-mentioned jacket base 4110, jacket processing module 4120, jacket feedback module 4130, and jacket acquisition module 4140. The outerwear processing module 4120 is similar to the processing module 220 in Figure 2, and the outerwear acquisition module 4140 is similar to the acquisition module 210 in Figure 2. For specific descriptions of each module in the outerwear 410, please refer to the relevant descriptions in Figure 2 of the present application, and no further description will be given here.

[0052] 5 is an exemplary flowchart of an exercise monitoring method according to some embodiments of the present application. As shown in FIG. 5, a flow 500 may include the following steps 510 to 520.

[0053] In step 510, a movement signal of a user is acquired during exercise. In some embodiments, step 510 may be performed by the acquisition module 210. The motion signal refers to human body parameter information of the user during exercise. In some embodiments, the human body parameter information may include, but is not limited to, one or more of an electromyographic signal, a posture signal, an electrocardiographic signal, a temperature signal, a humidity signal, a blood oxygen level, a respiratory rate, etc. In some embodiments, an electromyographic sensor in the acquisition module 210 may collect electromyographic signals during the user's exercise. For example, when a user performs a chest press, an electromyographic sensor in the wearable device corresponding to a position of the person's pectoral muscles, latissimus dorsi, etc. may collect electromyographic signals of the user's corresponding muscle positions. For example, when a user performs a squat, an electromyographic sensor in the wearable device corresponding to a position of the person's gluteus maximus, quadriceps, etc. may collect electromyographic signals of the user's corresponding muscle positions. For example, when a user performs a running exercise, an electromyographic sensor in the wearable device corresponding to a position of the person's gastrocnemius, etc. may collect electromyographic signals of the user's corresponding muscle positions. In some embodiments, the posture sensor in the acquisition module 210 can collect posture signals of a user during exercise. For example, when a user performs a bench press exercise, a posture sensor in the wearable device corresponding to a position such as the user's triceps can collect posture signals of the user's triceps. For example, when a user performs a dumbbell fly exercise, a posture sensor installed in a position such as the user's deltoid can collect posture signals of the user's deltoid. In some embodiments, the acquisition module 210 may have multiple posture sensors, and the multiple posture sensors can acquire posture signals of multiple parts of the user's body during exercise. The multiple part posture signals can reflect the relative movement status between different parts of the user. For example, a posture signal of an arm and a posture signal of a torso can reflect the movement status of an arm relative to the torso. In some embodiments, the posture signal is related to the type of posture sensor. For example, when the posture sensor is a three-axis angular velocity sensor, the acquired posture signal is angular velocity information.For example, if the posture sensor is a three-axis angular velocity sensor and a three-axis acceleration sensor, the acquired posture signal is angular velocity information and acceleration information. For example, if the posture sensor is a strain sensor, the strain sensor may be installed at the user's joint position, and the acquired posture signal may be displacement information, stress, etc., by measuring the magnitude of the resistance of the strain sensor that changes with the tensile length, and this posture signal can represent the flexion angle and flexion direction of the user's joint. It should be noted that any parameter information available to indicate the relative movement of the user's body may be feature information corresponding to the posture signal, and different types of posture sensors may be used to acquire the information depending on the type of feature information.

[0054] In some embodiments, the movement signal may include an electromyographic signal of a specific part of the user's body and a posture signal of the specific part. The electromyographic signal and the posture signal can reflect the movement state of the specific part of the user's body from different angles. In simple terms, the posture signal of the specific part of the user's body can reflect the movement type, movement amplitude, movement frequency, etc. of the specific part. The electromyographic signal can reflect the muscle state of the specific part during movement. In some embodiments, the electromyographic signal and / or the posture signal of the same part of the body can better evaluate whether the movement of the part is normal.

[0055] At step 520, the user's athletic performance is monitored based on at least the feature information corresponding to the electromyographic signal or the feature information corresponding to the postural signal.

[0056] In some embodiments, this step may be performed by the processing module 220 and / or the processing device 110. In some embodiments, the feature information corresponding to the EMG signal may include, but is not limited to, one or more of frequency information, amplitude information, etc. The feature information corresponding to the posture signal refers to parameter information for representing the occurrence of a relative movement of the user's body. In some embodiments, the feature information corresponding to the posture signal may include, but is not limited to, one or more of an angular velocity direction, an angular velocity value, an angular velocity acceleration value, etc. In some embodiments, the feature information corresponding to the posture signal may further include an angle, displacement information (e.g., a tension length in a strain sensor), stress, etc. For example, if the posture sensor is a strain sensor, the strain sensor may be installed at a joint position of the user, and the acquired posture signal may be displacement information, stress, etc. by measuring the magnitude of the resistance of the strain sensor that changes with the tension length, and the posture signal may represent the flexion angle and flexion direction of the user's joint. In some embodiments, the processing module 220 and / or the processing device 110 may extract feature information corresponding to the electromyographic signal (e.g., frequency information, amplitude information) or feature information corresponding to the posture signal (e.g., angular velocity direction, angular velocity value, angular velocity acceleration value, angle, displacement information, stress, etc.), and monitor the user's exercise movement based on the feature information corresponding to the electromyographic signal or the feature information corresponding to the posture signal. Here, monitoring the user's exercise movement includes monitoring information related to the user's movement. In some embodiments, the information related to the movement may include one or more of the user's movement type, movement count, movement quality (e.g., whether the user's movement is standard), movement duration, etc. The movement type refers to the user's fitness movement during exercise. In some embodiments, the movement type may include one or more of, but is not limited to, chest press, squat exercise, deadlift exercise, plank, running, swimming, etc. The movement count refers to the number of times the user performs a movement during exercise. For example, if a user performs 10 chest presses during exercise, 10 is the movement count.The quality of a movement refers to the degree to which the fitness movement performed by the user compares with a standard fitness movement. For example, when a user performs a squat movement, the processing device 110 can determine the movement type of the user's movement based on feature information corresponding to the movement signals (electromyography signals and posture signals) of specific muscle positions (such as the gluteus maximus and quadriceps), and determine the quality of the user's squat movement based on the movement signals of the standard squat movement. The movement time refers to the time corresponding to one or more movement types of the user or the total time of exercise.

[0057] As described above, the feature information corresponding to the posture signal may include parameter information that can be used to indicate the occurrence of relative motion on the user's body. To monitor the user's movements during exercise, the exercise monitoring system 100 needs to acquire the relative motion between different parts of the user's body. As described above, the posture signal may be posture data measured by posture sensors. The posture sensors may be distributed on multiple different parts of the user's body. To acquire the relative motion between the multiple different parts of the user's body, the exercise monitoring system 100 may calibrate the posture signals between the multiple different parts on the user's body.

[0058] FIG. 6 is an exemplary flowchart of an athletic data calibration method 3000 according to some embodiments of the present application. As described above, athletic data calibration system 180 can execute athletic data calibration method 3000 according to the present application. Specifically, athletic data calibration system 180 can execute athletic data calibration method 3000 on processing device 110, and can also execute athletic data calibration method 3000 on mobile terminal device 140. For convenience of explanation, the inventors will use an example in which athletic data calibration system 180 executes athletic data calibration method 3000 on processing device 110. Specifically, processor 320 in processing device 110 can read an instruction set stored in its local storage medium and execute athletic data calibration method 3000 according to the instructions of the instruction set. The athletic data calibration method 3000 may include the following steps S3020 to S3060.

[0059] S3020: Obtain motion data of the user during exercise. In some embodiments, this step may be performed by the processing module 220 and / or the processing device 110. The motion data refers to human body motion parameter information of the user during exercise. In some embodiments, the motion data may include at least one posture signal corresponding to at least one measurement position on the user's body. The posture signal and its corresponding characteristic information (e.g., angular velocity direction, angular velocity value, angular velocity acceleration value, angle, displacement information, stress, etc.) can reflect the user's posture during exercise. The at least one measurement position has a one-to-one correspondence with the at least one posture signal. The measurement positions may be different parts of the user's body. The at least one posture signal corresponds to the actual posture of the at least one measurement position on the user's body when the user is exercising. Each posture signal of the at least one posture signal may include three-dimensional posture data in an intrinsic coordinate system of the corresponding measurement position. The intrinsic coordinate system may be a coordinate system in which the posture signal is located. Even if the user's posture does not change, a change in the intrinsic coordinate system may cause a change in the posture signal. Each posture signal may include one or more three-dimensional posture data. For example, three-dimensional angle data, three-dimensional angular velocity data, three-dimensional angular acceleration data, three-dimensional velocity data, three-dimensional displacement data, three-dimensional stress data, and the like.

[0060] In some embodiments, the posture signal may be acquired by a posture sensor on the wearable device 130. As described above, the sensor unit of the acquisition module 210 in the wearable device 130 may include a posture sensor. Specifically, the wearable device 130 may include at least one posture sensor. The at least one posture sensor may be disposed at at least one measurement position on the user's body. The posture sensor can collect posture signals at the corresponding measurement position on the user's body. The posture sensors on the wearable device 130 may be distributed at limbs (e.g., arms, legs, etc.), torso (e.g., chest, abdomen, back, waist, etc.), and head of the person. The posture sensors can also collect posture signals at other parts of the person, such as limbs and torso. The posture sensors may be disposed at different positions on the wearable device 130 according to the posture signal to be acquired, so as to measure posture signals corresponding to different positions of the person. In some embodiments, the posture sensor may further include an posture fusion algorithm posture measurement unit (AHRS). The posture fusion algorithm can acquire a posture signal of a user's body part where the posture sensor is located by fusing data from a 9-axis inertial measurement unit (IMU) having a 3-axis acceleration sensor, a 3-axis angle sensor, and a 3-axis geomagnetic sensor into Euler angles or quaternions. In some embodiments, the processing module 220 and / or the processing device 110 can determine feature information corresponding to posture based on the posture signal. In some embodiments, the feature information corresponding to the posture signal may include, but is not limited to, an angular velocity value, an angular velocity direction, an angular velocity acceleration value, etc. In some embodiments, the posture sensor may be a strain sensor, and the strain sensor can acquire a posture signal of a user during exercise by acquiring a flexion direction and flexion angle of a user joint. For example, the strain sensor may be installed on the user's knee joint. When the user exercises, the user's body part acts on the strain sensor, and the flexion direction and flexion angle of the user's knee joint can be calculated based on the resistance or length change of the strain sensor, thereby acquiring a posture signal of the user's thigh.In some embodiments, the posture sensor may further include an optical fiber sensor, and the posture signal may be represented by a change in direction of light from the optical fiber sensor after bending. In some embodiments, the posture sensor may further include a magnetic flux sensor, and the posture signal may be represented by a change in the magnetic flux. It should be noted that the type of posture sensor is not limited to the above sensors and may be other sensors, and any sensor that can obtain a user posture signal is within the scope of the posture sensor of the present application.

[0061] As described above, each attitude signal may include one or more three-dimensional attitude data. The attitude sensor may include multiple types of sensors. In some embodiments, the attitude sensor may include at least one of an acceleration sensor, an angle sensor, and a magnetic sensor.

[0062] When the posture signal is data measured by a posture sensor, the intrinsic coordinate system may be the coordinate system in which the posture sensor is located. In some embodiments, the intrinsic coordinate system refers to a coordinate system corresponding to a posture sensor attached to a person. When a user uses the wearable device 130, the posture sensors on the wearable device 130 are distributed at different locations on the person, and therefore the attachment angles of the posture sensors on the person are different. As a result, the posture sensors on different locations have their own coordinate systems as intrinsic coordinate systems. In some embodiments, the posture signals acquired by each posture sensor may be expressed in its corresponding intrinsic coordinate system. The posture signals acquired by the posture sensors may be posture signals in an intrinsic coordinate system of a preset fixed coordinate system. The preset fixed coordinate system may be a geodetic coordinate system or any other preset coordinate system. The movement data calibration system 180 may pre-store a transformation relationship between the intrinsic coordinate system and the preset fixed coordinate system.

[0063] In some embodiments, the attitude signal may be a signal directly acquired by an attitude sensor, or may be a attitude signal formed by performing signal processing processes such as regular filtering, rectification, wavelet transform, glitch processing, etc. on the signal directly acquired by the attitude sensor, or may be a signal obtained by combining any one or more of the above processing flows.

[0064] In some embodiments, the posture signal may be data measured by an image sensor. The image sensor may be an image sensor capable of acquiring depth information, such as a 3D structured light camera, a binocular camera, or the like. The image sensor may be attached at any position capable of capturing an image of the user's movements. The number of image sensors may be one or more. When there are multiple image sensors, the multiple image sensors may be attached at multiple different positions. The image sensor may acquire a depth image of the user during exercise. The depth image may include depth information of at least one measurement position on the user's body relative to a coordinate system in which the image sensor is located. When the user exercises, the motion data calibration system 180 may calculate a posture signal for each measurement position of the at least one measurement position based on changes in multiple frames of depth images. As described above, each posture signal may include one or more three-dimensional posture data. The motion data calibration system 180 may calculate different three-dimensional posture data.

[0065] When the attitude signal is data measured by an image sensor, the intrinsic coordinate system may be the coordinate system in which the image sensor itself is located. In some embodiments, the attitude signal acquired by the image sensor may be expressed in the coordinate system (intrinsic coordinate system) of the corresponding image sensor itself. The image sensor may be pre-calibrated. That is, the motion data calibration system 180 may pre-store a transformation relationship between the coordinate system of the image sensor itself and the previously set fixed coordinate system.

[0066] For convenience of explanation, in the following description, the inventors will take as an example that the motion data is data measured by at least one posture sensor. For convenience of explanation, the inventors will define the intrinsic coordinate system as an o-xyz coordinate system. Here, o is the coordinate origin of the intrinsic coordinate system o-xyz, and the x-axis, y-axis, and z-axis are three mutually orthogonal coordinate axes of the intrinsic coordinate system o-xyz.

[0067] As described above, each attitude signal may be three-dimensional attitude data in the intrinsic coordinate system o-xyz of its corresponding measurement position. In some embodiments, the three-dimensional attitude data may be attitude data in three mutually orthogonal coordinate axes in the coordinate system in which it is located. In some embodiments, the attitude data may include angle data and angular velocity data. In some embodiments, the three-dimensional attitude data in the intrinsic coordinate system o-xyz may include angle data and angular velocity data in three mutually orthogonal coordinate axes x-axis, y-axis, and z-axis. For convenience of explanation, the inventors refer to the three-dimensional attitude data in the intrinsic coordinate system o-xyz of each attitude signal as three-dimensional angle (Euler) data E. sens and three-dimensional angular velocity (Gyro) data G sens Mark as 3D angle data E sens is the angle data E on the x-axis sens_x , the angle data E in the y-axis sens_y and angle data E on the z-axis sens_z The three-dimensional angular velocity data G sens is the angular velocity data G on the x-axis sens_x , angular velocity data G on the y-axis sens_y and angular velocity data G on the z-axis sens_z may include:

[0068] S3040: Create a target coordinate system. In some embodiments, this step may be performed by the processing module 220 and / or the processing device 110. For ease of explanation, we define the target coordinate system as O-XYZ. To easily determine the relative motion between different parts of the user, the motion data calibration system 180 may convert the motion data into posture data in the same known coordinate system (e.g., the target coordinate system is defined as O-XYZ). The target coordinate system O-XYZ may include three mutually orthogonal coordinate axes: X-axis, Y-axis, and Z-axis.

[0069] In some embodiments, the target coordinate system O-XYZ may be a calibrated arbitrary coordinate system. In some embodiments, the target coordinate system O-XYZ may be the aforementioned preset fixed coordinate system. In some embodiments, the target coordinate system O-XYZ and the aforementioned preset fixed coordinate system may be different coordinate systems. The motion data calibration system 180 may pre-store a transformation relationship between the aforementioned preset fixed coordinate system and the target coordinate system O-XYZ.

[0070] The motion data calibration system 180 is used to calibrate the motion data of a user during exercise, and the measurement target is the user. Therefore, in some embodiments, the target coordinate system O-XYZ may have the Z axis along the longitudinal direction of the torso when the person is standing. That is, the Z axis is the opposite direction to the vertical direction of gravitational acceleration. That is, the Z axis is a coordinate axis perpendicular to the ground and pointing skyward. The plane formed by the X axis and the Y axis is a horizontal plane perpendicular to the Z axis. In some embodiments, the X axis and the Y axis may be any two coordinate axes perpendicular to each other in a horizontal plane perpendicular to the Z axis. In some embodiments, the X axis may be an east-west coordinate axis, e.g., a coordinate axis pointing east, and the Y axis may be a north-south coordinate axis, e.g., a coordinate axis pointing north. FIG. 7 shows a schematic diagram of the target coordinate system O-XYZ according to an embodiment of the present specification. Here, the X axis direction is directly in front of the user 001 when standing.

[0071] S3060: Each of the attitude signals is converted into two-dimensional attitude data in the target coordinate system. 8 is an exemplary flowchart of conversion to two-dimensional posture data according to some embodiments of the present application. Fig. 8 corresponds to step S3060. As shown in Fig. 8, step S3060 may include the following steps S3062 to S3066.

[0072] S3062: The conversion relationship between the target coordinate system O-XYZ and the inherent coordinate system o-xyz stored in advance is acquired. As described above, the posture signal acquired by each posture sensor may be expressed in its corresponding intrinsic coordinate system. Specifically, the posture signal acquired by each posture sensor may be a posture signal in the intrinsic coordinate system of a measurement position in a preset fixed coordinate system corresponding to the current posture signal. The movement data calibration system 180 can acquire, based on each posture signal, a representation of the intrinsic coordinate system o-xyz of the corresponding posture sensor in the preset fixed coordinate system by inverse transformation. As described above, the movement data calibration system 180 may pre-store a transformation relationship between the preset fixed coordinate system and the target coordinate system O-XYZ. The movement data calibration system 180 can calculate and determine a transformation relationship between each intrinsic coordinate system o-xyz and the target coordinate system O-XYZ based on the transformation relationship between the preset fixed coordinate system and the target coordinate system O-XYZ. The movement data calibration system 180 can convert posture information in the intrinsic coordinate system o-xyz into posture information in the target coordinate system O-XYZ based on the transformation relationship. In some embodiments, the transformation relationship may be expressed as one or more rotation matrices, which may be pre-stored in movement data calibration system 180.

[0073] S3064: Based on the conversion relationship between the target coordinate system O-XYZ and the inherent coordinate system O-xyz, each of the attitude signals is converted into three-dimensional movement data in the target coordinate system O-XYZ.

[0074] As described above, each attitude signal is represented by three-dimensional attitude data E in the corresponding unique coordinate system o-xyz. sens_x , E sens_y , E sens_z , Gsens_x , G sens_y and G sens_z The motion data calibration system 180 can determine three-dimensional motion data in the target coordinate system O-XYZ of the intrinsic coordinate system O-xyz, where a measurement position corresponding to each attitude signal is located, based on a transformation relationship between the target coordinate system O-XYZ and the intrinsic coordinate system O-xyz. In some embodiments, the three-dimensional motion data includes angular velocity data G in at least the X-axis. global_X , angular velocity data G on the Y axis global_Y and angular velocity data G on the Z axis global_Z Includes:

[0075] S3066: The three-dimensional motion data in the target coordinate system O-XYZ is converted into two-dimensional posture data in the target coordinate system O-XYZ.

[0076] The two-dimensional posture data is data in a two-dimensional coordinate system. The two-dimensional coordinate system in the target coordinate system O-XYZ may include a motion plane when the limbs of the user 001 swing and a motion plane when the torso of the user 001 rotates. In some embodiments, the two-dimensional posture data in the target coordinate system O-XYZ may include horizontal posture data and vertical posture data. The horizontal posture data is horizontal angle data E when moving in a horizontal plane perpendicular to the Z axis. global_Z and horizontal angular velocity data G global_Z The vertical attitude data may include vertical angle data E when moving in an arbitrary vertical plane perpendicular to the horizontal plane. global_XY and vertical angular velocity data G global_XY The vertical plane may be any plane perpendicular to a horizontal plane. global_Z may be the rotation angle of the measurement position in the horizontal plane in the target coordinate system O-XYZ. global_Z may be the rotation angular velocity of the measurement position in the horizontal plane in the target coordinate system O-XYZ. global_XY may be the rotation angle of the measurement position in any one of the vertical planes in the target coordinate system O-XYZ. global_XYmay be the rotational angular velocity of the measurement position in any one of the vertical planes in the target coordinate system O-XYZ.

[0077] For most fitness movements in a gym, the main parts of user 001's movements can be broken down into movements on two planes. For example, movement on a horizontal plane and movement on one of the vertical planes. Therefore, different movements performed by user 001 during exercise can be distinguished only by movement on the horizontal plane and movement on the vertical plane. For example, when user 001 runs on a treadmill, user 001's running movement is primarily rotational movement around joints parallel to the horizontal plane. In this case, the rotational movement occurs in a vertical plane perpendicular to the horizontal plane, and the vertical plane extends along the orientation of user 001's body. In this vertical plane, user 001's center of gravity moves linearly up and down, and the user's limbs swing in accordance with the center of gravity. For example, when user 001 performs an arm curl, the arm curl movement only occurs in the vertical plane. Furthermore, for example, when user 001 performs a chest press movement, the chest press movement only occurs in the horizontal plane.

[0078] FIG. 9 is a coordinate system diagram of a user 001 during exercise according to some embodiments of the present application. In FIG. 9, when the user 001 performs an arm curl, the orientation of the user 001 hardly changes. The arm curl motion is mainly in a vertical plane P formed by the forearm AO' and the upper arm O'B. Because the motion overcomes the gravity of the dumbbell, the plane P is generally perpendicular to the horizontal XY plane. Within the vertical plane P, the posture of the upper arm O'B remains almost constant, while the forearm AO' swings back and forth around the elbow O'. The swing vector direction is normal to the vertical plane P. In addition, the weight of the user 001's arm curl motion in other directions is small and can be ignored. Based on the above analysis, the motion data calibration system 180 then converts the three-dimensional motion data into two-dimensional posture data.

[0079] As shown in FIG. 8, step S3066 may include the following steps S3066-2 to S3066-8.

[0080] S3066-2: Angular velocity data G on the X axis global_X and angular velocity data G on the Y axis global_Y The vertical angular velocity data G is calculated by the vector law. global_XY Convert to. Vertical angular velocity data G global_XY may be expressed as follows:

number

number

number

[0081] S3066-4: Vertical angular velocity data G based on the time corresponding to the start and end positions of user 001 during exercise global_XY Time integration is performed on the vertical angle data E global_XY Get. Here, the vertical angle data E global_XY may be expressed as the following formula:

number

[0082] Here, startpos and endpos are the start time and end time corresponding to the start position and end position of one movement.

[0083] S3066-6: Angular velocity data G on the Z axis global_Z The horizontal angular velocity data G global_Z Let's say.

[0084] S3066-8: Horizontal angular velocity data G based on the time corresponding to the start position and end position of user 001 during exercise global_Z Time integration is performed on the horizontal angle data Eglobal_Z Get.

[0085] Here, the horizontal angle data E global_Z may be expressed as the following formula:

number

[0086] In some embodiments, the motion data calibration method 3000 comprises: S3080: The method may further include determining relative motion between the at least one measurement position based on the two-dimensional attitude data corresponding to each of the attitude signals.

[0087] The motion data calibration system 180 can determine the relative motion between different motion parts of the user's 001 body by using at least one two-dimensional posture data corresponding to at least one posture signal corresponding to at least one measurement position on the user's 001 body. For example, the relative motion between the user's 001 arm and the user's 001 torso during exercise can be determined by using feature information corresponding to a posture sensor on the user's 001 arm and feature information corresponding to a posture sensor on the user's 001 torso.

[0088] As described above, the motion data calibration method 3000 and system 180 according to the present application convert the motion data of the user 001 during exercise from three-dimensional posture data in three mutually orthogonal coordinate axes into two-dimensional data in a target coordinate system, i.e., posture data in a horizontal plane and posture data in a vertical plane, and divide the motion of the user 001 during exercise into horizontal and vertical movements, thereby avoiding data differences due to different orientations of the user 001. The method 3000 and system 180 can eliminate the influence of the orientation of the user 001 on the motion data. Therefore, the method 3000 and system 180 can calibrate the motion data without requiring any calibration action by the user 001.

[0089] Although the basic concept has been described above, it will be apparent to those skilled in the art that the above detailed disclosure is provided by way of example only and is not intended to limit the present application. Although not expressly described herein, those skilled in the art may make various changes, improvements, and modifications to the present application. These changes, improvements, and modifications are within the spirit and scope of the exemplary embodiments of the present application, as proposed in the present application. Accordingly, certain terms are used herein to describe embodiments of the present application. For example, "one embodiment," "one embodiment," and / or "some embodiments" refer to particular features, structures, or characteristics associated with at least one embodiment of the present application. As such, it is emphasized and understood that the appearances of "one embodiment," "one embodiment," or "one alternative embodiment" more than once in various parts of the present application do not necessarily all refer to the same embodiment. However, particular features, structures, or characteristics in one or more embodiments of the present application may be combined as appropriate.

Claims

1. 1. A method for calibrating exercise data, comprising: acquiring motion data of a user during exercise based on a plurality of sensors, the motion data includes a plurality of posture signals corresponding to a plurality of measurement locations on the user's body; Each of the plurality of attitude signals includes three-dimensional attitude data in an inherent coordinate system of a corresponding measurement position, the inherent coordinate system being composed of x-axis, y-axis, and z-axis that are orthogonal to each other, and the three-dimensional attitude data being composed of x-axis angular velocity data G sens_x on the x-axis, y-axis angular velocity data G sens_y on the y-axis, and z-axis angular velocity data G sens_z on the z-axis. Steps and creating a target coordinate system including three mutually orthogonal coordinate axes, namely, an X-axis, a Y-axis, and a Z-axis; converting each of the plurality of attitude signals into three-dimensional motion data in the target coordinate system, the three-dimensional motion data consisting of X-axis angular velocity data G global_X on the X-axis, Y-axis angular velocity data G global_Y on the Y-axis, and Z-axis angular velocity data G global_Z on the Z-axis; generating two-dimensional attitude data in the target coordinate system based on the three-dimensional motion data and the three-dimensional attitude data, the two-dimensional attitude data including vertical angular velocity data G global_XY and horizontal angular velocity data G global_Z , the vertical angular velocity data G global_XY satisfying the following equations (1) to (3); Including, A method for calibrating exercise data, comprising: [Equation 1] [Equation 2] [Equation 3]

2. each of the attitude signals includes data measured by an attitude sensor; the intrinsic coordinate system includes a coordinate system in which the attitude sensor is located; 2. The movement data calibration method according to claim 1, wherein:

3. the attitude sensor includes at least one of an acceleration sensor, an angle sensor, and a magnetic sensor; 3. The movement data calibration method according to claim 2.

4. each of the attitude signals includes data measured by an image sensor; the intrinsic coordinate system includes a coordinate system in which the image sensor is located; 2. The movement data calibration method according to claim 1, wherein:

5. the three-dimensional posture data includes angle data and angular velocity data in three coordinate axes; 2. The movement data calibration method according to claim 1, wherein:

6. The step of converting each of the attitude signals into three-dimensional motion data in the target coordinate system described above includes: obtaining a pre-stored transformation relationship between the target coordinate system and the intrinsic coordinate system; converting each of the attitude signals into three-dimensional motion data in the target coordinate system based on the conversion relationship; Including, 2. The movement data calibration method according to claim 1, wherein:

7. The Z axis of the target coordinate system is in the opposite direction to the vertical direction in which gravitational acceleration occurs.

7. The movement data calibration method according to claim 6.

8. The two-dimensional attitude data in the target coordinate system is the horizontal attitude data including horizontal angle data and horizontal angular velocity data when moving in a horizontal plane perpendicular to the Z axis; the vertical attitude data including the vertical angle data and vertical angular velocity data when moving in an arbitrary vertical plane perpendicular to the horizontal plane; Including, 8. The movement data calibration method according to claim 7.

9. The step of generating two-dimensional posture data in the target coordinate system based on the three-dimensional motion data and the three-dimensional posture data includes: converting the X-axis angular velocity data G global_X and the Y-axis angular velocity data G global_Y into the vertical angular velocity data; performing time integration on the vertical angular velocity data based on times corresponding to a start position and an end position of the user during the exercise to obtain the vertical angle data; a step of setting the Z-axis angular velocity data G global_Z as the horizontal angular velocity data; performing time integration on the horizontal angular velocity data based on times corresponding to a start position and an end position of the user during the exercise to obtain the horizontal angle data; Including, 9. The movement data calibration method according to claim 8.

10. 1. An athletic data calibration system, comprising: at least one storage medium having stored thereon at least one instruction set for calibrating motion data; at least one processor communicatively coupled to the at least one storage medium; wherein, when the athletic data calibration system is in operation, the at least one processor reads the at least one instruction set and performs the athletic data calibration method of any one of claims 1 to 9. A movement data calibration system comprising:

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