Motion data calibration method and system

Abstract

The motion data calibration method and system provided in this application can transform user motion data from three-dimensional posture data in three mutually perpendicular coordinate axes into two-dimensional data in a target coordinate system, namely posture data in the horizontal plane and posture data in the vertical plane. This distinguishes user motion into horizontal and vertical movements, thus avoiding data discrepancies caused by different user orientations. The method and system can eliminate the influence of user orientation on motion data. Therefore, the method and system can calibrate motion data without requiring the user to perform calibration actions.

Description

Technical Field

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

[0002] With increasing public awareness of scientific exercise and physical health, motion monitoring equipment is experiencing rapid development. Currently, motion monitoring equipment primarily monitors user movement parameters through sensors. Existing motion monitoring devices require users to perform a series of calibration actions (such as raising both arms forward or laterally) before the device can convert the posture data from the sensor coordinate system to the human body coordinate system. Every time the user adjusts the position of the motion monitoring device, coordinate calibration needs to be repeated; otherwise, the calculation results will be affected, leading to a poor user experience.

[0003] Therefore, there is a need for a motion data calibration method and system that can calibrate motion data without requiring users to perform calibration actions. Summary of the Invention

[0004] This application provides a motion data calibration method and system that allows motion data to be calibrated without requiring the user to perform calibration actions.

[0005] In a first aspect, this application provides a motion data calibration method, comprising: acquiring motion data of a user during movement, the motion data including at least one posture signal corresponding to at least one measurement position on the user, each posture signal including three-dimensional posture data of its corresponding measurement position in the original coordinate system; constructing a target coordinate system, the target coordinate system including three mutually perpendicular coordinate axes of X-axis, Y-axis and Z-axis; and converting each posture signal into two-dimensional posture data in the target coordinate system.

[0006] In some embodiments, each attitude signal includes data measured by an attitude sensor, and the original coordinate system includes the coordinate system in which the attitude sensor is located.

[0007] In some embodiments, the attitude sensor includes at least one of an accelerometer, an angular velocity sensor, and a magnetic sensor.

[0008] In some embodiments, each attitude signal includes data measured by an image sensor, and the original coordinate system includes the coordinate system in which the image sensor is located.

[0009] In some embodiments, the three-dimensional attitude data includes angle data and angular velocity data on three mutually perpendicular coordinate axes.

[0010] In some embodiments, converting each attitude signal into two-dimensional attitude data in the target coordinate system includes: acquiring a pre-stored transformation relationship between the target coordinate system and the original coordinate system; based on the transformation relationship, converting each attitude signal into three-dimensional motion data in the target coordinate system, the three-dimensional motion data including at least angular velocity data on the X-axis, angular velocity data on the Y-axis, and angular velocity data on the Z-axis; and converting the three-dimensional motion data in the target coordinate system into the two-dimensional attitude data in the target coordinate system.

[0011] In some embodiments, the Z-axis of the target coordinate system is the opposite direction to the vertical direction of gravitational acceleration.

[0012] 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 any vertical plane perpendicular to the horizontal plane.

[0013] In some embodiments, converting the three-dimensional motion data in the target coordinate system into two-dimensional attitude data in the target coordinate system includes: converting the angular velocity data on the X-axis and the angular velocity data on the Y-axis into vertical angular velocity data using vector rules; performing time integration on the vertical angular velocity data based on the time corresponding to the start and end positions of the user's movement 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 the time corresponding to the start and end positions of the user's movement to obtain the horizontal angle data.

[0014] Secondly, this specification also provides a motion data calibration system, including at least one storage medium and at least one processor, wherein the at least one storage medium stores at least one instruction set for motion data calibration; the at least one processor is communicatively connected to the at least one storage medium, wherein when the motion data calibration system is running, the at least one processor reads the at least one instruction set and implements the motion data calibration method described in the first aspect of this specification.

[0015] As can be seen from the above technical solutions, the motion data calibration method and system provided in this application can transform the user's motion data from three-dimensional posture data in three mutually perpendicular coordinate axes into two-dimensional data in the target coordinate system, namely, posture data in the horizontal plane and posture data in the vertical plane. This distinguishes the user's motion into horizontal and vertical movements, thus avoiding data differences caused by different user orientations. The method and system can eliminate the influence of user orientation on motion data. Therefore, the method and system can calibrate motion data without requiring the user to perform calibration actions. Attached Figure Description

[0016] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0017] Figure 1 These are schematic diagrams illustrating application scenarios of a motion monitoring system according to some embodiments of this application;

[0018] Figure 2 These are schematic diagrams of exemplary hardware and / or software of wearable devices according to some embodiments of this application;

[0019] Figure 3 These are schematic diagrams of exemplary hardware and / or software of a computing device according to some embodiments of this application;

[0020] Figure 4 These are exemplary structural diagrams of wearable devices shown in some embodiments of this application;

[0021] Figure 5 This is an exemplary flowchart of a motion monitoring method according to some embodiments of this application;

[0022] Figure 6 This is an exemplary flowchart of a motion data calibration method according to some embodiments of this application;

[0023] Figure 7 This is a schematic diagram of the target coordinate system according to some embodiments of this application;

[0024] Figure 8 This is an exemplary flowchart illustrating the conversion of data into two-dimensional pose data according to some embodiments of this application; and

[0025] Figure 9 It is a coordinate system diagram of user movement according to some embodiments of this application. Detailed Implementation

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0027] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0028] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0029] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0030] This application provides a motion monitoring system that can acquire motion signals from a user's movements. These motion signals include at least electromyography (EMG) signals, posture signals, electrocardiogram (ECG) signals, and respiratory rate signals. The system can monitor the user's movements based at least on feature information corresponding to the EMG signals or the posture signals. For example, it can determine the user's movement type, number of movements, movement quality, movement time, or physiological parameters during the movement by using frequency and amplitude information corresponding to the EMG signals and angular velocity, angular velocity direction and value, angle, displacement information, and stress corresponding to the posture signals. In some embodiments, the motion monitoring system can also generate feedback on the user's fitness movements based on the analysis results to guide the user's exercise. For example, if the user's fitness movements are not standard, the motion monitoring system can issue prompts (e.g., voice prompts, vibration prompts, electrical stimulation, etc.) to the user. This motion monitoring system can be applied to wearable devices (e.g., clothing, wristbands, helmets), medical testing equipment (e.g., electromyography devices), fitness equipment, etc. By acquiring the user's motion signals during exercise, the system can accurately monitor and provide feedback on the user's movements without the need for professional personnel. This can improve the user's fitness efficiency while reducing the user's fitness costs.

[0031] Figure 1 These are schematic diagrams illustrating application scenarios of a motion monitoring system according to some embodiments of this application. For example... Figure 1 As shown, the motion 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, the motion monitoring system 100 may further include a motion data calibration system 180. The motion monitoring system 100 can acquire motion signals (e.g., electromyographic signals, posture signals, electrocardiogram signals, respiratory rate signals, etc.) to characterize the user's motion movements and monitor and provide feedback on the user's movements based on the motion signals.

[0032] For example, the motion monitoring system 100 can monitor and provide feedback on a user's movements during exercise. When a user wears the wearable device 130 to exercise, the wearable device 130 can acquire the user's movement signals. The processing device 110 or the mobile terminal device 140 can receive and analyze the user's movement signals to determine whether the user's exercise movements are standardized, thereby monitoring the user's movements. Specifically, monitoring the user's movements may include determining the type, number, quality, and duration of the movements, or physiological parameter information of the user when performing the movements. Furthermore, the motion monitoring system 100 can generate feedback on the user's exercise movements based on the analysis results to guide the user's exercise.

[0033] For example, the motion monitoring system 100 can monitor and provide feedback on a user's running movements. For instance, when a user wears the wearable device 130 for running, the motion monitoring system 100 can monitor whether the user's running form is correct and whether the running time meets health standards. If the user runs for too long or their running form is incorrect, the fitness device can provide feedback on their exercise status to prompt the user to adjust their running form or running time.

[0034] In some embodiments, the processing device 110 can be used to process information and / or data related to user movement. For example, the processing device 110 can receive user movement signals (e.g., electromyography signals, posture signals, electrocardiogram signals, respiratory rate signals, etc.) and further extract feature information corresponding to the movement signals (e.g., feature information corresponding to the electromyography signals and the posture signals in the movement signals). In some embodiments, the processing device 110 can perform specific signal processing on the electromyography signals or posture signals collected by the wearable device 130, such as signal segmentation, signal preprocessing (e.g., signal correction processing, filtering processing, etc.). In some embodiments, the processing device 110 can also determine whether the user's movement is correct based on the user's movement signals. For example, the processing device 110 can determine whether the user's movement is correct based on the feature information corresponding to the electromyography signals (e.g., amplitude information, frequency information, etc.). As another example, the processing device 110 can determine whether the user's movement is correct based on the feature information corresponding to the posture signals (e.g., angular velocity, angular velocity direction, angular velocity acceleration, angle, displacement information, stress, etc.). For example, the processing device 110 can determine whether the user's movements are correct based on the feature information corresponding to the electromyographic signals and the feature information corresponding to the posture signals. In some embodiments, the processing device 110 can also determine whether the user's physiological parameter information during exercise meets health standards. In some embodiments, the processing device 110 can also issue corresponding instructions to provide feedback on the user's exercise status. For example, when a user is running, if the motion monitoring system 100 detects that the user's running time is too long, the processing device 110 can issue an instruction to the mobile terminal device 140 to prompt the user to adjust the running time. It should be noted that the feature information corresponding to the posture signals is not limited to the aforementioned angular velocity, angular velocity direction, angular velocity acceleration, angle, displacement information, stress, etc., but can also be other feature information. Any parameter information that can be used to reflect the relative movement of the user's body can be the feature information corresponding to the posture signals. For example, when the posture sensor is a strain gauge sensor, by measuring the magnitude of the resistance in the strain gauge sensor that changes with the stretching length, the bending angle and bending direction at the user's joint can be obtained.

[0035] In some embodiments, the processing device 110 may be local or remote. For example, the processing device 110 may access information and / or data stored in the wearable device 130 and / or the mobile terminal device 140 via a network 120. In some embodiments, the processing device 110 may be directly connected to the wearable device 130 and / or the mobile terminal device 140 to access the information and / or data stored therein. For example, the processing device 110 may be located in the wearable device 130 and interact with the mobile terminal device 140 via the network 120. As another example, the processing device 110 may be located in the mobile terminal device 140 and interact with the wearable device 130 via a network. In some embodiments, the processing device 110 may run on a cloud platform.

[0036] In some embodiments, the processing device 110 can process data and / or information related to motion monitoring to perform one or more functions described in this application. In some embodiments, the processing device 110 can acquire motion signals collected by the wearable device 130 during user movement. In some embodiments, the processing device 110 can send control commands to the wearable device 130 or the mobile terminal device 140. The control commands can control the on / off states of the wearable device 130 and its sensors, and can also control the mobile terminal device 140 to issue prompt messages. In some embodiments, the processing device 110 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-core processing device).

[0037] Network 120 facilitates the exchange of data and / or information within the motion monitoring system 100. In some embodiments, one or more components of the motion monitoring system 100 can send data and / or information to other components within the motion monitoring system 100 via network 120. For example, motion signals collected by wearable device 130 can be transmitted to processing device 110 via network 120. As another example, confirmation results regarding motion signals in processing device 110 can be transmitted to mobile terminal device 140 via network 120. In some embodiments, network 120 can be any type of wired or wireless network.

[0038] Wearable device 130 refers to clothing or equipment with wearable functions. In some embodiments, wearable device 130 may include, but is not limited to, top 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. Sensors can acquire various motion signals (e.g., electromyography signals, posture signals, temperature information, heart rate, electrocardiogram signals, etc.) during user movement. In some embodiments, sensors may include, but are not limited to, one or more of the following: electromyography sensors, posture sensors, temperature sensors, humidity sensors, electrocardiogram sensors, blood oxygen saturation sensors, Hall effect sensors, skin conductance sensors, rotation sensors, etc. For example, an electromyography sensor may be placed at a muscle location (e.g., biceps brachii, triceps brachii, latissimus dorsi, trapezius, etc.) in top device 130-1, and the electromyography sensor may be in contact with the user's skin to acquire electromyography signals during user movement. As another example, an electrocardiogram sensor may be placed near the left pectoral muscle in top device 130-1, and the electrocardiogram sensor may acquire the user's electrocardiogram signals. For example, posture sensors can be installed at the locations of human muscles (e.g., gluteus maximus, vastus lateralis, vastus medialis, gastrocnemius, etc.) in the pants device 130-2, and these posture sensors can collect the user's posture signals. In some embodiments, the wearable device 130 can also provide feedback on the user's movements. For example, if the movement of a certain part of the user's body does not conform to the standard during exercise, the electromyography sensor corresponding to that part can generate a stimulation signal (e.g., electrical stimulation or a striking signal) to remind the user.

[0039] It should be noted that wearable devices 130 are not limited to Figure 1 The garment device 130-1, trouser device 130-2, wristband device 130-3, and shoe device 130-4 shown may also include other devices that require motion monitoring, such as helmet devices, knee pad devices, etc., without limitation. Any device that can use the motion monitoring method contained in this application is within the protection scope of this application.

[0040] In some embodiments, the mobile terminal device 140 can acquire information or data from the motion monitoring system 100. In some embodiments, the mobile terminal device 140 can receive motion data processed by the processing device 110 and provide feedback such as motion records based on the processed motion data. Exemplary feedback methods may include, but are not limited to, voice prompts, image prompts, video displays, and text prompts. In some embodiments, a user can acquire motion records during their own exercise through the mobile terminal device 140. For example, the mobile terminal device 140 can be connected to the wearable device 130 via a network 120 (e.g., wired connection, wireless connection), and the user can acquire motion records during their exercise through the mobile terminal device 140, which can be transmitted to the processing device 110 through 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, etc. In some embodiments, the 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, smart home devices may include control devices for smart appliances, smart monitoring devices, smart TVs, smart cameras, etc., or any combination thereof. In some embodiments, smart mobile devices may include smartphones, personal digital assistants (PDAs), gaming devices, navigation devices, POS devices, etc., or any combination thereof. In some embodiments, virtual reality devices and / or augmented reality devices may include virtual reality helmets, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, etc., or any combination thereof.

[0041] In some embodiments, the motion monitoring system 100 may further include a motion data calibration system 180. The motion data calibration system 180 can process motion data related to user movement and can execute the motion data calibration method described in this specification. Specifically, the motion data calibration system 180 can receive motion data from the user during movement and can convert the motion data from three-dimensional posture data in three mutually perpendicular coordinate axes into two-dimensional posture data in a target coordinate system, i.e., posture data in the horizontal plane and posture data in the vertical plane, thereby dividing the user's movement into horizontal and vertical movements, avoiding data differences caused by different user orientations. The motion data calibration system 180 can remove the influence of user orientation on the motion data, calibrating the motion data without requiring the user to perform calibration actions. The motion data may be three-dimensional posture data of the measurement position on the user during movement. The motion data and the motion data calibration method will be described in detail later.

[0042] In some embodiments, the motion data calibration system 180 may be integrated into the processing device 110. In some embodiments, the motion data calibration system 180 may also be integrated into the mobile terminal device 140. In some embodiments, the motion data calibration system 180 may also exist independently of the processing device 110 and the mobile terminal device 140. The motion data calibration system 180 may be communicatively connected to the processing device 110, the wearable device 130, and the mobile terminal device 140 for the transmission and exchange of information and / or data. In some embodiments, the motion data calibration system 180 may access information and / or data stored in the processing device 110, the wearable device 130, and / or the mobile terminal device 140 via the network 120. In some embodiments, the wearable device 130 may be directly connected to the processing device 110 and / or the mobile terminal device 140 to access the information and / or data stored therein. For example, the motion data calibration system 180 may be located in the processing device 110 and may interact with the wearable device 130 and the mobile terminal device 140 via the network 120. For example, the motion data calibration system 180 may be located in the mobile terminal device 140 and interact with the processing device 110 and the wearable device 130 via a network. In some embodiments, the motion data calibration system 180 may be executed on a cloud platform and interact with the processing device 110, the wearable device 130, and the mobile terminal device 140 via a network.

[0043] For ease of demonstration, the following description will use the example of the motion data calibration system 180 being located in the processing device 110.

[0044] In some embodiments, the motion monitoring system 100 may further include a database. The database may store data (e.g., initially set threshold conditions, etc.) and / or instructions (e.g., feedback instructions). In some embodiments, the database may store data acquired from wearable device 130 and / or mobile terminal device 140. In some embodiments, the database may store information and / or instructions for processing device 110 to execute or use, to perform the exemplary methods described herein. In some embodiments, the database may be connected to network 120 to communicate with one or more components of the motion monitoring system 100 (e.g., processing device 110, wearable device 130, mobile terminal device 140, etc.). One or more components of the motion monitoring system 100 may access the data or instructions stored in the database via network 120. In some embodiments, the database may be directly connected to or communicate with one or more components of the motion monitoring system 100. In some embodiments, the database may be part of processing device 110.

[0045] Figure 2These are schematic diagrams illustrating exemplary hardware and / or software of a wearable device 130 according to some embodiments of this application. Figure 2 As shown, 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.

[0046] The acquisition module 210 can be used to acquire motion signals during user movement. In some embodiments, the acquisition module 210 may include a sensor unit, which can be used to acquire one or more motion signals during user movement. In some embodiments, the sensor unit may include, but is not limited to, one or more of the following: electromyography (EMG) sensors, posture sensors, electrocardiogram (ECG) sensors, respiration sensors, temperature sensors, humidity sensors, inertial sensors, blood oxygen saturation sensors, Hall effect sensors, electrodermal sensors, rotation sensors, etc. In some embodiments, motion signals may include one or more of the following: EMG signals, posture signals, ECG signals, respiratory rate, temperature signals, humidity signals, etc. The sensor unit can be placed at different locations on the wearable device 130 according to the type of motion signal to be acquired. For example, in some embodiments, an EMG sensor (also referred to as an electrode element) can be placed at the location of human muscles, and the EMG sensor can be configured to acquire EMG signals during user movement. The EMG signals and their corresponding characteristic information (e.g., frequency information, amplitude information, etc.) can reflect the state of the muscles during user movement. A posture sensor can be placed at different locations on the human body (e.g., locations in the wearable device 130 corresponding to the torso, limbs, and joints), and the posture sensor can be configured to acquire posture signals during user movement. Attitude signals and their 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 movement. An electrocardiogram (ECG) sensor can be positioned around the chest and configured to collect ECG data during user movement. A respiration sensor can also be positioned around the chest and configured to collect respiratory data (e.g., respiratory rate, respiratory amplitude, etc.) during user movement. A temperature sensor can be configured to collect temperature data (e.g., body surface temperature) during user movement. A humidity sensor can be configured to collect humidity data of the external environment during user movement.

[0047] Processing module 220 can process data from acquisition module 210, control module 230, communication module 240, power supply module 250, and / or input / output module 260. For example, processing module 220 can process motion signals from the user's movement process obtained from acquisition module 210. In some embodiments, processing module 220 can preprocess the motion signals (e.g., electromyographic signals, posture signals) acquired by acquisition module 210. For example, processing module 220 can segment the electromyographic signals or posture signals during user movement. Another example is that processing module 220 can preprocess the electromyographic signals during user movement (e.g., filtering, signal correction) to improve the quality of the electromyographic signals. Yet another example is that processing module 220 can determine feature information corresponding to the posture signals based on the user's posture signals during movement. In some embodiments, processing module 220 can process instructions or operations from input / output module 260. In some embodiments, the processed data can be stored in a memory or hard disk. In some embodiments, the processing module 220 can transmit its processed data to one or more components of the motion monitoring system 100 via the communication module 240 or the network 120. For example, the processing module 220 can send the monitoring results of the user's movement to the control module 230, and the control module 230 can execute subsequent operations or instructions based on the results of the action determination.

[0048] The control module 230 can be connected to other modules in the wearable device 130. In some embodiments, the control module 230 can control the operating state of other modules in the wearable device 130. For example, the control module 230 can control the power supply state (e.g., normal mode, power saving mode), power supply time, etc., of the power supply module 250. For another example, the control module 230 can determine the input / output module 260 based on the user's actions, and then control the mobile terminal device 140 to send feedback results of the user's movement. When a problem occurs in the user's movement (e.g., the movement does not meet the standard), the control module 230 can control the input / output module 260, and then control the mobile terminal device 140 to provide feedback to the user, allowing the user to understand their own movement status in real time and adjust their movements. In some embodiments, the control module 230 can also control one or more sensors or other modules in the acquisition module 210 to provide feedback to the human body. For example, when a muscle exerts excessive force during the user's movement, the control module 230 can control the electrode module at that muscle location to provide electrical stimulation to the user to prompt the user to adjust their movements in time.

[0049] In some embodiments, the communication module 240 can be used for information or data exchange. In some embodiments, the communication module 240 can be used for communication between internal components of the wearable device 130. For example, the acquisition module 210 can send user motion signals (e.g., electromyographic signals, posture signals, etc.) to the communication module 240, and the communication module 240 can send the motion signals to the processing module 220. In some embodiments, the communication module 240 can also be used for communication between the wearable device 130 and other components in the motion monitoring system 100. For example, the communication module 240 can send the status information of the wearable device 130 (e.g., on / off status) to the processing device 110, and the processing device 110 can monitor the wearable device 130 based on the status information. The communication module 240 can employ wired, wireless, or wired / wireless hybrid technologies.

[0050] In some embodiments, the power supply module 250 can provide power to other components in the motion monitoring system 100.

[0051] The input / output module 260 can acquire, transmit, and send signals. The input / output module 260 can connect to or communicate with other components in the motion monitoring system 100. Other components in the motion monitoring system 100 can connect to or communicate with the input / output module 260.

[0052] It should be noted that the above description of the motion monitoring system 100 and its modules is for convenience only and should not be construed as limiting the one or more embodiments of this application to the scope of the described embodiments. It is understood that those skilled in the art, after understanding the principle of the system, may arbitrarily combine the various modules without departing from this principle, or form a subsystem connected to other modules, or omit one or more modules. For example, the acquisition module 210 and the processing module 220 can be a single module, which can have the function of acquiring and processing user motion signals. As another example, the processing module 220 may not be located in the wearable device 130, but integrated into the processing device 110. Such modifications are all within the protection scope of one or more embodiments of this application.

[0053] Figure 3 This is a schematic diagram of exemplary hardware and / or software of a computing device 300 according to some embodiments of this application. In some embodiments, processing device 110 and / or mobile terminal device 140 may be implemented on computing device 300. In some embodiments, motion data calibration system 180 may be implemented on computing device 300. Figure 3As shown, the 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.

[0054] The internal communication bus 310 enables data communication between components within the computing device 300. For example, at least one processor 320 can transmit data to at least one storage medium or other hardware such as an input / output port 360 via the internal communication bus 310. In some embodiments, the internal communication bus 310 can be an Industry Standard (ISA) bus, an Extended Industry Standard (EISA) bus, a Video Electronics Standard (VESA) bus, an External Component Interconnect (PCI) bus, etc. In some embodiments, the internal communication bus 310 can be used for connecting... Figure 1 The various modules in the motion monitoring system 100 shown (e.g., acquisition module 210, processing module 220, control module 230, communication module 240, input / output module 260).

[0055] 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 temporary storage medium. For example, the data storage device may include one or more of a read-only memory (ROM) 330, random access memory (RAM) 340, and a hard disk 370. Exemplary ROMs may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), optical disc ROM (CD-ROM), and digital universal 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 capacitance (Z-RAM), etc. The storage medium may store data / information acquired from any other component of the motion monitoring system 100. The storage medium also includes at least one set of instructions stored in the data storage device. The instructions are computer program code, which may include programs, routines, objects, components, data structures, processes, modules, etc., that execute the motion data calibration methods provided in this specification. In some embodiments, the storage medium of the computing device 300 may be located in the wearable device 130 or in the processing device 110.

[0056] At least one processor 320 can be communicatively connected to at least one storage medium. The at least one processor 320 is used to execute the at least one instruction set described above. When the computing device 300 is running, the at least one processor 320 can read the at least one instruction set and execute computational instructions (program code) according to the instructions of the at least one instruction set, thereby performing the functions of the motion monitoring system 100 described in this application. The processor 320 can execute all the steps included in the data processing method. The computational instructions may include programs, objects, components, data structures, processes, modules, and functions (the functions refer to the specific functions described in this application). For example, the processor 320 can process motion signals (e.g., electromyographic signals, posture signals) acquired from the wearable device 130 and / or mobile terminal device 140 of the motion monitoring system 100 during user movement, and monitor the user's movement based on the motion signals during user movement. For example, processor 320 can be used to process motion data of a user during movement acquired from wearable device 130 and / or mobile terminal device 140 of motion monitoring system 100, and execute the motion data calibration method described herein according to the instructions of the at least one instruction set, converting the motion data into two-dimensional posture data. In some embodiments, processor 320 may include a microcontroller, microprocessor, reduced instruction set computer (RISC), application-specific integrated circuit (ASIC), application-specific instruction set processor (ASIP), central processing unit (CPU), graphics processing unit (GPU), physical processing unit (PPU), microcontroller unit, digital signal processor (DSP), field-programmable gate array (FPGA), advanced reduced instruction set computer (ARM), programmable logic device, and any circuit and processor capable of performing one or more functions, or any combination thereof. Figure 3 The computing device 300 described in this application only includes one processor 320, but it should be noted that the computing device 300 in this application may also include multiple processors 320.

[0057] The hard disk 370 can be used to store information and data generated by or received from the processing device 110. For example, the hard disk 370 can store user confirmation information. In some embodiments, the hard disk 370 can be located in the processing device 110 or in the wearable device 130.

[0058] The user interface 380 enables interaction and information exchange between the computing device 300 and the user. In some embodiments, the user interface 380 can be used to present motion records generated by the motion monitoring system 100 to the user. In some embodiments, the 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.

[0059] The input / output interface 360 ​​can be used to input or output signals, data, or information. In some embodiments, the input / output interface 360 ​​allows a user to interact with the motion monitoring system 100.

[0060] Figure 4 These are exemplary structural diagrams of a wearable device according to some embodiments of this application. For further description of the wearable device, clothing is used as an example. Figure 4As shown, the wearable device 400 may include clothing 410. Clothing 410 may include a clothing base 4110, at least one clothing processing module 4120, at least one clothing feedback module 4130, at least one clothing acquisition module 4140, etc. The clothing base 4110 can refer to clothing worn on the upper body. In some embodiments, the clothing base 4110 may include a short-sleeved T-shirt, a long-sleeved T-shirt, a shirt, a jacket, etc. At least one clothing processing module 4120 and at least one clothing acquisition module 4140 may be located on the clothing base 4110 in areas that fit against different parts of the body. At least one clothing feedback module 4130 may be located at any position on the clothing base 4110, and at least one clothing feedback module 4130 may be configured to provide feedback on the user's upper body movement status. Exemplary feedback methods may include, but are not limited to, voice prompts, text prompts, pressure prompts, electrical stimulation, etc. In some embodiments, at least one garment acquisition module 4140 may include, but is not limited to, one or more of the following: a posture sensor, an electrocardiogram (ECG) sensor, an electromyography (EMG) sensor, a temperature sensor, a humidity sensor, an inertial sensor, an acid-base sensor, and an acoustic transducer. The sensors in the garment acquisition module 4140 can be placed at different positions on the user's body depending on the signal to be measured. For example, when a posture sensor is used to acquire posture signals during user movement, the posture sensor can be placed in the garment base 4110 at positions corresponding to the human torso, arms, and joints. As another example, when an EMG sensor is used to acquire EMG signals during user movement, the EMG sensor can be located near the muscles to be measured. In some embodiments, the posture sensor may include, but is not limited to, a triaxial acceleration sensor, a triaxial angular velocity sensor, a magnetic sensor, or any combination thereof. For example, a posture sensor may include a triaxial acceleration sensor and a triaxial angular velocity sensor. In some embodiments, the posture sensor may also include a strain gauge sensor. A strain gauge sensor can refer to a sensor that can be based on the strain generated by the deformation of the object under force. In some embodiments, the strain gauge sensor may include, but is not limited to, one or more of strain gauge force sensors, strain gauge pressure sensors, strain gauge torque sensors, strain gauge displacement sensors, and strain gauge acceleration sensors. For example, the strain gauge sensor can be placed at the user's joint position, and by measuring the magnitude of the resistance in the strain gauge sensor that changes with the tensile length, the bending angle and bending direction at the user's joint can be obtained. It should be noted that, in addition to the aforementioned garment base 4110, garment processing module 4120, garment feedback module 4130, and garment acquisition module 4140, the garment 410 may also include other modules, such as a power supply module, a communication module, and an input / output module. The garment processing module 4120 and... Figure 2 The processing module 220 is similar to the garment acquisition module 4140. Figure 2Similar to the acquisition module 210 in the above, for a detailed description of each module in the upper garment 410, please refer to this application. Figure 2 The relevant descriptions in the text will not be repeated here.

[0061] Figure 5 This is an exemplary flowchart of a motion monitoring method according to some embodiments of this application. Figure 5 As shown, process 500 may include:

[0062] In step 510, the motion signal of the user during movement is acquired.

[0063] In some embodiments, step 510 can be performed by the acquisition module 210. Action signals refer to human body parameter information during user movement. In some embodiments, human body parameter information may include, but is not limited to, one or more of electromyography (EMG) signals, posture signals, electrocardiogram (ECG) signals, temperature signals, humidity signals, blood oxygen concentration, and respiratory rate. In some embodiments, the EMG sensor in the acquisition module 210 can collect the user's EMG signals during movement. For example, when a user performs a seated chest press, the EMG sensor in the wearable device corresponding to the pectoral muscles, latissimus dorsi, etc., can collect the EMG signals of the corresponding muscle positions. As another example, when a user performs a squat, the EMG sensor in the wearable device corresponding to the gluteus maximus, quadriceps, etc., can collect the EMG signals of the corresponding muscle positions. Yet another example, when a user performs running, the EMG sensor in the wearable device corresponding to the gastrocnemius, etc., can collect the EMG signals of the gastrocnemius, etc. In some embodiments, the posture sensor in the acquisition module 210 can collect the user's posture signals during movement. For example, when a user performs a barbell bench press, the posture sensor in the wearable device, corresponding to the triceps and other muscles, can collect posture signals from these areas. Similarly, when a user performs dumbbell flyes, a posture sensor positioned at the deltoid and other muscles can collect posture signals from these areas. In some embodiments, the acquisition module 210 can have multiple posture sensors, which can acquire posture signals from multiple body parts during movement. These posture signals can reflect the relative motion between different parts of the body. For example, posture signals from the arm and torso can reflect the arm's motion relative to the torso. In some embodiments, the posture signal is associated with the type of posture sensor. For example, when the posture sensor is an angular velocity triaxial sensor, the acquired posture signal is angular velocity information. Similarly, when the posture sensor is both an angular velocity triaxial sensor and an acceleration triaxial sensor, the acquired posture signal is both angular velocity and acceleration information. For example, when the attitude sensor is a strain gauge sensor, it can be placed at the user's joint position. By measuring the resistance of the strain gauge sensor as it changes with the stretching length, the acquired attitude signal can be displacement information, stress, etc. These attitude signals can characterize the bending angle and direction at the user's joint. It is important to note that any parameter information that can reflect the relative motion of the user's body can be considered the characteristic information corresponding to the attitude signal. Different types of attitude sensors can be used to acquire this characteristic information depending on its type.

[0064] In some embodiments, the motion signal may include electromyographic (EMG) signals from a specific part of the user's body and postural signals from that specific part. EMG and postural signals can reflect the movement state of a specific part of the user's body from different perspectives. Simply put, the postural signal of a specific part of the user's body can reflect the type, amplitude, and frequency of movement of that specific part. EMG signals can reflect the muscle state of that specific part during movement. In some embodiments, by using EMG signals and / or postural signals from the same body part, it is possible to better assess whether the movement of that part is correct.

[0065] In step 520, the user's movements are monitored based at least on the feature information corresponding to the electromyographic signal or the feature information corresponding to the posture signal.

[0066] In some embodiments, this step may be performed by processing module 220 and / or processing device 110. In some embodiments, the characteristic information corresponding to the electromyographic signal may include, but is not limited to, one or more of frequency information, amplitude information, etc. The characteristic information corresponding to the posture signal refers to parameter information used to characterize the relative movement of the user's body. In some embodiments, the characteristic information corresponding to the posture signal may include, but is not limited to, one or more of angular velocity direction, angular velocity value, angular velocity acceleration value, etc. In some embodiments, the characteristic information corresponding to the posture signal may also include angle, displacement information (e.g., tensile length in a strain gauge sensor), stress, etc. For example, when the posture sensor is a strain gauge sensor, the strain gauge sensor can be set at the user's joint position. By measuring the magnitude of the resistance in the strain gauge sensor that changes with the tensile length, the obtained posture signal can be displacement information, stress, etc., and these posture signals can characterize the bending angle and bending direction at the user's joint. In some embodiments, the processing module 220 and / or the processing device 110 can extract feature information corresponding to electromyographic signals (e.g., frequency information, amplitude information) or feature information corresponding to posture signals (e.g., angular velocity direction, angular velocity value, angular velocity acceleration value, angle, displacement information, stress, etc.), and monitor the user's movements based on the feature information corresponding to the electromyographic signals or the feature information corresponding to the posture signals. Monitoring the user's movements here includes monitoring information related to the user's movements. In some embodiments, the movement-related information may include one or more of the following: user movement type, number of movements, movement quality (e.g., whether the user's movements meet standards), and movement time. Movement type refers to the fitness movements performed by the user during exercise. In some embodiments, movement type may include, but is not limited to, one or more of the following: seated chest press, squat, deadlift, plank, running, swimming, etc. Number of movements refers to the number of times the user performs the movement during exercise. For example, if the user performs 10 seated chest presses during exercise, 10 repetitions represent the number of repetitions. Movement quality refers to the standardization of the user's performed fitness movements relative to standard fitness movements. For example, when a user performs a squat, the processing device 110 can determine the type of the user's movement based on the feature information corresponding to the movement signals (electromyographic signals and postural signals) of specific muscle locations (gluteus maximus, quadriceps, etc.), and determine the quality of the user's squat based on the movement signals of a standard squat. Movement time refers to the time corresponding to one or more movement types or the total time of the movement process.

[0067] As mentioned earlier, the characteristic information corresponding to the posture signal can include parameter information that can be used to reflect the relative motion of the user's body. To monitor the user's movements, the motion monitoring system 100 needs to acquire the relative motion between different parts of the user's body. As previously mentioned, the posture signal can be posture data measured by posture sensors. Posture sensors can be distributed across multiple different parts of the user's body. To acquire the relative motion between multiple different parts of the user's body, the motion monitoring system 100 can calibrate the posture signals between multiple different parts of the user's body.

[0068] Figure 6 This is an exemplary flowchart of a motion data calibration method 3000 according to some embodiments of this application. As previously described, the motion data calibration system 180 can execute the motion data calibration method 3000 provided in this application. Specifically, the motion data calibration system 180 can execute the motion data calibration method 3000 on the processing device 110 or on the mobile terminal device 140. For ease of demonstration, we will describe the motion data calibration method 3000 executed by the motion data calibration system 180 on the processing device 110 as an example. Specifically, the processor 320 in the processing device 110 can read the instruction set stored in its local storage medium and then execute the motion data calibration method 3000 provided in this application according to the provisions of the instruction set. The motion data calibration method 3000 may include:

[0069] S3020: Acquire motion data of the user during movement.

[0070] In some embodiments, this step may be performed by processing module 220 and / or processing device 110. Motion data refers to human motion parameter information of a user during movement. In some embodiments, motion data may include at least one posture signal corresponding to at least one measurement location on the user's body. The posture signal and its corresponding feature 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 movement. At least one measurement location corresponds one-to-one with at least one posture signal. The measurement location may be different parts of the user's body. At least one posture signal corresponds to the actual posture of at least one measurement location on the user's body during movement. Each of the at least one posture signal may include three-dimensional posture data of its corresponding measurement location in the original coordinate system. The original coordinate system may be the coordinate system in which the posture signal is located. When the user's posture does not change, changes in the original coordinate system may also lead to changes 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, etc.

[0071] In some embodiments, the posture signal can be acquired by a posture sensor on the wearable device 130. As previously described, 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 located at at least one measurement position on the user's body. The posture sensor can acquire posture signals from the user's body at the corresponding measurement position. The posture sensors on the wearable device 130 may be distributed on the limbs (e.g., arms, legs, etc.), the torso (e.g., chest, abdomen, back, waist, etc.), and the head. The posture sensor can acquire posture signals from the limbs, torso, and other parts of the body. The posture sensor can be placed at different positions on the wearable device 130 according to the posture signal to be acquired, to measure the posture signal corresponding to different positions of the human body. In some embodiments, the posture sensor may also be a sensor of an attitude measurement unit (AHRS) with a posture fusion algorithm. Attitude fusion algorithms can fuse data from a nine-axis inertial measurement unit (IMU) equipped with a three-axis accelerometer, a three-axis angular velocity sensor, and a three-axis magnetometer into Euler angles or quaternions to obtain attitude signals of the user's body parts where the attitude sensor is located. In some embodiments, processing module 220 and / or processing device 110 can determine characteristic information corresponding to the attitude based on the attitude signal. In some embodiments, the characteristic information corresponding to the attitude signal may include, but is not limited to, angular velocity value, angular velocity direction, and angular velocity acceleration value. In some embodiments, the attitude sensor may be a strain sensor, which can acquire the bending direction and bending angle at the user's joints, thereby acquiring the user's attitude signal during movement. For example, the strain sensor may be located at the user's knee joint. When the user moves, the user's body parts act on the strain sensor, and the bending direction and bending angle at the user's knee joint can be calculated based on the resistance or length changes of the strain sensor, thereby acquiring the user's leg attitude signal. In some embodiments, the attitude sensor may also include an optical fiber sensor, and the attitude signal may be characterized by the change in the direction of the bent light from the optical fiber sensor. In some embodiments, the attitude sensor may also be a magnetic flux sensor, and the attitude signal may be characterized by the change in magnetic flux. It should be noted that the type of attitude sensor is not limited to the sensors mentioned above, but can also be other sensors. Sensors capable of acquiring user attitude signals are all within the scope of the attitude sensor in this application.

[0072] As previously described, each attitude signal may include one or more three-dimensional attitude data. The attitude sensor may also include various types of sensors. In some embodiments, the attitude sensor may include at least one of an accelerometer, an angular velocity sensor, and a magnetometer.

[0073] When the attitude signal is data measured by an attitude sensor, the original coordinate system can be the coordinate system in which the attitude sensor is located. In some embodiments, the original coordinate system refers to the coordinate system corresponding to the attitude sensor mounted on the human body. When a user uses the wearable device 130, the attitude sensors on the wearable device 130 are distributed in different parts of the human body, resulting in different installation angles for each attitude sensor on the human body. Since the attitude sensors in different parts of the body use their own coordinate system as the original coordinate system, the attitude sensors in different parts of the body have different original coordinate systems. In some embodiments, the attitude signal acquired by each attitude sensor can be expressed in its corresponding original coordinate system. The attitude signal acquired by the attitude sensor can be an attitude signal in the original coordinate system within a preset fixed coordinate system. The preset fixed coordinate system can be the geodetic coordinate system or any other preset coordinate system. The motion data calibration system 180 can pre-store the transformation relationship between the original coordinate system and the preset fixed coordinate system.

[0074] In some embodiments, the attitude signal can be a signal directly acquired by the attitude sensor, or an attitude signal formed by processing the signal directly acquired by the attitude sensor through conventional filtering, rectification, wavelet transform, glitches removal, or any combination of one or more of the above processing procedures.

[0075] In some embodiments, the attitude signal can be data measured by an image sensor. The image sensor can be an image sensor capable of acquiring depth information, such as a 3D structured light camera, a binocular camera, etc. The image sensor can be mounted at any location capable of capturing images of the user's movement. There can be one or more image sensors. When there are multiple image sensors, they can be mounted at multiple different locations. The image sensor can acquire depth images as the user moves. The depth image can contain depth information of at least one measurement location on the user relative to the coordinate system of the image sensor. When the user moves, the motion data calibration system 180 can calculate the attitude signal for each measurement location in at least one measurement location based on changes in multiple frames of depth images. As previously described, each attitude signal can include one or more three-dimensional attitude data. The motion data calibration system 180 can calculate different three-dimensional attitude data.

[0076] When the attitude signal is data measured by an image sensor, the original coordinate system can be the coordinate system in which the image sensor itself resides. In some embodiments, the attitude signal acquired by the image sensor can be expressed in its corresponding image sensor's own coordinate system (original coordinate system). The image sensor can be pre-calibrated. That is, the motion data calibration system 180 can pre-store the transformation relationship between the image sensor's own coordinate system and the aforementioned preset fixed coordinate system.

[0077] For ease of demonstration, the following description will assume that the motion data is obtained from measurements by at least one attitude sensor. For clarity, we define the original coordinate system as the o-xyz coordinate system. Here, o is the origin of the original coordinate system o-xyz, and the x-axis, y-axis, and z-axis are the three mutually perpendicular axes of the original coordinate system o-xyz.

[0078] As mentioned earlier, each attitude signal can be three-dimensional attitude data of its corresponding measurement position in the original coordinate system o-xyz. In some embodiments, the three-dimensional attitude data can be attitude data on three mutually perpendicular coordinate axes in its coordinate system. In some embodiments, the attitude data can include angle data and angular velocity data. In some embodiments, the three-dimensional attitude data in the original coordinate system o-xyz can include angle data and angular velocity data on three mutually perpendicular coordinate axes: the x-axis, y-axis, and z-axis. For ease of illustration, we label the three-dimensional attitude data of each attitude signal in the original coordinate system o-xyz as three-dimensional angle (Euler) data E. sens and three-dimensional angular velocity (Gyro) data G sens 3D angle data E sens This can include angle data E on the x-axis sens_x , y-axis angle data E sens_y and the angle data E on the z-axis sens_z Three-dimensional angular velocity data G sens This can include angular velocity data G on the x-axis. sens_x Angular velocity data G on the y-axis sens_y and the angular velocity data G on the z-axis sens_z .

[0079] S3040: Construct the target coordinate system.

[0080] In some embodiments, this step may be performed by processing module 220 and / or processing device 110. For ease of illustration, we define the target coordinate system as O-XYZ. To facilitate the determination of the relative motion between different parts of the user's body, motion data calibration system 180 can convert 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 perpendicular coordinate axes: the X-axis, the Y-axis, and the Z-axis.

[0081] In some embodiments, the target coordinate system O-XYZ can be any calibrated coordinate system. In some embodiments, the target coordinate system O-XYZ can be the aforementioned preset fixed coordinate system. In some embodiments, the target coordinate system O-XYZ and the aforementioned preset fixed coordinate system can be different coordinate systems. The motion data calibration system 180 can pre-store the transformation relationship between the aforementioned preset fixed coordinate system and the target coordinate system O-XYZ.

[0082] The motion data calibration system 180 is used to calibrate the motion data of a user during movement, and the measurement object is the user. Therefore, in some embodiments, the target coordinate system O-XYZ can be defined with the length direction of the torso when the human body is standing as the Z-axis. That is, the Z-axis is the opposite direction to the vertical direction of gravitational acceleration. In other words, the Z-axis is a coordinate axis perpendicular to the ground and pointing towards the sky. The plane formed by the X-axis and Y-axis is a horizontal plane perpendicular to the Z-axis. In some embodiments, the X-axis and Y-axis can be any two mutually perpendicular coordinate axes within the horizontal plane perpendicular to the Z-axis. In some embodiments, the X-axis can be an east-west oriented coordinate, such as a coordinate axis pointing east, and the Y-axis can be a north-south oriented coordinate, such as a coordinate axis pointing north. Figure 7 A schematic diagram of the target coordinate system O-XYZ provided according to an embodiment of this specification is shown. The X-axis direction is exactly in front of user 001 when standing.

[0083] S3060: Convert each attitude signal into two-dimensional attitude data in the target coordinate system.

[0084] Figure 8 This is an exemplary flowchart illustrating the conversion of data into two-dimensional pose data according to some embodiments of this application. Figure 8 This corresponds to step S3060. For example... Figure 8 As shown, step S3060 may include:

[0085] S3062: Obtain the transformation relationship between the pre-stored target coordinate system O-XYZ and the original coordinate system o-xyz.

[0086] As mentioned above, the attitude signal acquired by each attitude sensor can be expressed in its corresponding original coordinate system. Specifically, the attitude signal acquired by the attitude sensor can be the attitude signal of a preset fixed coordinate system in the original coordinate system of the measurement position corresponding to the current attitude signal. The motion data calibration system 180 can obtain the expression of the original coordinate system o-xyz of the corresponding attitude sensor in the preset fixed coordinate system based on each attitude signal through inverse transformation. As mentioned above, the motion data calibration system 180 can pre-store the transformation relationship between the preset fixed coordinate system and the target coordinate system O-XYZ. The motion data calibration system 180 can calculate and determine the transformation relationship between each original 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 motion data calibration system 180 can convert the attitude information in the original coordinate system o-xyz into the attitude information in the target coordinate system O-XYZ based on the transformation relationship. In some embodiments, the transformation relationship can be represented as one or more rotation matrices. The rotation matrix can be pre-stored in the motion data calibration system 180.

[0087] S3064: Based on the transformation relationship between the target coordinate system O-XYZ and the original coordinate system o-xyz, convert each attitude signal into three-dimensional motion data in the target coordinate system O-XYZ.

[0088] As mentioned earlier, each attitude signal may include three-dimensional attitude data E in its corresponding original coordinate system o-xyz. sens_x E sens_y E sens_z G sens_x G sens_y and G sens_z The motion data calibration system 180 can determine the three-dimensional motion data of the original coordinate system 0-xyz in the target coordinate system O-XYZ based on the transformation relationship between the target coordinate system O-XYZ and the original coordinate system o-xyz. In some embodiments, the three-dimensional motion data includes at least the angular velocity data G on the X-axis. global_X Angular velocity data G on the Y-axis global_Y and the angular velocity data G on the Z-axis global_Z .

[0089] S3066: Convert the three-dimensional motion data in the target coordinate system O-XYZ into two-dimensional attitude data in the target coordinate system O-XYZ.

[0090] 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 the motion plane when user 001 swings their limbs and the motion plane when user 001 rotates their torso. 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 may include the 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 Vertical attitude data can include vertical angle data E during movement in any vertical plane perpendicular to the horizontal plane. global_XY and vertical angular velocity data G global_XY The vertical plane can be any plane perpendicular to the horizontal plane. Horizontal angle data E global_Z It can be the rotation angle measured within the horizontal plane of the target coordinate system O-XYZ. Horizontal angular velocity data G global_Z It can measure the rotational angular velocity of the position within the horizontal plane of the target coordinate system O-XYZ. Vertical angle data E global_XY It can be the rotation angle measured in any vertical plane within the target coordinate system O-XYZ. Vertical angular velocity data G global_XY It can measure the rotational angular velocity of the position in any vertical plane in the target coordinate system O-XYZ.

[0091] For most gym exercises, the main components of User 001's movements can be broken down into motions in two planes: a horizontal plane and a vertical plane. The different movements User 001 makes can be distinguished solely by these horizontal and vertical plane movements. For example, when User 001 runs on a treadmill, their running motion primarily involves rotational movements around joints parallel to the horizontal plane. This rotational motion occurs in a vertical plane perpendicular to the horizontal plane, extending in the direction User 001's body is facing. In this vertical plane, User 001's center of gravity moves linearly up and down, and their limbs swing accordingly. Similarly, when User 001 performs bicep curls, the movement is entirely within the vertical plane. Likewise, when User 001 performs seated chest presses, the movement is entirely within the horizontal plane.

[0092] Figure 9 This is a coordinate system diagram of user 001 moving according to some embodiments of this application. Figure 9When user 001 performs bicep curls, user 001's orientation remains essentially unchanged. The bicep curl movement is primarily concentrated on the vertical plane P formed by the forearm AO' and upper arm O'B. Since it's an exercise against the weight of the dumbbells, plane P is roughly perpendicular to the horizontal XY plane. Within the vertical plane P, the position of the upper arm O'B remains essentially unchanged, while the forearm AO' swings back and forth around the elbow O'. The vector direction of this swing is the normal direction to the vertical plane P. Apart from this, the components of user 001's bicep curl movement in other directions are very 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.

[0093] like Figure 8 As shown, step S3066 may include:

[0094] S3066-2: Transfer the angular velocity data G on the X-axis global_X and angular velocity data G on the Y-axis global_Y Converted into vertical angular velocity data G using vector laws global_XY .

[0095] Vertical angular velocity data G global_XY This can be expressed as the following formula:

[0096]

[0097] G globaal_XY =|G globaal_XY |*a Formula (2)

[0098] in,

[0099] S3066-4: Based on the time corresponding to the starting and ending positions of user 001's movement, the vertical angular velocity data G... global_XY Perform time integration to obtain vertical angle data E. global_XY .

[0100] Among them, the vertical angle data E global_XY This can be expressed as the following formula:

[0101]

[0102] startpos and endpos are the start and end times corresponding to the start and end positions of an action.

[0103] S3066-6: Transfer the angular velocity data G along the Z-axis global_Z As the horizontal angular velocity data G global_Z .

[0104] S3066-8: Based on the time corresponding to the starting and ending positions of user 001's movement, the horizontal angular velocity data G global_Z Perform time integration to obtain horizontal angle data E. global_Z .

[0105] Among them, the horizontal angle data E global_Z This can be expressed as the following formula:

[0106]

[0107] startpos and endpos are the start and end times corresponding to the start and end positions of an action.

[0108] In some embodiments, the motion data calibration method 3000 may further include:

[0109] S3080: Based on the two-dimensional attitude data corresponding to each attitude signal, determine the relative motion between the at least one measurement position.

[0110] The motion data calibration system 180 can determine the relative motion between different moving parts of user 001's body by using at least one two-dimensional posture data corresponding to at least one posture signal at at least one measurement position of user 001's body. For example, by using the feature information corresponding to the posture sensor at user 001's arm and the feature information corresponding to the posture sensor at user 001's torso, the relative motion between user 001's arm and torso during movement can be determined.

[0111] In summary, the motion data calibration method 3000 and system 180 provided in this application can transform the motion data of user 001 from three-dimensional posture data in three mutually perpendicular coordinate axes into two-dimensional data in the target coordinate system, namely posture data in the horizontal plane and posture data in the vertical plane. This distinguishes the user 001's motion into horizontal and vertical movements, thus avoiding data discrepancies caused by different orientations of user 001. The method 3000 and system 180 can eliminate the influence of user 001's orientation on the motion data. Therefore, the method 3000 and system 180 can calibrate motion data without requiring user 001 to perform calibration actions.

[0112] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0113] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this application do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

Claims

1. A method for calibrating motion data, characterized in that, include: Acquire motion data of the user during movement, the motion data including at least one posture signal corresponding to at least one measurement position on the user, each of the at least one posture signal including three-dimensional posture data of its corresponding measurement position in the original coordinate system; A target coordinate system is constructed, which includes three mutually perpendicular coordinate axes: the X-axis, the Y-axis, and the Z-axis. The Z-axis is the opposite direction to the vertical direction of the gravitational acceleration. The target coordinate system is determined based on a preset fixed coordinate system, which is independent of the user's body orientation. Obtain the pre-stored transformation relationship between the target coordinate system and the original coordinate system; Based on the transformation relationship, each attitude signal is converted into three-dimensional motion data in the target coordinate system. The three-dimensional motion data includes at least the angular velocity data on the X-axis, the angular velocity data on the Y-axis, and the angular velocity data on the Z-axis. Based on the motion data corresponding to the X-axis and the motion data corresponding to the Y-axis, vertical posture data is determined, wherein the vertical posture data represents the user's motion in the vertical direction; and Based on the motion data corresponding to the Z-axis, horizontal posture data is determined, which represents the user's motion in the horizontal direction.

2. The motion data calibration method as described in claim 1, characterized in that, Each attitude signal includes data measured by an attitude sensor, and the original coordinate system includes the coordinate system in which the attitude sensor is located.

3. The motion data calibration method as described in claim 2, characterized in that, The attitude sensor includes at least one of an accelerometer, an angular velocity sensor, or a magnetic sensor.

4. The motion data calibration method as described in claim 1, characterized in that, Each attitude signal includes data measured by an image sensor, and the original coordinate system includes the coordinate system in which the image sensor is located.

5. The motion data calibration method as described in claim 1, characterized in that, The three-dimensional attitude data includes angle data and angular velocity data on three mutually perpendicular coordinate axes.

6. The motion data calibration method as described in claim 1, characterized in that, The motion data calibration method as described in claim 1 is characterized in that the preset fixed coordinate system is a geodetic coordinate system.

7. The motion data calibration method as described in claim 1, characterized in that, The horizontal attitude data includes horizontal angle data and horizontal angular velocity data during movement in a horizontal plane perpendicular to the Z-axis; and The vertical attitude data includes vertical angle data and vertical angular velocity data when moving in any vertical plane perpendicular to the horizontal plane.

8. The motion data calibration method as described in claim 7, characterized in that, The process of determining vertical attitude data based on the motion data corresponding to the X-axis and the motion data corresponding to the Y-axis includes: The angular velocity data on the X-axis and the angular velocity data on the Y-axis are converted into vertical angular velocity data using vector rules. Based on the time corresponding to the start and end positions of the user's movement, the vertical angular velocity data is integrated over time to obtain the vertical angle data; The determination of horizontal attitude data based on the motion data corresponding to the Z-axis includes: The angular velocity data along the Z-axis is used as the horizontal angular velocity data, and Based on the time corresponding to the start and end positions of the user's movement, the horizontal angular velocity data is integrated over time to obtain the horizontal angle data.

9. The motion data calibration method as described in claim 1, characterized in that, Also includes: Based on the vertical attitude data and the horizontal attitude data, the relative motion between the at least one measurement position is determined.

10. A motion data calibration system, characterized in that, include: At least one storage medium storing at least one instruction set for motion data calibration; as well as At least one processor is communicatively connected to the at least one storage medium. When the motion data calibration system is running, the at least one processor reads the at least one instruction set and implements the motion data calibration method according to any one of claims 1-9.

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