High-risk exercise physiology and action data synchronous acquisition and error correction method

Abstract

The invention relates to the technical field of exercise data acquisition and processing, and particularly discloses a high-risk exercise physiology and action data synchronous acquisition and error correction method. According to the method, real-time synchronous acquisition of physiological data and action data is realized by integrating a wearable physiological signal acquisition module, a high-precision action capture module, a synchronous controller and a data analysis platform. The system adopts a uniform timestamp mechanism and a multi-sensor fusion and motion artifact recognition technology to intelligently correct noise, delay and distortion in acquired data, so that the consistency and reliability of the data are improved. The method is suitable for rock climbing, parachuting, free diving and other high-risk sports scenes, and high-precision data support is provided for athlete state monitoring, training optimization and risk assessment.

Description

Technical Field

[0001] This invention relates to the field of exercise physiological monitoring and motion analysis technology, specifically to a method for synchronous acquisition and error correction of physiological and motion data in high-risk sports scenarios. Background Technology

[0002] With the development of competitive and extreme sports, precise monitoring and analysis of athletes' physiological state and movement techniques during training and competition has become an important means of improving athletic performance and preventing sports injuries. Especially in high-risk sports such as rock climbing, skiing, skydiving, and freediving, athletes are often under extreme physiological and psychological loads. Accurate collection and analysis of data such as heart rate variability, muscle activation patterns, and movement coordination are crucial for assessing sports risks and optimizing training strategies.

[0003] Currently, sports data acquisition technologies mainly fall into two categories: physiological signal acquisition and motion capture. Physiological signal acquisition primarily utilizes wearable ECG, EMG, and pulse oximetry devices, such as products from brands like Polar and Garmin, enabling continuous monitoring of indicators such as heart rate and blood oxygen saturation. Motion capture relies on inertial measurement units or optical motion capture systems, such as Xsens and Vicon, to acquire data on athletes' posture, acceleration, and joint angles. However, existing technologies still have the following shortcomings in practical applications:

[0004] Poor data synchronization: Physiological acquisition equipment and motion capture system often work independently and lack a unified time reference, which makes it difficult for data to be aligned on the timeline and affects subsequent fusion analysis.

[0005] Motion artifact interference is severe: During high-intensity exercise, the sensor is susceptible to motion noise interference, especially motion artifacts mixed in with electrocardiogram and electromyography signals, which reduce signal quality and recognition accuracy.

[0006] Inconsistent system latency: Different sensors and transmission modules have different degrees of acquisition and transmission latency, resulting in poor data real-time performance and difficulty in accurately reflecting the instantaneous state of motion.

[0007] Lack of intelligent error correction mechanism: Existing systems mostly rely on manual correction or simple filtering in the later stage, which cannot adapt to the complex and ever-changing noise environment in high-risk sports, and the correction effect is limited.

[0008] Therefore, there is an urgent need for a technical solution that can achieve high-precision synchronous acquisition of physiological and motor data, and has intelligent error recognition and correction capabilities, in order to support scientific training and safety monitoring of high-risk sports. Summary of the Invention

[0009] This invention provides a method and system for synchronous acquisition and error correction of high-risk exercise physiological and movement data, aiming to solve the problems of poor data synchronization, large noise interference, inconsistent delay and lack of intelligent correction in the prior art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a system for synchronous acquisition and error correction of high-risk exercise physiological and movement data, comprising:

[0011] Wearable physiological signal acquisition module, used to collect real-time physiological signals of athletes' electrocardiogram, electromyography, and blood oxygenation;

[0012] The motion capture module is used to collect real-time data on the athlete's posture, acceleration, and angular velocity.

[0013] The synchronization controller provides a unified timestamp for all acquisition modules based on a high-precision clock source.

[0014] The data processing platform receives synchronous data and performs motion artifact recognition, delay compensation, and signal reconstruction.

[0015] Furthermore, the wearable physiological signal acquisition module employs flexible electrodes and a low-noise amplification circuit to improve wearing comfort and signal quality.

[0016] Furthermore, the motion capture module integrates inertial measurement unit and optical marker data to improve attitude estimation accuracy.

[0017] Furthermore, the synchronization controller supports a combination of GPS second pulse timing and local crystal oscillator timing to ensure time synchronization even in the absence of a signal.

[0018] Furthermore, the data processing platform incorporates an artifact recognition model based on wavelet transform and support vector machine, which can automatically identify and filter out motion noise.

[0019] Furthermore, the platform also features a dynamic latency compensation algorithm that adjusts the data alignment strategy based on real-time network conditions.

[0020] Furthermore, the system supports dual-mode transmission of Bluetooth 5.0 and Wi-Fi 6, enabling low-latency and highly reliable data transmission.

[0021] Furthermore, the system provides a visual data dashboard that displays the results of physiological and motor fusion analysis in real time.

[0022] Furthermore, the system has edge computing capabilities, enabling preliminary filtering and feature extraction to be performed on the device side, reducing the burden on the cloud.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] It achieves millisecond-level synchronous acquisition of physiological and action data, improving the accuracy of multimodal data fusion analysis;

[0025] The intelligent motion artifact recognition and filtering algorithm significantly improves signal quality and feature extraction reliability;

[0026] It has dynamic latency compensation capabilities, adapts to different network environments, and ensures data real-time performance and consistency;

[0027] The system has a lightweight structure and is comfortable to wear, making it suitable for various high-risk sports scenarios and easy to deploy on-site and monitor for long-term use.

[0028] It supports cloud collaboration and edge computing, balancing data processing efficiency and system scalability. Attached Figure Description

[0029] Figure 1 This is a flowchart of the system architecture of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0031] In the description of this invention, the term "wearable" refers to a device that can be attached to the surface of an athlete's body; "synchronization" refers to the fact that the data from each acquisition module has a unified time reference; and "motion artifact" refers to physiological signal noise caused by body movement.

[0032] Please see Figure 1 The present invention provides the following technical solutions:

[0033] A system for synchronous acquisition and error correction of high-risk exercise physiological and movement data includes a wearable physiological signal acquisition module, a motion capture module, a synchronization controller, and a data processing platform. The physiological signal acquisition module uses an AD8232 electrocardiogram sensor with a sampling rate of 500Hz; a Max30102 blood oxygen sensor with a sampling rate of 100Hz; and Delsys Trigno series surface electromyography electrodes with a sampling rate of 2000Hz. The motion capture module includes an Xsens MTw Awinda inertial sensor array (sampling rate 100Hz) and an OptiTrack Flex 13 optical camera array (sampling rate 120Hz). The synchronization controller uses a U-blox NEO-M8N GPS module for timing and is equipped with a TCXO temperature-compensated crystal oscillator for timekeeping, achieving a time synchronization accuracy better than ±1ms.

[0034] The data processing platform is built on NVIDIA Jetson Xavier edge computing devices. Its built-in motion artifact recognition algorithm uses wavelet transform (db4 wavelet basis) for signal decomposition and combines it with an SVM classifier to identify noise components. The latency compensation model dynamically adjusts the data buffers based on real-time ping values, achieving adaptive network alignment.

[0035] Working principle:

[0036] After system startup, the synchronization controller broadcasts a unified timestamp to all acquisition modules. The wearable physiological and motion modules begin data acquisition based on this timestamp and transmit the time-tagged data to the data processing platform in real time via Bluetooth / Wi-Fi. Upon receiving the data, the platform first performs time alignment and format standardization, then uses a motion artifact recognition model to detect and filter noise from ECG and EMG signals. For motion data, Kalman filtering and sensor fusion algorithms are used to improve posture estimation accuracy. Finally, the corrected physiological and motion data are synchronously stored and pushed to the visualization interface.

[0037] Example:

[0038] Taking the monitoring of rock climbers' training as an example, the system deployment is as follows:

[0039] The physiological module is worn on the chest and arm to collect electrocardiogram and electromyography signals;

[0040] Inertial sensors are fixed to the limbs and torso, and optical markers are attached to the joints;

[0041] The synchronization controller is placed in the center of the site to ensure signal coverage for all modules.

[0042] During use, the system collects heart rate variability, muscle activation timing, joint angles, and body center of gravity trajectory in real time as the athlete completes the climb. The data processing platform identifies and filters out ECG artifacts caused by arm swings; simultaneously, it corrects for drift errors in the inertial sensors based on optical data. The final output is a synchronized and clean data curve used to analyze the economy and risks of climbing movements.

[0043] It is worth noting that in this embodiment:

[0044] For ECG electrode placement, it is recommended to choose the V2-V4 area on the chest to reduce motion artifacts.

[0045] The inertial sensor sampling rate is no less than 100Hz to capture details of fast-moving motion;

[0046] When there is no GPS signal indoors, the synchronization controller can switch to crystal oscillator timekeeping mode to keep the system continuously synchronized;

[0047] The data processing platform algorithm can optimize parameters according to the type of sport (such as skiing or diving) and adapt to different noise characteristics.

[0048] Although embodiments of this application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for synchronous acquisition and error correction of high-risk exercise physiological and motor data, characterized in that, include: Wearable physiological signal acquisition module is used to collect athletes' heart rate, blood oxygen, and electromyography signals; The motion capture module is used to collect data on the athlete's posture, acceleration, and angular velocity. The synchronization controller is used to provide a unified time base for all modules and achieve data synchronization. The data processing platform is used to receive synchronized data and perform error identification and correction.

2. The system according to claim 1, characterized in that, The wearable physiological signal acquisition module includes an electrocardiogram sensor, a blood oxygen probe, and surface electromyography electrodes.

3. The system according to claim 1, characterized in that, The motion capture module includes an inertial measurement unit and an optical motion capture camera array.

4. The system according to claim 1, characterized in that, The synchronization controller uses GPS timing or crystal oscillator clock synchronization mechanism.

5. The system according to claim 1, characterized in that, The data processing platform incorporates motion artifact recognition algorithms and delay compensation models.

6. A method for synchronously acquiring high-risk motion data based on the system described in any one of claims 1-5, characterized in that, Includes the following steps: Initialize each acquisition module and the synchronization controller; Physiological and motor data are collected synchronously using a unified timestamp; Real-time transmission to the data processing platform; Motion artifact recognition and data correction are performed.

7. The method according to claim 6, characterized in that, The motion artifact recognition method combines wavelet transform with a machine learning classifier.

8. The method according to claim 6, characterized in that, The data correction includes time alignment, noise filtering, and signal reconstruction.

9. A method for correcting errors in high-risk sports data, characterized in that, Based on the data collected by the method described in any one of claims 6-8, the collected data is iteratively optimized by establishing an athlete motion model and a sensor error model.

10. A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the method of any one of claims 6-9.

Images