Detection and biomechanical analysis of human posture embedded electronic system based on D-H matrix method
Abstract
Background: This study aims to model the kinematics of human joints using the Denavit-Hartenberg matrix method (hereinafter referred to as D-H matrix method) and combine biomechanical analysis for posture evaluation, thereby providing a more accurate and efficient detection solution. It ensures the implementation of complex calculations under low-power conditions and has broad application prospects in fields such as rehabilitation medicine, sports analysis, and virtual reality. Objective: The aim of this study is to design a sensor fusion-based embedded electronic system by integrating nine-axis sensors such as accelerometers, gyroscopes, and magnetometers. This system combines the D-H matrix method and forward kinematics for human posture detection and biomechanical analysis, to improve the system’s detection accuracy and response speed. Methods: Traditional forward kinematics and the D-H matrix method are used for kinematic modeling to enhance the accuracy and efficiency of posture calculation. Innovation: The D-H matrix method, a classical analysis technique in robotics typically used for kinematic analysis of robotic arms, is successfully applied in this study to human posture detection, breaking through traditional posture analysis methods. By utilizing the D-H matrix method to model the movement relationships between human joints, this study provides a more precise mathematical model for posture detection. By combining embedded electronic systems with biomechanical analysis to evaluate human posture, and introducing real-time monitoring of biomechanical loads from a biomechanical perspective, this study ensures that real-time human posture detection is not only efficient but also capable of performing complex calculations under low power conditions. Results: To further improve the accuracy of the sensors, this study analyzed the error characteristics of the inertial sensors and applied preprocessing algorithms to correct the errors in the signals from the magnetometer, accelerometer, and gyroscope. Combined with a high-pass and low-pass complementary filter fusion algorithm, the experiment showed that this algorithm successfully resolved the random drift and cumulative errors in the attitude angles calculated. The posture calculation system using the D-H matrix method outperforms the traditional forward kinematics method in terms of response time and root mean square error (hereinafter referred to as MSE). For instance, the response time for the right upper arm is reduced by 74.67% compared to traditional methods, while the MSE remains within a reasonable range.
References
1. Li H. Analysis of lightweight human pose estimation algorithm based on key-point detection. Computer Programming Techniques and Maintenance; 2024.
2. Li J, Wang C, & Su W. Two-dimensional human pose estimation algorithm based on adaptive parametric NMS. Journal of Jilin University (Engineering Edition). 2024.
3. Fang X, & Huang J. Human pose detection model based on YOLOv8-pose. Laser Magazine; 2024.
4. Wang S, Zhu S, Zang Y, et al. (2024). Development of human pose detection system and design of smart clothing. Journal of Sensor Technology. 2024.
5. Ding Y, Wang Y, & Li Z. Research on real-time human pose and heart rate detection method for wearable single soldier systems. Journal of Guilin University of Aerospace Technology. 2024.
6. Zhao J, & Jia D. Improved YOLOv5 multi-person pose estimation correction algorithm. Computer Engineering and Science; 2024.
7. Zeng W, & Li Y. Deep learning algorithm for human pose keypoint detection in pose images. Computer Simulation; 2024.
8. Liu H, Tao X, Xu C, Cao D. An optimized multi-scale feature fusion method for human pose estimation. Journal of Mechanical Engineering. 2024.
9. Zhang G, Zhang L, Ma C, & Fan Z. Design of multifunctional pose detection and early warning device. Electronic Design Engineering; 2023.
10. Wu T, Yuan X, & Hu W. Human motion capture system based on inertial sensors. Automation and Information Engineering; 2023.
11. Wang X, Wu J, Yang K, et al. Human pose detection method for underground coal mines. Industrial Automation; 2022.
12. Zhang L, Chen F, Yang W, & Li H. Design of remote fall alarm system based on six-axis pose sensor. Science and Technology and Innovation; 2022.
13. Zhang J, & Li C. Human behavior recognition method based on LSTM multi-sensor data fusion. Journal of Wuhu Vocational Technical College. 2021.
14. Dong H, Ao W, Luo Q, et al. Design and implementation of a wearable smart posture correction system. Journal of Zhejiang University of Technology. 2021.
15. Liu Y, Hui H, Lu Y, et al. A remote human pose monitoring system based on smartphone terminals. Journal of Inertial Technology. 2019.
16. Liu X, Tian G, Chen W, et al. Human pose detection and home appliance control system design based on MPU6050. Modern Computer; 2019.
17. Jin, S., Liu, J., Luo, T., & Zhu, H. (2019). Design of a monitoring system for elderly living alone based on microcontroller. Light Industry Technology, 09, 78–79 + 82.
18. Wang L, Zhang Z, Niu Q, et al. Human pose detection system design based on MPU9250 and MS5611. Electronic Devices; 2019.
19. Mo S, Liu T, & He L. Research on 3D human pose sensing detection method. Medical and Health Equipment; 2018.
20. Song Y, Zhang B, Tang F, et al. Research on pose detection methods in sports wristbands. Computer and Telecommunications; 2018.
21. Zheng, Y., Li, F., Zhang, L., & Liu, S. (2018). Human pose detection method based on long short-term memory networks. Computer Applications, 06, 1568–1574.
22. Qiang J, Zhang W, & Wang D. Human pose detection and behavior analysis method based on foot pressure. Measurement and Control Technology; 2018.
23. Ma W, Wang X, & Wu Z. Fall detection method based on PSO-SVM feature vector for human pose. Journal of Sensor Technology. 2017.
24. Hu X, Li X, Qi Y, et al. Design of a wearable human pose detection system. Electronic Technology Applications; 2017.
25. Liu T. Design of a motion sensing skateboard control system based on human pose detection. Microcontroller and Embedded Systems Applications; 2017.
26. Liu X, Wang L, Zou Y, et al. Wearable modular human pose detection system based on BLE technology. Experimental Science and Technology; 2018.
27. Zhang Y, Ren T, & Luo Y. Research on human motion mode recognition based on multi-sensor data fusion. Computer Measurement and Control; 2016.
28. Li S, & Wang H. Design of a human motion detection system based on inertial sensors. Computer Optical Disc Software and Applications; 2014.
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
