Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography
Abstract
:1. Introduction
2. Experimental Setup
2.1. Experimental Design
- Gait: Each participant completed this experiment in approximately 5.5 s, with a walking speed controlled at 2 m/s, performing a total of 5 trials. Gait involves the dynamic changes of lower limb joints during walking, which helps analyze the joint angle variations and gait cycle of normal walking. Although this speed (equivalent to 7.2 km/h) exceeds typical walking speeds (usually around 1.2–1.5 m/s), it was deliberately chosen to simulate brisk walking or rapid movement conditions, offering a more thorough evaluation of the model’s performance in such scenarios.
- Obstacle Crossing: Each participant completed this experiment in approximately 5 s, performing a total of 5 trials. Obstacle crossing simulates lower limb movements when stepping over an obstacle, providing angle change data for rapid movements within a short time.
- Squatting: Each participant completed this experiment in approximately 25 s, with 10 repetitions of each squatting action, performing a total of 5 trials. This movement pattern is designed to simulate the squatting and standing actions in daily life and is suitable for studying dynamic control and joint angle variations in the lower limbs.
- Knee Flexion–Extension: Each participant completed this experiment in approximately 20 s, with 10 repetitions of each knee flexion–extension action, performing a total of 5 trials. This movement pattern primarily focuses on the flexion–extension of the knee joint and effectively tests the continuous variation in the knee joint angle.
2.2. Data Collection
2.3. Data Processing
2.4. Sliding Window Data Expansion Strategy
3. Methods
3.1. TCN
3.1.1. Causal Convolution
3.1.2. Dilated Convolution
3.1.3. Residual Module
3.2. CBAM
3.3. CB-TCN
3.4. Evaluation Metrics
3.5. Model Comparison
- (1)
- ED-TCN
- (2)
- TCN
- (3)
- LSTM
- (4)
- Wiener Filter
4. Results
4.1. Angle Estimation Curve Analysis
- (1)
- Gait Motion
- (2)
- Obstacle Crossing Motion
- (3)
- Squat and Stand Motion
- (4)
- Knee Flexion–Extension Motion
4.2. Comparison Analysis of the CB-TCN Model with Other Models
- (1)
- Gait Motion
- (2)
- Obstacle Crossing Motion
- (3)
- Squat and Stand Motion
- (4)
- Flexion–Extension Motion
4.3. Real-Time Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Activity | Hip Joint (ms) | Knee Joint (ms) | Ankle Joint (ms) |
Walking | 13.20 ± 0.048 | 13.53 ± 0.124 | 13.46 ± 0.056 |
Obstacle Crossing | 34.59 ± 0.068 | 34.31 ± 0.044 | 34.01 ± 0.072 |
Squatting | 28.68 ± 0.064 | 28.68 ± 0.076 | 27.77 ± 0.060 |
Knee Flexion-Extension | 27.17 ± 0.060 | 27.18 ± 0.048 | 27.18 ± 0.048 |
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Han, Y.; Tao, Q.; Zhang, X. Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography. Sensors 2025, 25, 719. https://doi.org/10.3390/s25030719
Han Y, Tao Q, Zhang X. Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography. Sensors. 2025; 25(3):719. https://doi.org/10.3390/s25030719
Chicago/Turabian StyleHan, Yonglin, Qing Tao, and Xiaodong Zhang. 2025. "Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography" Sensors 25, no. 3: 719. https://doi.org/10.3390/s25030719
APA StyleHan, Y., Tao, Q., & Zhang, X. (2025). Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography. Sensors, 25(3), 719. https://doi.org/10.3390/s25030719