期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 30, 期 -, 页码 486-495出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2022.3153252
关键词
High-density electromyography; movement estimation; deep learning; convolutional neural networks; dynamic contraction
资金
- Natural Sciences and Engineering Research Council of Canada [RGPIN-2016-04788]
A novel method for accurately modelling joint angle and velocity using EMG-based motion estimation is proposed. Experimental results demonstrate the robustness of the method compared to conventional approaches.
EMG-based motion estimation is required for applications such as myoelectric control, where the simultaneous estimation of kinematic information, namely joint angle and velocity, is challenging and critical. We propose a novel method for accurately modelling the generated joint angle and velocity simultaneously under isotonic, isokinetic (quasi-dynamic), and fully dynamic conditions. Our solution uses two streams of CNN, called TS-CNN to learn informative features from raw EMG data using different scales and estimate the generated motion during elbow flexion and extension. The experimental results show the robustness of our approach in comparison to conventional CNN as well as some methods used in the literature. The best obtained R-2 values, are 0.81 +/- 0.06, 0.71 +/- 0.06, and 0.80 +/- 0.13 for joint angle estimation and 0.78 +/- 0.05, 0.79 +/- 0.07, and 0.71 +/- 0.13 for the velocity estimation, during isotonic, isokinetic, and dynamic contractions, respectively. Additionally, our results indicate that the experimental condition can have an impact on the model's performance for motion prediction. EMG-based velocity estimation obtains higher performance than joint angle estimation under isokinetic conditions. Under dynamic conditions, joint angle estimation is more accurate than velocity estimation, and there is no difference between joint angle and velocity estimation in the isotonic case.
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