| Autor: |
Joseph Berman, Robert Hinson, He Huang |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). |
| DOI: |
10.1109/embc46164.2021.9630744 |
| Popis: |
Recent work on electromyography (EMG)-based decoding of continuous joint kinematics has included model-based approaches, such as musculoskeletal modeling, as well as model-free approaches such as supervised learning neural networks (SLNN). This study aimed to present a new kinematics decoding framework based on reinforcement learning (RL), which combines machine learning and model-based approaches together. We compared the performance and robustness of our new method with those of the SLNN approach. EMG and kinematic data were collected from 5 able-bodied subjects while they performed flexion and extension of the metacarpophalangeal (MCP) and wrist joints simultaneously at both a slow and fast tempo. The data were used to train an RL agent and a SLNN for each of the 2 tempos. All the trained agents and SLNNs were tested with both fast and slow kinematic data. Pearson's correlation coefficient (r) and normalized root mean square error (NRMSE) between measured and estimated joint angles were used to determine performance. Our results suggest that the RL-based kinematics decoder is more robust to changes in movement speeds between training and testing data and has better performance than the SLNN. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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