Efficiently Training Two-DoF Hand-Wrist EMG-Force Models

Single-use EMG-force models (i.e., a new model is trained each time the electrodes are donned) are used in various areas, including ergonomics assessment, clinical biomechanics, and motor control research. For one degree of freedom (1-DoF) tasks, input-output (black box) models are common. Recently,...

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Bibliographic Details
Main Authors: Bardizbanian, Berj, Zhu, Ziling, Li, Jianan, Huang, Xinming, Dai, Chenyun, Martinez-Luna, Carlos, McDonald, Benjamin E., Farrell, Todd R., Clancy, Edward A.
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Computational modeling
Dynamics
Electrodes
Electromyography
Finite impulse response filters
Training
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Summary:Single-use EMG-force models (i.e., a new model is trained each time the electrodes are donned) are used in various areas, including ergonomics assessment, clinical biomechanics, and motor control research. For one degree of freedom (1-DoF) tasks, input-output (black box) models are common. Recently, black box models have expanded to 2-DoF tasks. To facilitate efficient training, we examined parameters of black box model training methods in 2-DoF force-varying, constant-posture tasks consisting of hand open-close combined with one wrist DoF. We found that approximately 40-60 s of training data is best, with progressively higher EMG-force errors occurring for progressively shorter training durations. Surprisingly, 2-DoF models in which the dynamics were universal across all subjects (only channel gain was trained to each subject) generally performed 15-21% better than models in which the complete dynamics were trained to each subject. In summary, lower error EMG-force models can be formed through diligent attention to optimization of these factors.
ISSN:2694-0604
DOI:10.1109/EMBC44109.2020.9175675