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bardizbanian_2020_AuthorsVersion_EfficientlyTrainingTwoDoFHandWristEMGForceModels.pdf (1.12 MB)

Efficiently Training Two-DoF Hand-Wrist EMG-Force Models (Author's Version)

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conference contribution
posted on 2020-08-12, 20:02 authored by Berj Bardizbanian, Ziling Zhu, Jianan Li, Xinming Huang, Chenyun Dai, Carlos Martinez-Luna, Benjamin McDonaldBenjamin McDonald, Todd Farrell, Edward Clancy, Liberating Technologies, Inc.Liberating Technologies, Inc.
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.

Presented at the 2020 IEEE Engineering in Medicine and Biology Society Conference (EMBC).

*This is the Author's Final Copy. Official version available in the proceedings of the 2020 IEEE Engineering in Medicine and Biology Society Conference (EMBC) (Volume 42, pp. 369–373).

Funding

Simultaneous, Proportional and Independent sEMG-Based Hand-Wrist Prosthesis Control

Eunice Kennedy Shriver National Institute of Child Health and Human Development

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