Leveraging sEMG gesture recognition training on edge devices

Tatiana S. Tavares; Alexandre F. Osorio; Irene H. Fantini; Rodrigo R. Alves

doi:10.1117/12.2606147

10 December 2021 Leveraging sEMG gesture recognition training on edge devices

Tatiana S. Tavares, Alexandre F. Osorio, Irene H. Fantini, Rodrigo R. Alves

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 1208813 (2021) https://doi.org/10.1117/12.2606147
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

Surface electromyography (sEMG) is the most common technology used in gesture recognition for hand prosthesis control. Machine learning models based on Convolutional Neural Networks (CNN) that classify the gestures from the sEMG signals can reach high accuracy. However, common changes in the condition of use of the prosthesis, such as electrode shifts, can drastically impact these metrics, causing the need to retrain the model. Considering the application of a model based on such characteristics which was originally trained using data from subjects (source domain) other than the data from the user of the prosthesis (target domain), a domain adaptation must be employed. To lower the time spent during the retraining, only a small amount of data from the target domain must be considered. A relevant factor to be taken into account is that, for the prosthesis to be economically viable, the computational effort required to solve this problem must be supported by a common and inexpensive hardware device. In the current work, the CapgMyo sEMG dataset was used to fine-tune a 2D CNN-based model in an edge device. Inter-subject gesture recognition is performed, where the source domain is composed of the data from 17 of the 18 subjects of the dataset, while the target domain consists of data from the remaining subject. During the fine-tuning, only the classifier layer was retrained, while all the other layers were frozen. The fine-tuning was performed in a common Raspberry Pi 3. Results show that the computational power of the device is enough for a good accuracy using a small amount of data collected by the subject.

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Tatiana S. Tavares, Alexandre F. Osorio, Irene H. Fantini, and Rodrigo R. Alves "Leveraging sEMG gesture recognition training on edge devices", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 1208813 (10 December 2021); https://doi.org/10.1117/12.2606147

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KEYWORDS

Data modeling

Statistical modeling

Gesture recognition

Performance modeling

Feature extraction

Electrodes

Instrument modeling

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