Wearable devices that monitor muscle activity based on surface electromyography could be of use in the development of hand gesture recognition applications. Such devices typically use machine-learning models, either locally or externally, for gesture classification. However, most devices with local processing cannot offer training and updating of the machine-learning model during use, resulting in suboptimal performance under practical conditions. Here we report a wearable surface electromyography biosensing system that is based on a screen-printed, conformal electrode array and has in-sensor adaptive learning capabilities. Our system implements a neuro-inspired hyperdimensional computing algorithm locally for real-time gesture classification, as well as model training and updating under variable conditions such as different arm positions and sensor replacement. The system can classify 13 hand gestures with 97.12% accuracy for two participants when training with a single trial per gesture. A high accuracy (92.87%) is preserved on expanding to 21 gestures, and accuracy is recovered by 9.5% by implementing model updates in response to varying conditions, without additional computation on an external device.

A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition / Moin, A.; Zhou, A.; Rahimi, A.; Menon, A.; Benatti, S.; Alexandrov, G.; Tamakloe, S.; Ting, J.; Yamamoto, N.; Khan, Y.; Burghardt, F.; Benini, L.; Arias, A. C.; Rabaey, J. M.. - In: NATURE ELECTRONICS. - ISSN 2520-1131. - 4:1(2021), pp. 54-63. [10.1038/s41928-020-00510-8]

A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition

Benatti S.;
2021

Abstract

Wearable devices that monitor muscle activity based on surface electromyography could be of use in the development of hand gesture recognition applications. Such devices typically use machine-learning models, either locally or externally, for gesture classification. However, most devices with local processing cannot offer training and updating of the machine-learning model during use, resulting in suboptimal performance under practical conditions. Here we report a wearable surface electromyography biosensing system that is based on a screen-printed, conformal electrode array and has in-sensor adaptive learning capabilities. Our system implements a neuro-inspired hyperdimensional computing algorithm locally for real-time gesture classification, as well as model training and updating under variable conditions such as different arm positions and sensor replacement. The system can classify 13 hand gestures with 97.12% accuracy for two participants when training with a single trial per gesture. A high accuracy (92.87%) is preserved on expanding to 21 gestures, and accuracy is recovered by 9.5% by implementing model updates in response to varying conditions, without additional computation on an external device.
2021
4
1
54
63
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition / Moin, A.; Zhou, A.; Rahimi, A.; Menon, A.; Benatti, S.; Alexandrov, G.; Tamakloe, S.; Ting, J.; Yamamoto, N.; Khan, Y.; Burghardt, F.; Benini, L.; Arias, A. C.; Rabaey, J. M.. - In: NATURE ELECTRONICS. - ISSN 2520-1131. - 4:1(2021), pp. 54-63. [10.1038/s41928-020-00510-8]
Moin, A.; Zhou, A.; Rahimi, A.; Menon, A.; Benatti, S.; Alexandrov, G.; Tamakloe, S.; Ting, J.; Yamamoto, N.; Khan, Y.; Burghardt, F.; Benini, L.; Arias, A. C.; Rabaey, J. M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1264897
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