A novel fully wearable system based on a smart wristband equipped with stretchable strain gauge sensors and readout electronics have been assembled and tested to detect a set of movements of a hand crucial in rehabilitation procedures. The high sensitivity of the active devices embedded on the wristband do not need a direct contact with the skin, thus maximizing the comfort on the arm of the tester. The gestures done with the device have been auto-labeled by comparing the signals detected in real-Time by the sensors with a commercial infrared device (Leap motion). Finally, the system has been evaluated with two machine-learning algorithms Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), reaching a reproducibility of 98% and 94%, respectively.
Ferrone, A.A., Maita, F.A., Maiolo, L.A., Arquilla, M.A., Castiello, A.A., Pecora, A., et al. (2017). A fabric-based wearable band for hand gesture recognition based on filament strain sensors: A preliminary investigation. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. Singapore : IEEE Computer Society [10.1109/BIOROB.2016.7523814].
A fabric-based wearable band for hand gesture recognition based on filament strain sensors: A preliminary investigation
FERRONE, ANDREA;COLACE, Lorenzo
2017-01-01
Abstract
A novel fully wearable system based on a smart wristband equipped with stretchable strain gauge sensors and readout electronics have been assembled and tested to detect a set of movements of a hand crucial in rehabilitation procedures. The high sensitivity of the active devices embedded on the wristband do not need a direct contact with the skin, thus maximizing the comfort on the arm of the tester. The gestures done with the device have been auto-labeled by comparing the signals detected in real-Time by the sensors with a commercial infrared device (Leap motion). Finally, the system has been evaluated with two machine-learning algorithms Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), reaching a reproducibility of 98% and 94%, respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
