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Open AccessArticle

Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor

University Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, Spain
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Sensors 2019, 19(2), 371; https://doi.org/10.3390/s19020371
Received: 28 November 2018 / Revised: 11 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
(This article belongs to the Special Issue Assistance Robotics and Biosensors 2019)
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures. We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. The proposed approach obtained a 99.90% training accuracy and 99.75% validation accuracy. We also evaluated the proposed system on a test set (new subjects) obtaining an accuracy of 77.85%. In addition, we showed the test prediction results for each gesture separately and analyzed which gestures for the Myo armband with our suggested network can be difficult to distinguish accurately. Moreover, we studied for first time the gated recurrent unit network capability in gesture recognition approaches. Finally, we integrated our method in a system that is able to classify live hand gestures. View Full-Text
Keywords: surface electromyography sensor; dataset; gated recurrent units; gesture recognition surface electromyography sensor; dataset; gated recurrent units; gesture recognition
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Nasri, N.; Orts-Escolano, S.; Gomez-Donoso, F.; Cazorla, M. Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor. Sensors 2019, 19, 371.

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