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

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Title: Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor
Authors: Nasri, Nadia | Orts-Escolano, Sergio | Gomez-Donoso, Francisco | Cazorla, Miguel
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Surface electromyography sensor | Dataset | Gated recurrent units | Gesture recognition
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 17-Jan-2019
Publisher: MDPI
Citation: 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(2):371. doi:10.3390/s19020371
Abstract: 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.
Sponsor: This work was supported by the Spanish Government TIN2016-76515R grant, supported with Feder funds. It has also been funded by the University of Alicante project GRE16-19, by the Valencian Government project GV/2018/022, and by a Spanish grant for PhD studies ACIF/2017/243.
ISSN: 1424-8220
DOI: 10.3390/s19020371
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Peer Review: si
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Appears in Collections:INV - RoViT - Artículos de Revistas

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