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Open AccessArticle

putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

Institute of Control, Robotics and Information Engineering - Poznan University of Technology, Piotrowo 3A, 60-965 Poznań, Poland
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3548;
Received: 28 June 2019 / Revised: 5 August 2019 / Accepted: 12 August 2019 / Published: 14 August 2019
(This article belongs to the Section Biomedical Sensors)
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In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject’s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin’s and Du’s feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement. View Full-Text
Keywords: sEMG; dataset; gesture recognition; hand; human-machine interface; wearable sEMG; dataset; gesture recognition; hand; human-machine interface; wearable

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kaczmarek, P.; Mańkowski, T.; Tomczyński, J. putEMG—A Surface Electromyography Hand Gesture Recognition Dataset. Sensors 2019, 19, 3548.

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