putEMG-A Surface Electromyography Hand Gesture Recognition Dataset

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-08, Vol.19 (16), p.3548
• Main Authors: Kaczmarek, Piotr, Mańkowski, Tomasz, Tomczyński, Jakub
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Algorithms; Classification; dataset; Datasets; Discriminant Analysis; Electrodes; Electromyography; Electromyography - methods; Female; Gesture recognition; Gestures; hand; Hand - physiology; human-machine interface; Humans; International conferences; Laboratories; Male; Muscle function; Robotics; sEMG; Signal processing; Signal-To-Noise Ratio; Support Vector Machine; Support vector machines; Video data; Wavelet transforms; wearable; Wearable Electronic Devices; Young Adult
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19163548

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.