In this paper, we present a putEMG dataset intended for 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. putEMG dataset is available under Creative Commons
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license at:
https://www.biolab.put.poznan.pl/putemg-dataset/. The dataset was validated
regarding sEMG amplitudes and gesture recognition performance. The
classification was performed using state-of-the-art classifiers and feature
sets. 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. Presented dataset can be used as a benchmark for
various classification methods, evaluation of electrode localisation concepts,
or development of classification methods invariant to user-specific features or
electrode displacement
