# putEMG -- a surface electromyography hand gesture recognition dataset

arXiv: https://arxiv.org/abs/1907.07428 · HTML: https://mlsanity.com/paper/1907.07428/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Open Access Dataset for Electromyography based Multi-code Biometric Authentication](https://mlsanity.com/paper/2201.01051/) (2022)
- [EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models](https://mlsanity.com/paper/2107.09853/) (2021)
- [Evaluating Classifier Confidence for Surface EMG Pattern Recognition](https://mlsanity.com/paper/2304.05898/) (2023)
- [Combining Vision and EMG-Based Hand Tracking for Extended Reality Musical Instruments](https://mlsanity.com/paper/2307.10203/) (2023)
- [The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces](https://mlsanity.com/paper/2408.02547/) (2024)
- [MyoCodec: A Streaming Neural Codec for Electromyography](https://mlsanity.com/paper/2609.36687/) (2026)

## Abstract (arXiv)

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.