# FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations

arXiv: https://arxiv.org/abs/2409.07484 · HTML: https://mlsanity.com/paper/2409.07484/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [HaGRID - HAnd Gesture Recognition Image Dataset](https://mlsanity.com/paper/2206.08219/) (2022)
- [Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features](https://mlsanity.com/paper/2203.14612/) (2022)
- [EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living](https://mlsanity.com/paper/2301.03325/) (2023)
- [Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning](https://mlsanity.com/paper/2404.15360/) (2024)

## Built on by (most cited first, 2 of 2)

- [Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition](https://mlsanity.com/paper/2411.15655/) (2024)
- [Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features](https://mlsanity.com/paper/2504.14708/) (2025)

## Abstract (arXiv)

Surface electromyography (sEMG) signals hold significant potential for gesture recognition and robust prosthetic hand development. However, sEMG signals are affected by various physiological and dynamic factors, including forearm orientation, electrode displacement, and limb position. Most existing sEMG datasets lack these dynamic considerations. This study introduces a novel multichannel sEMG dataset to evaluate commonly used hand gestures across three distinct forearm orientations. The dataset was collected from nineteen able-bodied subjects performing twelve hand gestures in three forearm orientations--supination, rest, and pronation. Eight MFI EMG electrodes were strategically placed at the elbow and mid-forearm to record high-quality EMG signals. Signal quality was validated through Signal-to-Noise Ratio (SNR) and Signal-to-Motion artifact ratio (SMR) metrics. Hand gesture classification performance across forearm orientations was evaluated using machine learning classifiers, including LDA, SVM, and KNN, alongside five feature extraction methods: TDD, TSD, FTDD, AR-RMS, and SNTDF. Furthermore, deep learning models such as 1D CNN, RNN, LSTM, and hybrid architectures were employed for a comprehensive analysis. Notably, the LDA classifier achieved the highest F1 score of 88.58% with the SNTDF feature set when trained on hand gesture data of resting and tested across gesture data of all orientations. The promising results from extensive analyses underscore the proposed dataset's potential as a benchmark for advancing gesture recognition technologies, clinical sEMG research, and human-computer interaction applications. The dataset is publicly available in MATLAB format. Dataset: urlhttps://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset