EMG features dataset for arm activity recognition

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-04-04 DOI:10.1016/j.dib.2025.111519

Koundinya Challa, Issa W. AlHmoud, Chandra Jaiswal, Anish C. Turlapaty, Balakrishna Gokaraju

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Abstract

This study presents a dataset on hand gesture recognition using electromyography (EMG) signals. The data was collected from eight healthy subjects aged between 19 and 35 years, with each subject performing three distinct hand gestures (lifting, grabbing, and flexing). Surface EMG signals were recorded using the Delsys Trigno Wireless biofeedback system from four sensors placed on the dominant hand's Palm A, Palm B, Biceps, and Forearm. The signals were sampled at 2000 Hz and segmented into gesture trials for analysis. The raw EMG data were filtered and processed to extract seven time-domain features across each channel, resulting in 28 total features. These features were reduced using Principal Component Analysis (PCA) to six components, which accounted for 95 % of the variance. The dataset was then used to train and test machine learning models (Random Forest and Logistic Regression) for gesture classification. This dataset has potential reuse in developing gesture recognition algorithms, enhancing prosthetic control, or exploring human–computer interaction (HCI) applications.

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肌电图特征数据集用于手臂活动识别

本研究提出了一个使用肌电信号进行手势识别的数据集。数据收集自8名年龄在19至35岁之间的健康受试者，每个受试者执行三种不同的手势（抬起，抓住和弯曲）。使用Delsys Trigno无线生物反馈系统记录体表肌电信号，这些信号来自放置在主用手手掌A、手掌B、二头肌和前臂上的四个传感器。信号在2000赫兹采样，并分割成手势试验进行分析。对原始肌电信号数据进行过滤和处理，在每个通道中提取7个时域特征，得到28个特征。使用主成分分析（PCA）将这些特征减少到6个成分，占方差的95%。然后使用该数据集训练和测试用于手势分类的机器学习模型（随机森林和逻辑回归）。该数据集在开发手势识别算法、增强假肢控制或探索人机交互（HCI）应用方面具有潜在的重用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.