FORS-EMG：一种用于多前臂朝向手势识别的新型表面肌电数据集
FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations
AI总结：
针对现有表面肌电数据集缺乏动态因素考量的问题，本研究构建了包含三种前臂朝向的新型手势识别数据集，可用于评估模型跨朝向性能，为相关技术发展提供基准支持。
摘要：
表面肌电（sEMG）信号在手语识别和鲁棒假肢手开发中具有重要潜力。然而，sEMG信号受多种生理和动态因素影响，包括前臂朝向、电极位移和肢体位置。大多数现有sEMG数据集缺乏这些动态考量。本研究引入一种新型多通道sEMG数据集，用于评估三种不同前臂朝向下的常用手势。该数据集从19名健康受试者中收集，受试者在三种前臂朝向（旋后、静止、旋前）下执行12种手势。8个MFI肌电电极被战略性放置在肘部和前臂中部，以记录高质量肌电信号。信号质量通过信噪比（SNR）和运动伪影比（SMR）指标验证。使用机器学习分类器（包括LDA、SVM和KNN）以及五种特征提取方法（TDD、TSD、FTDD、AR-RMS和SNTDF）评估了跨前臂朝向的手势分类性能。此外，还采用了1D CNN、RNN、LSTM和混合架构等深度学习模型进行综合分析。值得注意的是，LDA分类器在静止朝向手势数据上训练时，使用SNTDF特征集在所有朝向手势数据上达到了88.58%的最高F1分数。广泛分析的良好结果强调了该数据集作为推进手势识别技术、临床sEMG研究和人机交互应用基准的潜力。该数据集以MATLAB格式公开提供。数据集：https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset
英文摘要：
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: \url{https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
