Gesture Recognition and Biometrics ElectroMyogram (GRABMyo)

We present the Gesture Recognition and Biometrics ElectroMyogram (GRABMyo) dataset, an open-access dataset of electromyogram (EMG) recordings collected from the wrist and forearm muscles while performing hand gestures. Data were collected from 43 healthy participants (age range: 24-35 years) on three different days while performing 16 hand and finger gestures in identical experimental sessions on each day. The GRABMyo dataset can be used for research on: 1) EMG-based biometrics for personal identification and verification, and 2) EMG-based gesture recognition for neurorehabilitation and home applications. The large sample size will provide sufficient power for establishing results, specifically for applications such as biometrics. Further, it will be useful for subject-independent applications such as generalized classification models for gesture recognition. The multiday recordings will provide results that will be consistent over a longer duration, crucial for the reliability of EMG-based wearable devices.

本报告呈现了手势识别与生物特征肌电图（GRABMyo）数据集，该数据集为开放获取资源，包含从手腕和前臂肌肉采集的肌电图（EMG）记录。数据收集自43名健康参与者（年龄范围：24-35岁），在三天内进行，每天进行16种相同的手势和手指动作，实验条件保持一致。GRABMyo数据集可用于以下研究：1）基于肌电图的个人身份识别与验证的生物识别技术，以及2）基于肌电图的 gesture recognition技术，应用于神经康复和家庭应用。庞大的样本量将为结果建立提供充分的统计效力，尤其是对于生物识别等应用。此外，它对独立于受试者的应用也将大有裨益，例如，为手势识别构建通用分类模型。多日记录将提供在较长时段内保持一致的结果，这对于基于肌电图的穿戴设备的可靠性至关重要。

搜集汇总

背景与挑战

背景概述

GRABMyo是一个开放获取的肌电图数据集，记录了43名参与者在三天内执行16种手势时的手腕和前臂肌肉活动。该数据集适用于生物识别研究和手势识别应用，其多日记录设计确保了数据的长期可靠性。