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力学肌电图（GRABMyo）数据集，该数据集为开放获取资源，收录了在手腕及前臂肌肉执行手部动作时采集的肌电图（EMG）记录。数据收集自43名健康受试者（年龄范围：24-35岁），在三个不同的日子进行了相同的实验会话，每日均完成16种手及手指手势。GRABMyo数据集可用于以下研究：1) 基于肌电图的生物识别技术，用于个人身份识别与验证；2) 基于肌电图的姿态识别，应用于神经康复和家庭应用。庞大的样本量将为建立研究结果提供充足的统计效力，尤其是对于生物识别等应用而言。此外，对于需要独立于受试者的应用，如通用的姿态识别分类模型，也将具有实用价值。多日记录将提供长期一致的实验结果，这对于肌电图穿戴设备的可靠性至关重要。