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.

本报告推出名为“手势识别与生物特征肌电图（GRABMyo）”的开放访问数据集，该数据集收录了在执行手部手势时，从腕部和前臂肌肉采集的肌电图（EMG）记录。数据采集自43名健康的参与者（年龄范围：24-35岁），在三天内进行，每天在相同的实验条件下完成16种手部和手指手势。GRABMyo数据集适用于以下研究领域：1）基于肌电图的个人身份识别与验证的生物特征技术，以及2）基于肌电图的手势识别技术，用于神经康复和家庭应用。庞大的样本量将为研究提供充足的动力，尤其是在生物特征技术等应用中。此外，对于如手势识别的通用分类模型等独立于受试者的应用亦将大有裨益。多日记录将提供在更长时段内保持一致的结果，这对于基于肌电图的可穿戴设备的可靠性至关重要。