Dataset of Surface Electromyographic (sEMG) Signals and Finger Kinematics

Accurate proportional myo-electric control of the hand is important in replicating dexterous manipulation in robot prostheses. Many studies in this field have focused on recording discrete hand gestures, while few have focused on the proportional and multiple-DOF control of the human hand using EMG signals. To aid researchers on advanced myoelectric hand control and estimation, we present this data from our work "Extraction of nonlinear muscle synergies for proportional and simultaneous estimation of finger kinematics". In our study, surface lectromyographic (sEMG) signals from the forearm and finger joint marker data were recorded from able-bodied subjects while they were tasked to do individual, simultaneous and random multiple finger flexion and extension movements. Included in this dataset are the EMG signals from 8 extrinsic muscles along the forearm, and as much as 23 joint markers attached on the hand obtained from 10 subjects. More description about the experimental protocol, signal processing methods and equipments used are described in the paper below. - S.K.Dwivedi, J. Ngeo, T.Shibata, Transaction of Biomedical Engineering, In Press, "Extraction of Nonlinear Synergies for Proportional and Simultaneous Estimation of Finger Kinematics."

精确的按比例手部肌电控制对于在机器人假肢中复制灵巧操作至关重要。众多研究聚焦于记录离散的手部手势，而关于利用肌电图（EMG）信号进行比例和多自由度手部控制的研究则相对较少。为辅助研究者开展高级肌电手控制与估计研究，我们在此展示来自我们工作“提取非线性肌肉协同作用以实现比例和同时性手指运动学估计”的数据。在我们的研究中，记录了健康受试者在进行单独、同时及随机多指屈伸运动时的前臂表面肌电图（sEMG）信号和手指关节标记数据。本数据集中包括8块沿前臂分布的外周肌群的EMG信号，以及来自10名受试者手部上的多达23个关节标记。关于实验方案、信号处理方法和所使用设备更详细的描述见下述论文。S.K.Dwivedi, J. Ngeo, T.Shibata, 生物医学工程学报，待刊发，“非线性协同作用提取以实现比例和同时性手指运动学估计。”