摘要：Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fbers . However, decoding dexterous body movements from sEMG signals is still quite challenging . In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles . Specifcally, twenty able-bodied participants performed 12 fnger movements under two paces and three arm postures . HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm . Also, a data-glove was used to record the fnger joint angles . Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured . The collected data samples were further employed for automated recognition of dexterous fnger movements . The introduced dataset ofers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic fnger movements . As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for fnger movement decoding .
