Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation


High-density surface electromyography (HD-sEMG) is to record muscles' electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition is usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8x16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, csl-hdemg and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.

The CapgMyo database is a part of our sEMG-based gesture recognition project.

Gesture recognition based on deep domain adaptation. Fine-tuning is performed only when labeled calibration data are available.


    title={{Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation}},
    author={Du, Yu and Jin, Wenguang and Wei, Wentao and Hu, Yu and Geng, Weidong},
    publisher={Multidisciplinary Digital Publishing Institute}


This work was supported by a grant from the National Natural Science Foundation of China (No. 61379067) and the National Key Research and Development Program of China (No. 2016YFB1001300).