title: Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation.
abstract: 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 has 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 8 × 16 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.
authors.affiliation: State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China. answeror@zju.edu.cn.
authors.forename: Yu
authors.initials: Y
authors.lastname: Du
authors.affiliation: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China. jinguang@zju.edu.cn.
authors.forename: Wenguang
authors.initials: W
authors.lastname: Jin
authors.affiliation: State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China. wentaowei@zju.edu.cn.
authors.forename: Wentao
authors.initials: W
authors.lastname: Wei
authors.affiliation: State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China. yudeshui@zju.edu.cn.
authors.forename: Yu
authors.initials: Y
authors.lastname: Hu
authors.affiliation: State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China. gengwd@zju.edu.cn.
authors.forename: Weidong
authors.initials: W
authors.lastname: Geng
mesh_terms: D004566:Electrodes; D004576:Electromyography; D005868:Gestures; D006801:Humans; D018482:Muscle, Skeletal; D010363:Pattern Recognition, Automated
keywords: domain adaptation; electromyography; gesture recognition; muscle-computer interface