sEMG-Based Gesture-Free Hand Intention Recognition: System, Dataset, Toolbox, and Benchmark Results

Hongxin Li, Jingsheng Tang, Yaru Liu, Xuechao Xu, Wei Dai, Junhao Xiao, Huimin Lu, Zongtan Zhou

Abstract: In sensitive scenarios, such as meetings, negotiations, and team sports, messages must be conveyed without detection by noncollaborators. Previous methods, such as encrypting messages, eye contact, and micro-gestures, had problems with either inaccurate information transmission or leakage of interaction intentions. To this end, a novel gesture-free hand intention recognition scheme was proposed, that adopted surface electromyography (sEMG) and isometric contraction theory to recognize hand intentions without any gesture. Specifically, this work includes four aspects: first, the experimental system, consisting of the self-conducted myoelectric wristband, the matched host computer software, and the sports platform, is built to get sEMG signals and simulate multiple usage scenarios; second, the paradigm is designed to standard prompt and collect the gesture-free sEMG datasets. Eight-channel signals of ten subjects were recorded twice per subject at about 5–10 days intervals; third, the toolbox integrates preprocessing methods (data segmentation, filter, normalization, etc.), widely used sEMG classification methods, and various plotting functions, to facilitate future research based this dataset; fourth, the benchmark results of widely used methods are provided. The results involve single-day, cross-day, and cross-subject experiments of six-class and 12-class gesture-free hand intention when subjects have different time windows.

External IDs:dblp:journals/tii/LiTLXDXLZ25