In sensitive scenarios, such as meetings, negotiations, and team sports, messages must be conveyed without detection by non-collaborators. 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 different hand intentions without any gesture. Specifically, this work includes four parts: (1) the experimental system, consisting of the upper computer software, self-conducted myoelectric watch, and sports platform, is built to get sEMG signals and simulate multiple usage scenarios; (2) 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; (3) the toolbox integrates preprocessing methods (data segmentation, filter, normalization, etc.), commonly used sEMG signal decoding methods, and various plotting functions, to facilitate the research of the dataset; (4) the benchmark results of widely used methods are provided. The results involve single-day, cross-day, and cross-subject experiments of 6-class and 12-class gesture-free hand intention when subjects with different sports motions. To help future research, all data, hardware, software, and methods are open-sourced on the following website: click here.

Experimental system for sEMG-based gesture-free hand intention recognition. It comprises the self-conducted myoelectric wristband, the matched host computer software, and the Umay U3H sports platform. The myoelectric wristband is responsible for recording real-time sEMG signals; The host computer software has two pages, one is designed for data parameter setting and visualization, and another is for paradigm parameter settings and task prompts; The sports platform simulates different application scenarios.

The sEMG-based gesture-free hand intention recognition paradigm. Each experiment includes 12 blocks and each block includes 12 trials. The motion speed corresponds to each block and the hand force mode corresponds to each trial are different. The specific correspondence is shown in the table.

Dataset format. The dataset includes data from two experiments involving ten subjects. Each experiment records a source file of shape (T, C), where T and C denote the number of sampling points and channels respectively. There are a total of 15 channels, and the meaning of each channel is shown in the table.