Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network

作者：Qunfeng Niu, Lei Shi, Yang Niu, Kunming Jia, Guangxiao Fan, Ranran Gui, Li Wang · 发表于：Heliyon · 年份：2024 · DOI：10.1016/j.heliyon.2024.e26763 · 被引用次数：12 · 研究领域：Muscle activation and electromyography studies、EEG and Brain-Computer Interfaces、Gaze Tracking and Assistive Technology

The key to sEMG (surface electromyography)-based control of robotic hands is the utilization of sEMG signals from the affected hand of amputees to infer their motion intentions. With the advancements in deep learning, researchers have successfully developed viable solutions for CNN (Convolutional Neural Network)-based gesture recognition. However, most studies have primarily concentrated on utilizing sEMG data from the hands of healthy subjects, often relying on high-dimensional feature vectors obtained from a substantial number of electrodes. This approach has yielded high-performing sEMG recognition systems but has failed to consider the considerable inconvenience that the abundance of electrodes poses to the daily lives and work of patients. In this paper, we focused on transradial amputees and used sEMG data from the Ninapro DB3 database as our dataset. Firstly, we introduce a STFT (Short-Time Fourier Transform)-based time-frequency feature fusion map for sEMG. This map includes both time-frequency features and the time-frequency localization of sEMG signals. Secondly, we propose an Improved DenseNet (Dense Convolutional Network) model for recognizing motion intentions in the affected hand of amputees based on their sEMG signals. Finally, addressing the issue of optimizing the number of electrodes carried by amputees, we introduce the PCMIRR (Pearson Correlation and Motion Intention Recognition Rate) algorithm. This algorithm optimizes the number of channels by considerin...