A Multithreading Queuing-Based sEMG Data Acquisition System for Rehabilitation with Real-Time Visualization

Background: Over the past two decades, myoelectric signals have been extensively used in rehabilitation technology and hybrid human-machine interfaces. A key challenge in creating self-engineered, cost-effective devices lies in acquiring reliable and accurate myoelectric signals. Additionally, identifying optimal anatomical sites for signal detection remains complex and is addressed in this study. Method: This applied research aims to tackle the outlined challenges through technological development and experimental testing. A Multi-Threading-based Queuing (MTQ) approach is proposed for real-time display and recording of muscle activity within a low-cost, multi-channel surface electromyography (sEMG) system. The technique was tested using raw (R) and feature (F) datasets via specialized classifiers to categorize sEMG signals from the silent utterance of English vowels captured from three facial muscles of a single healthy volunteer. Results: The proposed low-cost sEMG data acquisition technique, utilizing MTQ, achieved a mean classification accuracy of 0.91 for both R and F datasets, surpassing previous techniques for English vowel classification. Model 4, paired with low-cost hardware, attained a remarkable mean accuracy of 0.94, showing improvements between 14.6% and 74.07% over prior studies. Conclusion: The MTQ technique significantly enhances performance compared to existing configurations, suggesting that cost-effective sEMG data acquisition systems could replace commercial hardware in rehabilitation and human-machine interface applications.