Wearable device for personalized EMG feedback-based treatments

Abstract

Curative effects of electromyography (EMG) feedback in treatment of various conditions and/or recovery after injuries have been earlier reported. However, wider application on EMG feedback is somehow limited due to the overall price of such systems and limited availability outside of the specialized treatment centers. Development of a personalized device for EMG feedback would be of great importance for home recovery after stroke or injuries, achieving better success in fitness and improving biofeedback-based treatments of conditions such as urinary incontinence. Despite extensive research on EMG signal collection, there is a lack of focus on in-situ EMG analysis that considers the intensity and duration of muscle activities. This gap presents the motivation for our research. In this paper, we present a methodology for the realization of wearable, rechargeable battery-powered, small-sized (90 mm × 60 mm) electronic device for recording two EMG channels (12-bits resolution, sampling frequency up to 1.6 kHz) with Bluetooth Low Energy connectivity to a smartphone. An average current consumption of 20.5 mA was experimentally determined, suggesting that multiday continuous functionality is possible. Advancing the state of the art, we propose a cross-correlation-based algorithm for in-situ dynamical computing and evaluation of muscle activation levels. This algorithm can determine if the muscle follows a predefined profile of contractions/relaxations (as needed for the treatment) and indicate if two muscles in a specific exercise were or were not engaged in proper time and with the proper intensity. The performed simulation analysis showed that the proposed approach exhibited shorter processing time compared to Morlet Wavelet Transform and Dynamic Time Warping. Finally, experimental work with five human volunteers demonstrated the reliability in EMG acquisition and signal processing. Therefore, the main contribution of this work is a cost-effective, small-sized, customizable EMG recording system with an efficient cross-correlation-based algorithm for in-situ EMG collection and processing.