Improving the Robustness and Consistency of EMG using High-Density Electrode Arrays – Electrode Shift Analysis

As the future of rehabilitation-related professions depend increasingly on quantitative evidence supporting their efficacy, this has prompted a push to further integrate wearable health monitoring systems into consumer and medical fields. This would enable more robust and accessible remote recovery tracking opportunities, reducing the need for in-person clinician visits for patients managing neurological impairments like stroke. Additionally, this can help bridge the gap in quality-of-care between rural and urban areas while boosting patient motivation and providing a stronger foundation for clinician recommendations given the quantitative recovery metrics these systems would provide.

In response, we propose a novel wireless, wearable electromyography-based (EMG) system using fabric surface EMG (sEMG) sensors. This technology offers a non-invasive means of monitoring muscle activity, facilitating the extraction of measures such as muscle strength, time-to-fatigue, activation timings, and muscle co-contractions, causing sEMG to show promise in tracking the recovery from chronic movement disorders arising from stroke. Furthermore, with smart fabrics as a viable alternative to non-reusable electrodes, and with the proposed data acquisition and analysis system being small and lightweight, this will provide a smart clothing platform for health tracking that is convenient, accessible, and comprehensive in the data that it provides.

To achieve our goals, we must first address existing barriers to sEMG’s clinical adoption, particularly inter-session placement variations that limit longitudinal monitoring conclusions. Through this application, we will use pre-collected data from a healthy population to confirm the potential of our muscle mapping methodology to account for placement variations, determine the required spatial resolution of the wearable system that would employ the technology, validate the fabric sensors that would be used in the system, and extend the mapping algorithm to real-time shift tracking to support the analysis of dynamic exercises before future projects explore the work’s generalizability to a stroke population.

The intern, Fraser Douglas is a Biomedical Engineering PhD candidate at the University of British Columbia, specializing in high-density EMG wearables for stroke rehabilitation and prosthesis control. He is expected to gain several key competencies and skills through his work on this project.

Firstly, Fraser will develop research skill with the works focusing on characterizing the effect of distance on sEMG features indicative of neuromuscular recovery. This involves defining the maximum resolvable shift required for the shift detection algorithm. He will also enhance his skills in algorithm development by creating an HDsEMG shift detection algorithm based on underlying muscle activity maps, and adapting this algorithm to track muscle motion during dynamic exercise.
Throughout these tasks, Fraser will develop strong analytical skills and gain experience in using advanced EMG systems. He will also have multiple opportunities to produce academic outputs, including peer-reviewed journal articles and conference presentations. Specifically, these may arise from:
Detailed analyses of the impact of distance on feature values and sensor reattachment effects.
Descriptions of the inter-session shift detection algorithm and its application to continuous muscle tracking during dynamic exercises.
A technical report on the signal quality of Focal Lines’ dry electrodes compared to standard wet electrodes.
Fraser will also have the chance to write a thesis summarizing his work, contributing to his academic development.
Focal Lines, a start-up company developing unique dry surface electrodes designed to be integrated into textiles for use in a Muscular Skeleton Monitoring platform, enabling convenient at-home use, will benefit significantly from the project and Fraser’s work. His contributions will enhance the robustness and consistency of muscle activity measurements, supporting the viability of Focal Lines’ technology for continuous health monitoring. Fraser’s skills and the project outcomes will align closely, fostering his growth in the biomedical engineering field while advancing Focal Lines’ innovative health tech solutions.