A Comparison of Day-Long Recording Stability and Muscle Force Prediction between BSN-Based Mechanomyography and Electromyography

摘要

Day-long continuous monitoring requires stable sensors that can minimise the effects of drift and maintain high accuracy and precision over time. We have recently shown that our inertial motion tracking system can capture stable kinematic data, calibrated against ground-truth over a long period of time. However, for many clinical and daily life activities, it is also essential to monitor the muscle-activity. In this study, we evaluate the long-term recording stability of our prototype mechanomyography (MMG) sensors as an extension to our existing ETHO1 body sensor network platform. We attached the MMG sensors along with commercial high-accuracy EMG electrodes on the arm muscles of 5 subjects throughout a working day of 9 hours. The subjects followed their daily routine but they had to perform a multi-level force-matching task through flexion and extension of their arm during four short sessions of the day, as measures of practical signal quality. We designed a force predictor that used either EMG or MMG signals to predict the forces generated by subjects. Our prototype low-cost MMG channels have shown comparable results (RMSE: 23N and R2: 0.91) in predicting the force levels applied when compared against the commercial high-accuracy EMG sensor (RMSE: 19N and R2: 0.95).