Real-Time Forecasting and Classification of Trunk Muscle Fatigue Using Surface Electromyography

Low Back Pain (LBP) affects the vast majority of the population at some point in their lives. People with LBP show altered trunk muscle activity and enhanced fatigability of trunk muscles is associated with the development and future risk of LBP. Therefore, a system that can forecast trunk muscle activity and detect fatigue can help subjects, practitioners and physiotherapists in the diagnosis, monitoring and recovery of LBP. In this paper, we present a novel approach in order to determine whether subjects are fatigued, or transitioning to fatigue, 25 seconds ahead of time using surface Electromyography (sEMG) from 14 trunk muscles. This is achieved using a three-step approach: A) extracting features related to fatigue from sEMG, B) forecasting the features using a real-time adaptive filter and C) performing dimensionality reduction (from 70 to 2 features) and then classifying subjects using a supervised machine learning algorithm. The forecasting classification accuracy across 13 patients is 99.1% +/- 0.004 and the area under the micro and macro ROC curve is 0.935 +/- 0.036 and 0.940 +/- 0.034 as determined by 10-fold cross validation. The proposed approach enables a computationally efficient solution which could be implemented in a wearable device for preventing muscle injury.