Systematic Development of Machine for Abnormal Muscle Activity Detection

Abstract

Anomaly detection algorithms have vast applications, from fraud detection in business transactions to rare pattern detection in a production line to help prevent machinery failures. The availability of quantitative clinical data makes a compelling case for using anomaly detection algorithms in clinical settings, for instance, to help prevent diagnosis errors. This work evaluates the feasibility of using Isolation Forest algorithm for detection of spikes in surface electromyography (sEMG) of biceps and muscle resistive force in upper limb spasticity datasets. Results show that the anomaly detection in sEMG data is a good predictor for the occurrence of catch. It could be deployed in rehabilitation robotic systems for injury prevention by linking the anomaly detection to the actuation module exerting force in the system.