Daily Locomotor Movement Recognition with a Smart Insole and a Pre-Defined Route Map: Towards Early Motor Dysfunction Detection

Abstract

This paper proposes a method to auto-recognize nine types of daily activities from coherent movements with the use of a smart insole and a pre-designed Route Map. The Route Map creates a semi-controlled environment to help the subjects take actions comfortably and behave in experiments as they are in real life instead of following orders/commands. The nine types of highly similar activities are selected from the motor examination and the balance evaluation system test. Four supervised machine learning classifiers are evaluated and compared for classification performance. The results show that it is feasible to recognize these activities from daily activities and further extract parameters of interest from activity periods for long-term motor dysfunction detection.