Detecting and differentiating leg bouncing behaviour from everyday movements using tri-axial accelerometer data

Abstract

Leg bouncing is assumed to be related to anxiety, engrossment, boredom, excitement, fatigue, impatience, and disinterest. Objective detection of this behaviour would enable researching its relation to different mental and emotional states. However, differentiating this behaviour from other movements is less studied. Also, it is less known which sensor placements are best for such detection. We collected recordings of everyday movements, including leg bouncing, from six leg bouncers using tri-axial accelerometers at three leg positions. Using a Random Forest Classifier and data collected at the ankle, we could obtain a 90% accuracy in the classification of the recorded everyday movements. Further, we obtained a 94% accuracy in classifying four types of leg bouncing. Based on the subjects' opinion on leg bouncing patterns and experience with wearables, we discuss future research opportunities in this domain.