Activity classification using foot contact force features from instrumented insoles

We examined the discriminant ability of features extracted from walking and running contact force data using instrumented insoles in order to optimize future classification model accuracy. Twenty-seven subjects walked and ran on four treadmills with varying deck stiffness at both self-selected and controlled speeds. After analysing 9 features, we found that stance time and log energy entropy are the most effective classification features.