Binary classification of gait impairments using a capacitance-based sensor floor system

This work investigates to what extent it is possible to detect different gait restrictions compared to normal gait using a capacitive sensory floor. For this purpose, several gait parameters and a classification using Random Decision Forest (RDF) are calculated. Furthermore, the importance of the individual features for the different classes is analyzed using Recursive Feature Elimination (RFE). In this paper, different results are visible for the classification of single gaits, but results with an accuracy of up to 90.28% have been achieved.