Feature Selection using Random Forest Classifier for Foot Strike Event Detection in Toe Walkers

Automated Gait event identification of Foot Strike (FS) and Foot Off (FO) in pathological gait data, can be time saving in comparison to conventional manual annotations done currently. Identification of FS and FO allows breaking walking trials into gait cycles and hence aids in comparison of gait parameters like joint angles, forces and moments across gait cycles. Automated Gait Event Detection is also useful in development of wearable sensor devices and robotic systems that assist gait. Researchers have proposed several automatic gait event detection algorithms based on kinematic parameters and systematic study of the literature suggests specific parameters to have higher contribution in identification of FS event in all common pathological gait patterns. We used Random Forest Classifier Feature selection technique to identify high contributing features in FS event in toe walking pediatric pathological gait dataset and the results suggest high similarity in selected features by the machine learning technique with those suggested by popular event detection algorithms based on kinematic parameters for pathological gait. Hence we conclude that RFC feature selection is suitable for feature selection in toe walkers gait dataset for event detection purpose.