ABSTRACT
Supervised and unsupervised machine learning algorithms were explored for gait segmentation using wearable sensor platform. Multiple wearable sensors modules were placed at key locations: Four Inertial Measurement Units (IMUs) were attached to the thigh and shank of each leg and a plantar pressure measuring foot insoles were implanted in the shoes. The gait data has been collected from 10 people wirelessly via TCI-IP protocol, which is later anonymized. Further, the Ranchos Los Amigos (RLA) gait nomenclature-based data preprocessing and peak/valley detector based annotation steps are performed on the acquired data followed by implementation of machine learning techniques on the labeled datasets. The methods explored for phase and sub-phase classification includes the Unsupervised methods such as K-Means clustering and supervised methods like the Support Vector Machine (SVM) and Artificial Neural Network (ANN).