Machine Learning based Human Gait Segmentation with Wearable Sensor Platform.

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).