Implementation of Machine Learning and Deep Learning Algorithms with Dimensionality Reduction Methods for Internet of Things Gait Analysis and Monitoring Systems

Abstract

In this paper, we present an end-to-end monitoring system, which is used for patients who have foot or ankle impairments. This system has been created to help orthopedic doctors optimize treatment for patients recovering from foot and ankle injuries. The system consists of three main parts: a wearable controlled ankle motion (CAM) boot equipped with inertial and load sensors, a web application that provides visual feedback obtained from sensors, and the implementation of machine learning and deep learning to analyze walking activity and gait. Sensors used on the CAM boot include an accelerometer, a gyroscope, and load cells. Values from sensors attached to the CAM boot are sent wirelessly to the database. The web application takes sensor values from the database and returns visual feedback on the patient's walking patterns in the form of different graphs. The graphs can be used to analyze and determine abnormalities in the patient's gait and serve as a visual aid for patients during rehabilitation. Sensor values obtained from the database are used to train machine learning and deep learning models to recognize and differentiate between seven activities performed by the patient. We study and compare three dimensionality reduction methods and six classifiers. As a result, we find that the joint incorporation of the dimensionality reduction method of sparse principal component analysis (PCA) and the classifier random forest (RF) gives the best result with an accuracy of 99.5%.