Review on Analysis and Optimization of Gait Stroke Detection using Variable Sensors with Machine Learning Approach

Abstract

Abstract: Gait monitoring is considered an important marker of impairment, disability and gait symmetry. This research aims to develop a real-time consumer health monitoring system based on IoT sensors and machine learning technology to detect health disorders such as the onset of stroke. The proposed consumer stroke prediction system consists of IoT-based gait monitoring sensors, real-time vital signs monitoring, and a machine learning-based disease prediction model that predicts impaired and healthy gait. This study is useful for post-stroke walking coordination rehabilitation and consumer health monitoring service.