A Wireless IoT System towards Fall Detection in Stroke Patient

In this paper, fall monitoring through the internet of things (IoT) is able to provide assessment of daily life .all existing system for predicting abnormality in fall detection related parameters. Their accuracy is limited because consequences due to injuries are significantly affected by different event in the fall. The objective of this study is to present a multisensory system that investigates walking patterns to predict a cautious gait in stroke patient .The proposed system can continuously monitors the stroke patient conditions and warn the user about abnormal fall and possibly save them from forthcoming injuries from fear of falling. In this project to detects the gaits we are using mems technology. Here the mems detects the mechanical movement and give digital values like front, back, left, right and fall detections and update the data into web page and also gives the buzzer alert and also analysis the abnormality through high and low pulse rate and temperature of the stroke person or normal person and update into web page and sent massage if any abnormal condition detected it will give buzzer alert.