HEALTH MONITORING SYSTEM FOR SENIOR CITIZENS BASED ON GAIT VELOCITY AND STRIDE LENGTH

Abstract

Gait velocity and stride length are acute health indicators for older adults. A decade of medical research shows that Gait speed is the sixth vital sign of health. Though still these metrics are measured only during medical check-ups which lessens the opportunities to intervene and detect changes timely in the impairment process. The objective ofthis project is to develop health monitoring system to predict health state on gait speed and stride length. This project uses the Convolutional Neural Network technique to develop the system. The main advantage of using this technique is that it provides features extraction and detection that is suitable for image recognition. Model of convolutional neural network and Error-back propagation algorithm was used due to its’ capability of forming internal representations of features in classification. After trials and errors, a suitable set oftraining parameters are define and network structure is created. The system first proceeds with the pre-process ofthe captured image with threshold, inverting and smoothing. Filtering, segmentation, resizing and features extraction are also performed in the process. Next, the feed forward process through the network is invoked to yield an output matrix. Based on the output matrix, the health state can be determined. This system is designed to customize the network according to the network data