Design and testing tool for a safe monitoring system for neurodegenerative disorder patients

This paper aims to develop a sensor based monitoring and analyzing system for Neuro-Degenerative Disorder patients (NDD); this may consist on SpO2 sensor, Electrophysiological sensors, NIBP, Motion Capture sensors and Eye Monitoring sensor, taking into consideration an acceptable cost for the whole system. Recorded data will be sent to an embedded decision making unit where detection, analysis, classification, prediction and action control will occur. The sensors and the decision making unit will be implemented in a comfortable jacket that doesn't affect the patients' movements and that can be used by several patients with reduced sensor placement alterations. The decision that can be made is creating a stimulus to avoid falling in case of sudden stop while moving, initiating an alarm, sending a notification to a mobile phone application, and/or telemedicine monitoring features. Artificial Neural Networks will be used to classify and predict the abnormal cases where action should be taken, and since the sensors will be continuously recording, it is possible to achieve continuous learning for the ANN as a first phase. Initial models will be defined by testing normal behaviors and some known abnormal behaviors or symptoms related to the electrical activity and other characteristics of the heart, oxygen saturation level, eye activity and body motion.