Intelligent Autonomous Elderly Patient Home Monitoring System

This paper presents the implementation of an intelligent home-based elderly patient monitoring system. Four patient's physiological parameters are being continuously monitored, namely, temperature, glucose, and 3D accelerometer and gyroscope data for fall detection. Contextual sensors are mounted across the home to observe the patient's surrounding environment such as temperature and humidity. All sensors, wearable and contextual, transmit their measured data to smart gateways (fog layer) via nRF communication protocol. At the fog layer, diverse functions are being carried out, from collected measurements transfer to health care providers for further processing and analysis via Internet (cloud layer), sending push notifications and reports to patient's mobile phone, to alerting ambulance or civil defence authorities in case of an emergency. To insure power autonomy and eliminate the need for frequent sensor node battery replacement, an efficient thermal energy harvesting system is developed. Associated with a boost converter, the thermal energy harvesting system is able to sustain 3.3 OCV leveraging a temperature difference of 20°C between patient's body and room temperature, while achieving an efficiency of 82.6%.