Studies show that 24% of the elderly suffer from cognitive impairment, have poor safety awareness and are at high risk of falls. Increase in the size of the elderly population, the decreasing supply of healthcare personnel and the emergence of chronic diseases due to changes in lifestyle are leading to escalation of healthcare costs. To tackle safety concerns of the elderly and to reduce medical expenditure by proactive personal health management, there is a need for a wearable continuous health monitoring and remote diagnostic system.
The objective of this project was achieved by implementing a wireless body sensor network (WBSN) between the custom-built compact modular hardware units containing medical grade sensors to acquire vital signs reliably and continuously. The system contains powerful ARM Cortex M4 microcontrollers and ATmega328P microcontrollers distributed among it’s individual units, for parallel processing of the physiological data. Basic signal processing algorithms were implemented to record ECG, body temperature, activity index as well as deduce parameters such as body posture, in real time. Equipped with Bluetooth®, the developed system is capable of wirelessly transmitting this data to laptops and smartphones.
While reduced hardware footprint and basic functionality has been achieved, the system requires work to be done in the signal processing domain to increase reliability of the data obtained. The system also requires a number of validation tests, standardization and calibration procedures before being deployed for clinical trials.
The data logged on the developed prototype can be used for the training of a pattern recognition system to predict abnormalities associated with certain specific activities and fall events, thereby assisting in effective diagnosis and rapid clinical intervention. With information presented intuitively to patients/next of kin, they can alter their lifestyle and lead a better quality of life with lesser visits to the hospital, resulting in a decrease in healthcare costs.
Keywords: Personal health management; Wireless Body Sensor Network (WBSN); wearable health monitoring; ARM Cortex M4; activity index; fall events.