Exhaustive Description of the System Architecture and Prototype Implementation of an IoT-Based eHealth Biometric Monitoring System for Elders in Independent Living

Abstract

In this paper, we present an exhaustive description of an extensible e-Health Internet-connected embedded system, which allows the measurement of three biometric parameters: pulse rate, oxygen saturation and temperature, via several wired and wireless sensors residing to the realm of Noncommunicable Diseases (NCDs) and cognitive assessment through Choice Reaction Time (CRT) analysis. The hardware used is based on ATMEGA AVR + MySignals Hardware printed circuit board (Hardware PCB), but with multiple upgrades (including porting from ATMEGA328P to ATMEGA2560). Multiple software improvements were made (by writing high-level device drivers, text-mode and graphic-mode display driver) for increasing functionality, portability, speed, and latency. A top-level embedded application was developed and benchmarked. A custom wireless AT command firmware was developed, based on ESP8266 firmware to allow AP-mode configuration and single-command JavaScript Object Notation (JSON) data-packet pushing towards the cloud platform. All software is available in a git repository, including the measurement results. The proposed eHealth system provides with specific NCDs and cognitive views fostering the potential to exploit correlations between physiological and cognitive data and to generate predictive analysis in the field of eldercare.

Biometrics in the Era of COVID-19: Challenges and Opportunities

“…On the other hand, the need for further digitalisation in almost all societal levels, including sensitive applications such as online exams or eHealth systems, where subject identification is of the utmost importance, has increased the acceptance of biometric technologies as a convenient and reliable means of authentication. Thus, more research is being done in this area [152], [153], together with socioeconomic analysis of success and failure of big-scale implementations of such systems [154].…”

A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes

“…Furthermore, Ponciano et al used this wearable device to identify the mobility's initiation [51], whereas the ECG and Galvanic Skin Response (GSR) signals were collected with the BITalino (r)evolution kit platform trying to unravel the impact of using personality assessments [52]. On the other hand, Mysignals was used to develop eHealth embedded systems for monitoring elderly health based on the Internet of Things (IoT) and Fog computing [53] for calculating specific biometric markers related to predictive analysis in the field of eldercare, such as the connection between noncommunicable diseases and cognitive impairments [54], and to generate vital signs and data for medical patients [55]. Lastly, many studies adopt Raspberry Pi, a low-cost computing platform that is profitably applied in the field of IoT and embedded systems [56].…”