1.
Vizitiu, Cristian; Bira, Calin; Dinculescu, Adrian; Nistorescu, Alexandru; Marin, Mihaela
In: SENSORS, vol. 21, no. 5, 2021.
Abstract | Links | BibTeX | Tags: e-Health; internet of things (IoT); elders; independent living; active and assisted living (AAL); systems engineering; biometric sensors; arduino; noncommunicable diseases (NCDs); choice reaction time (CRT)
@article{WOS:000628546300001,
title = {Exhaustive Description of the System Architecture and Prototype
Implementation of an IoT-Based eHealth Biometric Monitoring System for
Elders in Independent Living},
author = {Cristian Vizitiu and Calin Bira and Adrian Dinculescu and Alexandru Nistorescu and Mihaela Marin},
doi = {10.3390/s21051837},
year = {2021},
date = {2021-03-01},
journal = {SENSORS},
volume = {21},
number = {5},
publisher = {MDPI},
address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
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.},
keywords = {e-Health; internet of things (IoT); elders; independent living; active and assisted living (AAL); systems engineering; biometric sensors; arduino; noncommunicable diseases (NCDs); choice reaction time (CRT)},
pubstate = {published},
tppubtype = {article}
}
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.
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.