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A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking. / Li, Anna; Bodanese, Eliane; Poslad, Stefan et al.
In: IEEE Internet of Things Journal, 23.11.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Li, A, Bodanese, E, Poslad, S, Chen, P, Wang, J, Fan, Y & Hou, T 2023, 'A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking', IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2023.3336232

APA

Li, A., Bodanese, E., Poslad, S., Chen, P., Wang, J., Fan, Y., & Hou, T. (2023). A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking. IEEE Internet of Things Journal. Advance online publication. https://doi.org/10.1109/jiot.2023.3336232

Vancouver

Li A, Bodanese E, Poslad S, Chen P, Wang J, Fan Y et al. A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking. IEEE Internet of Things Journal. 2023 Nov 23. Epub 2023 Nov 23. doi: 10.1109/jiot.2023.3336232

Author

Li, Anna ; Bodanese, Eliane ; Poslad, Stefan et al. / A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking. In: IEEE Internet of Things Journal. 2023.

Bibtex

@article{466d13d5b1054f87bbe05815860f6c67,
title = "A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking",
abstract = "Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today{\textquoteright}s systems remain obtrusive, requiring users to wear devices, interfering with people{\textquoteright}s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR{\textquoteright}s practical utility for health monitoring.",
keywords = "Computer Networks and Communications, Computer Science Applications, Hardware and Architecture, Information Systems, Signal Processing",
author = "Anna Li and Eliane Bodanese and Stefan Poslad and Penghui Chen and Jun Wang and Yonglei Fan and Tianwei Hou",
year = "2023",
month = nov,
day = "23",
doi = "10.1109/jiot.2023.3336232",
language = "English",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

}

RIS

TY - JOUR

T1 - A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking

AU - Li, Anna

AU - Bodanese, Eliane

AU - Poslad, Stefan

AU - Chen, Penghui

AU - Wang, Jun

AU - Fan, Yonglei

AU - Hou, Tianwei

PY - 2023/11/23

Y1 - 2023/11/23

N2 - Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring.

AB - Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring.

KW - Computer Networks and Communications

KW - Computer Science Applications

KW - Hardware and Architecture

KW - Information Systems

KW - Signal Processing

U2 - 10.1109/jiot.2023.3336232

DO - 10.1109/jiot.2023.3336232

M3 - Journal article

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

ER -