A Lightweight IoT Healthcare Wearable for Fall Detection and Ambient Hazard Sensing
| aut.relation.endpage | 413 | |
| aut.relation.journal | Procedia Computer Science | |
| aut.relation.startpage | 406 | |
| aut.relation.volume | 280 | |
| dc.contributor.author | Sabit, Hakilo | |
| dc.date.accessioned | 2026-06-03T01:15:21Z | |
| dc.date.available | 2026-06-03T01:15:21Z | |
| dc.date.issued | 2026-06-02 | |
| dc.description.abstract | Rapid advancements in digital and embedded technologies have transformed modern healthcare, enabling innovative approaches to continuous patient monitoring. Caring for elderly individuals presents ongoing challenges, particularly when caregivers cannot remain physically present to provide support. This project addresses this need by developing an Internet of Things (IoT)-enabled wearable monitoring system capable of delivering real-time access to key health and environmental indicators. The proposed device integrates multiple sensors to monitor vital signs and safety-related events, including fall detection, and thermal comfort parameters. Detected events—such as abnormal temperature levels or sudden falls—trigger immediate alerts, ensuring timely intervention during emergencies. All sensor readings are transmitted to a web server, where data are processed and presented through an accessible dashboard for remote monitoring. This work demonstrates a proof-of-concept wearable platform designed to enhance caregiver awareness, improve responsiveness, and support safer independent living for elderly individuals. The system provides a foundation for future development in IoT-based healthcare monitoring solutions, offering the potential for scalable and continuous oversight of vulnerable populations. | |
| dc.identifier.citation | Procedia Computer Science, ISSN: 1877-0509 (Print), Elsevier BV, 280, 406-413. doi: 10.1016/j.procs.2026.04.052 | |
| dc.identifier.doi | 10.1016/j.procs.2026.04.052 | |
| dc.identifier.issn | 1877-0509 | |
| dc.identifier.uri | http://hdl.handle.net/10292/21312 | |
| dc.language | en | |
| dc.publisher | Elsevier BV | |
| dc.relation.uri | https://www.sciencedirect.com/science/article/pii/S1877050926010653 | |
| dc.rights | © 2026 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article. | |
| dc.rights.accessrights | OpenAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | 08 Information and Computing Sciences | |
| dc.subject | 10 Technology | |
| dc.subject | 46 Information and computing sciences | |
| dc.subject | IoT-healthcare | |
| dc.subject | wearable | |
| dc.subject | IoMT | |
| dc.subject | fall detection | |
| dc.subject | thermal comfort | |
| dc.subject | online caregiver awareness | |
| dc.title | A Lightweight IoT Healthcare Wearable for Fall Detection and Ambient Hazard Sensing | |
| dc.type | Journal Article | |
| pubs.elements-id | 762978 |
