Author Information
Asma Ahmed, McMaster University, CanadaShuning Wang, McMaster University, Canada
Mengmei Xu, McMaster University, Canada
Marjan Alavi, McMaster University, Canada
Abstract
Falls are a major cause of injury and disability among the elderly people which compromises their independence and overall quality of life. This paper presents an innovative Elderly Fall Detection and Assistance System by utilizing IoT technology for real-time monitoring and immediate response to fall events. The system is designed with a central processing unit and inertial sensors to monitor movement and orientation for fall detection. MQTT is being used for the wireless communication to deliver the alert notifications along with a user-friendly interface for smooth interaction. The fall detection algorithm identifies the sudden changes of the motion and the posture of the device which triggers the real-time alerts to caregivers including a visual indicator. Extensive testing has been done by simulating falls and routine activities which has exhibited the system’s reliability, minimized false positives, and ensured timely notifications. Moreover, this detection system can offer a cost-effective solution compared to many existing options on the market, with potential for future advancements in enhancing accuracy and adaptability.
Paper Information
Conference: AGen2025Stream: Frailty
This paper is part of the AGen2025 Conference Proceedings (View)
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To cite this article:
Ahmed A., Wang S., Xu M., & Alavi M. (2025) IoT-Enhanced Fall Detection System: Addressing the Needs of an Aging Population ISSN: 2432-4183 The Asian Conference on Aging & Gerontology 2025: Official Conference Proceedings (pp. 77-89) https://doi.org/10.22492/issn.2432-4183.2025.7
To link to this article: https://doi.org/10.22492/issn.2432-4183.2025.7
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