EventsThe 5th International Electronic Conference on Applied Sciences
Published
This submission belongs to the session S3. Computing and Artificial Intelligence of the event The 5th International Electronic Conference on Applied Sciences
Published date
04 Dec, 2024
Academic Editor
author-avatarFrancesco Dell'olio
Citation
Pedro C. Santana-Mancilla, Miguel Ángel Ruiz Torres, Abraham Antonio Carillo-Ramos, Néstor Azael Osorio Pérez, Raymundo Buenrostro-Mariscal, Fall detection assessment in older adults using a smart wearable device, in Proceedings of The 5th International Electronic Conference on Applied Sciences, 4 December–6 December 2024, MDPI: Basel, Switzerland
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Fall detection assessment in older adults using a smart wearable device

Miguel Ángel Ruiz Torres 1
Raymundo Buenrostro-Mariscal 1
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1. Universidad de Colima, Mexico, Mexico
Abstract

Introduction

Falls among older adults are a significant health concern as they can result in severe injuries or even death. This research aims to tackle this issue by creating a smart bracelet that utilizes Internet of Things (IoT) technology to detect falls and monitor the health parameters of aged individuals.

Methods

This project uses a quantitative research approach involving precise data analysis of sensor data from an accelerometer and a heart rate monitor. The bracelet incorporates an ESP32 microcontroller, a heart rate sensor, and an accelerometer. The device communicates via Wi-Fi using the MQTT protocol, sending data to a server for real-time monitoring and alerts. The bracelet was designed using 3D printing technology and assembled with lightweight, impact-resistant materials.

Results

The testing process involved both simulated and real-world scenarios to evaluate the bracelet's functionality. The accelerometer effectively detected falls by monitoring sudden changes in movement, while the heart rate sensor provided continuous health data. Most importantly, alerts were successfully transmitted to designated contacts via the IFTTT platform when a fall or abnormal health readings were detected, providing reassurance about the bracelet's effectiveness in emergencies. Data collected included heart rate variability and acceleration amplitude, confirming the device's accuracy in real-world conditions.

Conclusions

The smart bracelet successfully fulfilled the project's objectives by demonstrating reliable fall detection and health monitoring capabilities. While the device's physical design can be further refined, its functionality proved effective in preliminary tests. Future improvements will enhance sensor accuracy and user adaptability to ensure broader adoption among the senior population. The integration of IoT technology in healthcare devices shows promise in providing continuous and remote monitoring. With its potential to reduce the risks associated with falls among older adults, the smart bracelet shows potential in its impact on elderly care.

Keywords
Assistive technologies
Internet of Things
Wearable devices
Older adults
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