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Design and Analysis for Fall Detection System Simplification
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Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning

Farah Othmen1,2, Mouna Baklouti2, André Eugenio Lazzaretti3

  • 1Tunisia Polytechnic School, University of Carthage, La Marsa, Tunis 2078, Tunisia.

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Summary

This study introduces an energy-efficient, wearable fall detection system using an IoT architecture. The novel system achieves high accuracy and extends battery life for continuous monitoring, benefiting the elderly.

Keywords:
IoTSupervised Dictionary Learningelderly health careenergy efficientfall detectionwrist-based wearable device

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Area of Science:

  • Engineering
  • Computer Science
  • Gerontology

Background:

  • Falls are a significant health concern for the aging population.
  • Existing Internet of Things (IoT)-based fall detection systems face energy consumption challenges.
  • Wearable, wrist-based solutions offer comfort but require efficient power management.

Purpose of the Study:

  • To propose a novel, energy-aware IoT architecture for wearable fall detection.
  • To develop a gateway-less monitoring system using Message Queuing Telemetry Transport (MQTT).
  • To enhance fall detection accuracy and reduce energy consumption for long-term use.

Main Methods:

  • Implemented a hybrid double prediction technique based on Supervised Dictionary Learning.
  • Utilized a controlled dataset for offline training and real-world measurements for online validation.
  • Developed an energy-aware IoT architecture for MQTT-based, gateway-less monitoring.

Main Results:

  • Achieved high offline (99.8%) and online (91%) fall detection accuracy.
  • Demonstrated significant battery consumption optimization, extending working hours by a minimum of 27.32 hours.
  • Outperformed most related works relying solely on accelerometer data.

Conclusions:

  • The proposed system offers a promising solution for reliable, long-term, anywhere-anytime fall detection.
  • The energy-aware architecture addresses critical power consumption concerns in wearable IoT devices.
  • This approach enhances the feasibility of wearable fall detection for practical applications, especially for the elderly.