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Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Internet of things based multi-sensor patient fall detection system.

Sarah Khan¹, Ramsha Qamar¹, Rahma Zaheen¹

¹Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates.

Healthcare Technology Letters

|December 17, 2019

Summary

This summary is machine-generated.

This study introduces a wearable device for remote patient fall detection in hospitals. The system accurately identifies falls and daily activities, alerting staff to prevent serious complications.

Keywords:

Bayes methods Internet of Things Internet of things based multisensor patient fall detection system accelerometers biomedical equipment body sensor networks cost-effective integrated system credit card-sized single board microcomputer geriatrics gyroscopes k-nearest neighbour medical signal processing microcomputers naive Bayes' classifiers nearest neighbour methods nonfall motions classification patient monitoring pattern classification sensor data visual-based classifier

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Area of Science:

Biomedical Engineering
Computer Science
Gerontology

Background:

Accidental patient falls are a significant concern in healthcare settings, leading to severe complications.
Existing fall detection methods may be costly or lack comprehensive activity classification.

Purpose of the Study:

To develop and evaluate a cost-effective, integrated wearable system for remote patient fall detection.
To classify non-fall motions into activities of daily living (ADLs).

Main Methods:

A wearable device integrating a camera, gyroscope, and accelerometer connected to a microcomputer.
Utilizing a visual-based classifier for camera data and k-Nearest Neighbour (k-NN) and Naïve Bayes classifiers for sensor data.
Real-time fall detection and notification to hospital attendants.

Main Results:

The system achieved 95% accuracy in classifying fall versus non-fall activities.
The integrated system demonstrated effectiveness in remote patient monitoring.

Conclusions:

The proposed wearable system offers a promising solution for timely fall detection and ADL classification in hospitals.
This technology can enhance patient safety and reduce the severity of fall-related injuries.