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Related Experiment Video

Updated: Sep 18, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Wearable Fall Detection System with Real-Time Localization and Notification Capabilities.

Chin-Kun Tseng^1,2,3, Shi-Jia Huang¹, Lih-Jen Kau¹

¹Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This summary is machine-generated.

A new wearable device uses a low-complexity algorithm and GPS/NB-IoT to accurately detect falls outdoors. This system ensures timely emergency response and enhances safety for elderly individuals during daily activities.

Keywords:

Global Positioning System (GPS)Narrowband Internet of Things (NB-IoT)fall detection inertial measurement unit (IMU)

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Last Updated: Sep 18, 2025

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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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Published on: February 8, 2019

Area of Science:

Wearable technology
Biomedical engineering
Gerontology

Background:

Existing fall detection systems struggle with real-world implementation due to lack of location awareness and computational complexity for edge devices.
Outdoor fall detection is critical for timely emergency response, but current solutions are often impractical for continuous use.
Wearable systems face limitations in computational resources and battery life, hindering long-term deployment.

Purpose of the Study:

To develop a portable, wearable fall detection device with location awareness for outdoor use.
To create a low-complexity algorithm suitable for edge devices with limited resources.
To enhance safety assurance for elderly individuals during outdoor activities.

Main Methods:

Integration of a microcontroller (MCU), inertial sensor, and a Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) chip module.
Implementation of a low-complexity finite-state machine algorithm for fall event detection and daily activity filtering.
Transmission of fall alerts and GPS coordinates via NB-IoT to a server for forwarding to a communication application.

Main Results:

The system accurately filtered eight types of daily activities and detected four types of falls (forward, backward, left, right).
Experimental tests in outdoor environments with 6750 samples (6000 daily activities, 750 falls) demonstrated high performance.
Achieved an average sensitivity of 97.9%, specificity of 99.9%, and overall accuracy of 99.7%.

Conclusions:

The developed portable, wearable device effectively detects falls outdoors with high accuracy and location awareness.
The low-complexity algorithm and integrated GPS/NB-IoT technologies enable practical, long-term outdoor use on resource-constrained edge devices.
This system significantly enhances safety assurance for elderly individuals, facilitating prompt emergency response during outdoor activities.