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Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
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A framework to automatically detect near-falls using a wearable inertial measurement cluster
Maximilian Gießler1,2, Julian Werth3, Bernd Waltersberger4
1Department of Mechanical and Process Engineering, Offenburg University of Applied Sciences, Offenburg, Germany. maximilian.giessler@hs-offenburg.de.
Communications Engineering
|December 16, 2024
Summary
This study introduces an automated framework using wearable sensors to detect near-falls by analyzing trunk movements. It accurately distinguishes between trips and slips, improving fall risk assessment outside the lab.
Area of Science:
- Biomechanics
- Wearable technology
- Gerontology
Background:
- Assessing fall risk is crucial for active lifestyles.
- Current methods for near-fall detection are limited.
- Remote monitoring of balance disturbances is needed.
Purpose of the Study:
- To develop an automated framework for detecting near-fall scenarios.
- To accurately assess trunk kinematics during locomotion.
- To differentiate between various balance disturbances.
Main Methods:
- Utilized a wearable inertial measurement cluster.
- Focused on trunk angular acceleration analysis.
- Incorporated individual gait characteristics into algorithms.
Main Results:
- The framework accurately distinguished between trips and slips.
- High sensitivity and specificity for automatic near-fall detection were achieved.
- Minimized false detections during daily activities.
Conclusions:
- The sensor-framework enables accurate remote fall risk assessment.
- It impacts both healthy and pathological populations.
- Offers insights into active lifestyle and fall risk interactions.
