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Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test.

Wojciech Tylman¹, Rafał Kotas¹, Marek Kamiński¹

¹Department of Microelectronics and Computer Science, Lodz University of Technology, 90-924 Lodz, Poland.

Sensors (Basel, Switzerland)

|March 6, 2021

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A new fast mobility test using wearable sensors accurately assesses fall risk. This low-cost system identifies individuals with elevated fall risk, outperforming traditional methods.

Area of Science:

Biomechanics
Medical Technology
Gerontology

Background:

Fall risk assessment is crucial for preventing injuries in older adults and individuals with balance impairments.
Traditional methods often lack objectivity and detailed movement analysis.
A novel approach using fast mobility testing and advanced technology is needed.

Purpose of the Study:

To introduce and evaluate a novel fall risk assessment approach utilizing a fast mobility test.
To develop and validate a low-cost, scalable system for automated body movement analysis.
To compare the efficacy of this new method against established fall risk assessment tools.

Main Methods:

Subjects performed a fast mobility test while wearing 1-7 micro-electro-mechanical inertial measurement units.

Keywords:

bioinformatics decision support systems fall risk assessment microsensors

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Body segment rotations and translations were computed using a body model to recreate movements in software.

An artificial neural network classified subjects into faller and non-faller categories based on test data.

Subject classification was validated against the Sensory Organization Test.

Main Results:

The system achieved an 85% true-positive ratio and a 63% true-negative ratio in classifying fallers.
Performance metrics for the fast mobility test surpassed those of the Timed Up and Go test.
The system demonstrated effectiveness across diverse age groups and included individuals with vestibular impairment.

Conclusions:

The fast mobility test, analyzed by a low-cost wearable system, offers a promising and accurate method for fall risk assessment.
This approach provides objective, detailed movement analysis, overcoming limitations of traditional observational methods.
The validated system has the potential to improve fall prevention strategies in clinical and research settings.