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Related Experiment Video

Updated: Feb 2, 2026

Assessing the Effects of Music Listening on Psychobiological Stress in Daily Life

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Published on: February 2, 2017

Mobile Fall Risk Assessment Solution for Daily-Life Settings.

Heidi Simila, Milla Immonen, Timo Niemirepo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 17, 2018

Summary

This study presents a mobile app using a lower back sensor to assess fall risk during walking. The system effectively differentiates normal gait from abnormal gaits, paving the way for improved fall prevention strategies.

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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

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Published on: April 21, 2017

Area of Science:

Biomedical Engineering
Gerontology
Rehabilitation Science

Background:

Falls are a significant health concern, particularly for older adults.
Accurate fall risk assessment is crucial for effective prevention strategies.
Existing assessment methods may lack practicality for daily-life settings.

Purpose of the Study:

To introduce a novel mobile solution for fall risk assessment in everyday environments.
To develop an Android application utilizing acceleration sensor data for gait analysis.
To provide immediate feedback on fall risk to users.

Main Methods:

Development of an Android application integrating Bluetooth Low Energy (BLE) for sensor data reception.
Utilizing an accelerometer attached to the lower back to measure acceleration during a walk test.
Analysis of gait patterns, including normal, dragging, and slow gaits, in preliminary user tests.

Main Results:

The developed application successfully distinguished between normal gait and altered gaits (dragging, slow) based on acceleration features.
Preliminary feasibility was demonstrated in 12 healthy subjects.
The system shows potential for objective, real-time fall risk evaluation.

Conclusions:

The mobile fall risk assessment solution is feasible for distinguishing gait types.
Further data collection with older adults is necessary to refine application parameters for the target population.
This technology offers a promising tool for proactive fall prevention.