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Related Experiment Video

Updated: May 24, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Smartphone-Based Balance Assessment Using Machine Learning.

Marjan Nassajpour, Mustafa Shuqair, Amie Rosenfeld

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

|March 5, 2025

Summary

This summary is machine-generated.

This study uses smartphone accelerometers and machine learning to accurately assess balance, offering a convenient tool for elderly individuals and those in recovery. The method provides objective balance scores for improved at-home and remote health monitoring.

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Last Updated: May 24, 2025

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Area of Science:

Biomedical Engineering
Gerontology
Rehabilitation Science

Background:

Balance assessment is critical for fall prevention in the elderly and rehabilitation.
Current balance tests can be time-consuming, require specialized equipment, and lack objective, continuous monitoring.
Objective and accessible balance assessment tools are needed for widespread clinical and home use.

Purpose of the Study:

To develop and validate a smartphone-based method for objectively estimating Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB) scores.
To investigate the efficacy of machine learning algorithms in analyzing smartphone accelerometer data for balance assessment.
To provide a convenient and accessible tool for at-home and remote balance monitoring.

Main Methods:

Utilized smartphone accelerometer data and machine learning (XGBOOST) to estimate m-CTSIB scores.
Collected simultaneous data from 28 participants (aged 21-88) using smartphone sensors and a force plate system for ground truth.
Validated the algorithm's performance against gold-standard force plate measurements.

Main Results:

The XGBOOST algorithm achieved a high correlation (0.92) with ground truth m-CTSIB scores obtained from force plate data.
Demonstrated the feasibility of using smartphone sensors for objective balance assessment.
The developed methodology proved reliable and accurate in quantifying balance parameters.

Conclusions:

Smartphone-based balance assessment using machine learning is a reliable and objective method.
This technology offers a promising solution for accessible at-home and remote health monitoring.
The findings support the integration of this methodology into telemedicine for improved patient care and quality of life.