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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

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Related Experiment Video

Updated: May 20, 2025

A Vibrotactile Feedback Device for Seated Balance Assessment and Training

A Vibrotactile Feedback Device for Seated Balance Assessment and Training

Published on: January 20, 2019

AI-driven balance evaluation: a comparative study between blind and non-blind individuals using the mini-BESTest.

Milagros Jaén-Vargas^1,2, Josué Pagán^3,4, Shiyang Li¹

¹Bioinstrumentation and Nanomedicine Laboratory, Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain.

Peerj. Computer Science

|March 26, 2025

Summary

Blind individuals

Keywords:

Artificial intelligence Balance Blind IMU OpenSense RT Quaternions mini-BESTest

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Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

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Published on: May 17, 2020

Area of Science:

Biomedical Engineering
Rehabilitation Science
Human Movement Science

Background:

Globally, 2.2 billion people have visual impairments, necessitating improved balance assessment and physical therapy.
The vestibular system is crucial for balance, and blind individuals often require specialized physical therapy.
Existing balance tests like the mini-BESTest have limitations, including potential physiotherapist bias and lack of application to blind populations.

Purpose of the Study:

To objectively evaluate the balance of blind individuals using the mini-BESTest and inertial measurement units (IMUs).
To identify specific activities within the mini-BESTest that best differentiate between blind and sighted individuals.
To develop machine learning models for objective balance assessment and tele-rehabilitation in visually impaired populations.

Main Methods:

Utilized the OpenSense RT device to collect IMU data from 29 blind and sighted participants.
Developed machine learning and deep learning models to predict mini-BESTest scores.
Conducted cluster analysis on inertial data and analyzed acceleration data for performance insights.

Main Results:

The one-legged stance was identified as the most distinguishing activity between blind and sighted individuals.
Analysis indicated potential inconsistencies between physiotherapist evaluations and test criteria.
Machine learning models achieved an 85.6% F1-score in predicting mini-BESTest scores for binary classification.

Conclusions:

IMU data and machine learning offer a more objective method for assessing balance in blind individuals compared to traditional methods.
The findings can inform the development of advanced tele-rehabilitation systems tailored for the visually impaired.
Objective balance assessment can enhance physical therapy outcomes and patient care for individuals with visual impairments.