iBEST: intelligent Balance assessment and Stability Training system using smartphone

Area of Science:

• Rehabilitation Engineering
• Geriatric Medicine
• Digital Health

Background:

• Postural instability is a significant risk factor for falls and injuries in patients with balance disorders.
• Many individuals are unaware of their balance deficiencies, leading to delayed preventive interventions.
• There is a need for accessible and affordable solutions for continuous balance training and assessment.

Purpose of the Study:

• To introduce the intelligent Balance assessment and Stability Training (iBEST) system for convenient, anywhere-anytime balance training and assessment.
• To enable remote evaluation and management of patient recovery progress by therapists.
• To digitize and automate the Berg Balance Scale (BBS) using smartphone sensor technology.

Main Methods:

• Development of the iBEST system integrating smartphone sensors, cloud-based data analytics, and web applications.
• Implementation of sensorised automated balance assessment to digitize the Berg Balance Scale (BBS).
• Conducting an initial feasibility study to evaluate the system's performance.

Main Results:

• The iBEST system demonstrated high feasibility for remote balance training and assessment.
• Smartphone-based automated assessment achieved an average accuracy of 90.22% in classifying BBS test items.
• The system facilitates convenient and continuous monitoring of balance deficiencies.

Conclusions:

• The iBEST system offers a promising, affordable, and accessible solution for balance training and assessment.
• Leveraging smartphone technology can significantly improve the management of postural instability and reduce fall risks.
• Remote monitoring capabilities enhance therapeutic interventions and patient recovery tracking.