Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system

Shih Ching Yeh, Ming Chun Huang, Pa Chun Wang, Te Yung Fang, Mu Chun Su, Po Yi Tsai, Albert Rizzo

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Background and objective: Dizziness is a major consequence of imbalance and vestibular dysfunction. Compared to surgery and drug treatments, balance training is non-invasive and more desired. However, training exercises are usually tedious and the assessment tool is insufficient to diagnose patient's severity rapidly. Methods: An interactive virtual reality (VR) game-based rehabilitation program that adopted Cawthorne-Cooksey exercises, and a sensor-based measuring system were introduced. To verify the therapeutic effect, a clinical experiment with 48 patients and 36 normal subjects was conducted. Quantified balance indices were measured and analyzed by statistical tools and a Support Vector Machine (SVM) classifier. Results: In terms of balance indices, patients who completed the training process are progressed and the difference between normal subjects and patients is obvious. Conclusions: Further analysis by SVM classifier show that the accuracy of recognizing the differences between patients and normal subject is feasible, and these results can be used to evaluate patients' severity and make rapid assessment.

Original languageEnglish
Pages (from-to)311-318
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume116
Issue number3
DOIs
StatePublished - Oct 2014

Keywords

  • Assessment
  • Machine learning
  • Vestibular dysfunction
  • Virtual reality

Fingerprint

Dive into the research topics of 'Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system'. Together they form a unique fingerprint.

Cite this