title: Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system.
abstract: 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.
authors.affiliation: Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
authors.forename: Shih-Ching
authors.initials: SC
authors.lastname: Yeh
authors.affiliation: Department of Computer Science, University of California at Los Angles, USA.
authors.forename: Ming-Chun
authors.initials: MC
authors.lastname: Huang
authors.affiliation: Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan; Fu Jen Catholic University School of Medicine, New Taipei City, Taiwan; Department of Public Health, China Medical University, Taichung, Taiwan; School of Medicine, Taipei Medical University, Taipei, Taiwan. Electronic address: drtony@seed.net.tw.
authors.forename: Pa-Chun
authors.initials: PC
authors.lastname: Wang
authors.affiliation: Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan; Fu Jen Catholic University School of Medicine, New Taipei City, Taiwan.
authors.forename: Te-Yung
authors.initials: TY
authors.lastname: Fang
authors.affiliation: Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
authors.forename: Mu-Chun
authors.initials: MC
authors.lastname: Su
authors.affiliation: Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei, Taiwan.
authors.forename: Po-Yi
authors.initials: PY
authors.lastname: Tsai
authors.affiliation: Institute for Creative Technologies, University of Southern California, USA.
authors.forename: Albert
authors.initials: A
authors.lastname: Rizzo
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001676:Biofeedback, Psychology / Q000295:instrumentation / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D004244:Dizziness / Q000175:diagnosis* / Q000534:rehabilitation*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D013813:Therapy, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D016896:Treatment Outcome; D014584:User-Computer Interface; D015837:Vestibular Diseases / Q000175:diagnosis* / Q000534:rehabilitation*; D018910:Video Games
keywords: Assessment; Machine learning; Vestibular dysfunction; Virtual reality