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

• Published in: Computer methods and programs in biomedicine 2014-10, Vol.116 (3), p.311-318
• Main Authors: Yeh, Shih-Ching, Huang, Ming-Chun, Wang, Pa-Chun, Fang, Te-Yung, Su, Mu-Chun, Tsai, Po-Yi, Rizzo, Albert
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Applied sciences; Artificial Intelligence; Assessment; Biofeedback, Psychology - instrumentation; Biofeedback, Psychology - methods; Biological and medical sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Diagnosis, Computer-Assisted - instrumentation; Diagnosis, Computer-Assisted - methods; Dizziness - diagnosis; Dizziness - rehabilitation; Exact sciences and technology; Female; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Humans; Internal Medicine; Machine learning; Male; Medical sciences; Memory organisation. Data processing; Middle Aged; Nervous system (semeiology, syndromes); Neurology; Other; Pattern Recognition, Automated - methods; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Software; Technology. Biomaterials. Equipments. Material. Instrumentation; Therapy, Computer-Assisted - instrumentation; Therapy, Computer-Assisted - methods; Treatment Outcome; User-Computer Interface; Vestibular Diseases - diagnosis; Vestibular Diseases - rehabilitation; Vestibular dysfunction; Video Games; Virtual reality
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2014.04.014

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.