
pmid: 16617617
Balance dysfunctions are common, especially among elderly people. Present methods for the diagnosis and evaluation of severity of dysfuntion have limited value. We present a system that makes it easy to implement different visual and mechanical perturbations for clinical investigations of balance and visual-vestibular interaction. The system combines virtual reality visual stimulation with force platform posturography on a moving platform. We evaluate our contruction's utility in a classification task between 33 healthy controls and 77 patients with Ménière's disease, using a series of tests with different visual and mechanical stimuli. Responses of patients and controls differ significantly in parameters computed from stabilograms. We also show that the series of tests achieves a classification accuracy slightly over 80% between controls and patients.
Posture, Reproducibility of Results, Sensitivity and Specificity, Pattern Recognition, Automated, User-Computer Interface, Artificial Intelligence, Physical Stimulation, Humans, Diagnosis, Computer-Assisted, Postural Balance, Meniere Disease, Photic Stimulation
Posture, Reproducibility of Results, Sensitivity and Specificity, Pattern Recognition, Automated, User-Computer Interface, Artificial Intelligence, Physical Stimulation, Humans, Diagnosis, Computer-Assisted, Postural Balance, Meniere Disease, Photic Stimulation
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