Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning

نویسندگان

چکیده

Abstract Background Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders leading to balance difficulties and increased fall risks. This study aims investigate walking stability BPPV patients in clinical settings propose a machine-learning-based classification method for determining severity gait disturbances BPPV. Methods Twenty-seven outpatients twenty-seven healthy subjects completed level trials at self-preferred speed while wearing two accelerometers on head lower trunk, respectively. Temporo-spatial variables six related [root mean square (RMS), harmonic ratio (HR), variability, step/stride regularity, symmetry] derived from acceleration signals were analyzed. A support vector machine model (SVM) based developed differentiate controls classify handicapping effects dizziness imposed by Results The results showed that employed conservative significantly reduced compared controls. Significant different mediolateral HR trunk anteroposterior step regularity found among mild, moderate, severe DHI (dizziness handicap inventory) subgroups. SVM achieved promising accuracies with area under curve (AUC) 0.78, 0.83, 0.85 0.96 respectively differentiating classifying three stages Study suggest proposed analysis coupling wearable learning provides an objective approach assessing

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Benign paroxysmal positional vertigo.

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ژورنال

عنوان ژورنال: Journal of Neuroengineering and Rehabilitation

سال: 2021

ISSN: ['1743-0003']

DOI: https://doi.org/10.1186/s12984-021-00854-y