Discrimination of vestibular function based on inertial sensors

Vestibular dysfunction, as a common disease or symptom, can cause abnormalities in gait and balance. Since the existing detection methods are static detection and cannot obtain the dynamic vestibular information of patients, this paper proposes a simple method for detecting vestibular dysfunction based on gait signals of subjects.

In our study, the walking patterns of dynamic gait index (DGI) and inertial sensor were adopted for the data acquisition. Time-domain, frequency-domain and non-linear features were extracted from inertial sensor signals. Then the Relief algorithm was used for feature selection. Two classifiers, Support Vector Machine (SVM) and Random Forest (RF), were used to classify the patients with vestibular dysfunction and the healthy controls.

The highest accuracy of 84.79% was achieved based on magnetometer features and SVM classifier. To further improve classification results, features of three sensor signals were combined and applied to two classifiers. Combined features and RF classifier achieved a classification accuracy of 86.5%.

The detection of vestibular dysfunction based on inertial sensors might be simple, accurate and easy to implement in clinical examination, which provides a new method for the clinical diagnosis of vestibular function.