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Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

1
Department of Otolaryngology, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
2
Department of Electrical & Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
3
School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
4
Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biosensors 2019, 9(1), 29; https://doi.org/10.3390/bios9010029
Received: 27 December 2018 / Revised: 2 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue Feature Papers: State-of-the-Art Biosensors Technology 2018)
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Abstract

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls. View Full-Text
Keywords: dynamic gait index (DGI) tests; fall-risk prediction; fall prevention; wireless gait analysis sensor (WGAS); machine learning dynamic gait index (DGI) tests; fall-risk prediction; fall prevention; wireless gait analysis sensor (WGAS); machine learning
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Nguyen, T.Q.; Young, J.H.; Rodriguez, A.; Zupancic, S.; Lie, D.Y. Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor. Biosensors 2019, 9, 29.

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