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

• Published in: Biosensors (Basel) Vol. 9; no. 1; p. 29
• Main Authors: Nguyen, Tam Q, Young, Jonathan H, Rodriguez, Amanda, Zupancic, Steven, Lie, Donald Y C
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.02.2019; MDPI
• Subjects: Accelerometers; Classifiers; Complications; Cost analysis; Data collection; Datasets; Disorders; dynamic gait index (DGI) tests; fall prevention; fall-risk prediction; Falls; Feature extraction; Gait; Health sciences; Inertial platforms; Injuries; Injury prevention; Learning algorithms; Low cost; Machine learning; Patients; Principal components analysis; Quality of life; Real time; Remote sensors; Sensors; Support vector machines; Velocity; Vestibular system; Wearable technology; wireless gait analysis sensor (WGAS); United States > US; Texas; machine learning; wireless gait analysis sensor (WGAS); fall prevention; dynamic gait index (DGI) tests; fall-risk prediction
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios9010029

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.