Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor
Open Access
- 26 February 2019
- journal article
- research article
- Published by MDPI AG in Biosensors
- Vol. 9 (1), 29
- https://doi.org/10.3390/bios9010029
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.Keywords
This publication has 20 references indexed in Scilit:
- Development and validation of an accelerometer-based method for quantifying gait eventsMedical Engineering & Physics, 2015
- A quantitative analysis of gait patterns in vestibular neuritis patients using gyroscope sensor and a continuous walking protocolJournal of NeuroEngineering and Rehabilitation, 2014
- An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) AlgorithmsOpen Journal of Applied Biosensor, 2014
- Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controlsIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005
- Exact indexing of dynamic time warpingKnowledge and Information Systems, 2005
- Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplastyMedical & Biological Engineering & Computing, 1999
- Support-vector networksMachine Learning, 1995
- The Development of the Dizziness Handicap InventoryJAMA Otolaryngology–Head & Neck Surgery, 1990
- Model-based vision: a program to see a walking personImage and Vision Computing, 1983
- LIII. On lines and planes of closest fit to systems of points in spaceThe London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901