Steven Zupancic

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In this study, we report a custom designed wireless gait analysis sensor (WGAS) system for real-time fall detection using a Support Vector Machine (SVM) classifier. Our WGAS includes a tri-axial accelerometer, 2 gyroscopes and a MSP430 micro-controller. It was worn by the subjects at either the T4 or at the waist level for various intentional falls,(More)
Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, we took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic(More)
A comprehensive and quantified gait analysis is warranted for patients with balance disorders to prevent injury such as falls. We report here a custom-designed wireless-gait-analysis-sensor (WGAS) to perform functional gait analysis targeted for clinically evaluating balance disorders. We report here our first efforts to determine the optimal placements of(More)
BACKGROUND Because of its multifaceted nature, dizziness is difficult for clinicians to diagnose and manage independently. Current treatment trends suggest that patients are often referred to the otolaryngologist for intervention despite having a nonotologic disorder. Additionally, many individuals with atypical presentations are often misdiagnosed and(More)
Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients(More)
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