Date of Award
Fall 2019
Document Type
Thesis
Degree Name
Master of Science (MS)
First Advisor
Ahamed, Sheikh Iqbal
Second Advisor
Love, Richard R.
Third Advisor
Kawsar, Ferdaus Ahmed
Abstract
In the United States, falls are one of the leading causes of fatal and non-fatal injuries for people of all ages. Current clinical methods to assess fall risk are impractical, and often do not use individuals’ actual performance. With current technological advances, and the Internet of Things (IoT), the tools are available to create a digital system that can take into account an individual’s actual performance in making a fall risk assessment. A digital insole based sensory computing system can collect and analyze human gait patterns to develop a fall risk assessment platform with great accuracy.The presented research considers current clinical methods and describes a computerized self-service platform that successfully addresses different gait variables and metrics critical to accurate fall risk assessment. The system incorporates a shoe insole with pressure sensors, and an accelerometer. Collected foot data are transferred to an analytics visualization platform. A wide range of gait pattern recognition metrics, and gait data analyses features are then displayed on the platform enabling specific fall risk assessment.