Event Title

Activity Monitoring and Fall Detection Using Smart Phones in Individuals With Neurological Problems

Session Number

B03

Advisor(s)

Arun Jayaraman, Rehabilitation Institute of Chicago
Luka Lonini, Rehabilitation Institute of Chicago
Krishna Mummidisetty, Rehabilitation Institute of Chicago

Location

B-115

Start Date

28-4-2016 9:15 AM

End Date

28-4-2016 9:40 AM

Disciplines

Biomedical Engineering and Bioengineering

Abstract

This study focused on comparing three different leg braces that assist people with neurological disabilities namely micro-processor controlled knee-ankle-foot orthosis (C-Brace), stance-control knee- ankle-foot orthosis (SCO), and conventional knee-ankle-foot orthosis (KAFO). Each participant in the study wore accelerometer sensors along with a brace and was asked to perform activities of daily living (sitting, standing, climbing stairs, etc.) and simulated falls (in healthy participants and amputees) in a lab setting and at home. The data from the accelerometer sensors will be used to implement activity recognition using a smart phone (app in production) that has similar sensors. This application is useful in mining information related to a patient's community mobility and interactions to create specific rehabilitation plans.


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Apr 28th, 9:15 AM Apr 28th, 9:40 AM

Activity Monitoring and Fall Detection Using Smart Phones in Individuals With Neurological Problems

B-115

This study focused on comparing three different leg braces that assist people with neurological disabilities namely micro-processor controlled knee-ankle-foot orthosis (C-Brace), stance-control knee- ankle-foot orthosis (SCO), and conventional knee-ankle-foot orthosis (KAFO). Each participant in the study wore accelerometer sensors along with a brace and was asked to perform activities of daily living (sitting, standing, climbing stairs, etc.) and simulated falls (in healthy participants and amputees) in a lab setting and at home. The data from the accelerometer sensors will be used to implement activity recognition using a smart phone (app in production) that has similar sensors. This application is useful in mining information related to a patient's community mobility and interactions to create specific rehabilitation plans.