Primary Faculty Mentor Name

Dr. Ryan McGinnis

Secondary Mentor NetID

asolomo1

Secondary Mentor Name

Andrew Solomon

Graduate Student Mentors

Reed Gurchiek, Lindsey Tulipani

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Mechanical Engineering

Primary Research Category

Engineering & Physical Sciences

Secondary Research Category

Engineering & Physical Sciences

Tertiary Research Category

Engineering & Physical Sciences

Presentation Title

Classifying Postural Features from Patients with Multiple Sclerosis

Time

9:00 AM

Location

Silver Maple Ballroom - Engineering & Physical Sciences

Abstract

For patients with Multiple Sclerosis (MS) the risk of falling due to their decreased mobility is a serious concern. One in two MS patients will experience a fall in any three-month period. These falls decrease a patient’s quality of life as well as impair their ability to perform physical therapy and remain active, both of which are effective treatments for persons with MS. Individuals with MS are hypothesized to exhibit different patterns of movement than healthy individuals. Analysis of walking and standing are of particular interest as the postural metrics associated with them are likely sensitive enough to differentiate MS patients from healthy individuals. 40 patients with multiple sclerosis performed in lab movement tests while recording accelerometer data from the medial chest and right anterior thigh sensor locations. These same patients recorded data for an additional 48 hour period at home. Using in lab and in home accelerometer data from these subjects, activity identification and postural metrics were calculated. Training a support vector machine (SVM) using a Gaussian kernel on accelerometer data achieved 99.85% accuracy, 99.79% sensitivity, 99.94% specificity, and 99.96% for identifying standing activity. Additional postural metrics (time domain, frequency domain and JERK measures) were quantified from accelerometer data to help predict fall risk.

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Classifying Postural Features from Patients with Multiple Sclerosis

For patients with Multiple Sclerosis (MS) the risk of falling due to their decreased mobility is a serious concern. One in two MS patients will experience a fall in any three-month period. These falls decrease a patient’s quality of life as well as impair their ability to perform physical therapy and remain active, both of which are effective treatments for persons with MS. Individuals with MS are hypothesized to exhibit different patterns of movement than healthy individuals. Analysis of walking and standing are of particular interest as the postural metrics associated with them are likely sensitive enough to differentiate MS patients from healthy individuals. 40 patients with multiple sclerosis performed in lab movement tests while recording accelerometer data from the medial chest and right anterior thigh sensor locations. These same patients recorded data for an additional 48 hour period at home. Using in lab and in home accelerometer data from these subjects, activity identification and postural metrics were calculated. Training a support vector machine (SVM) using a Gaussian kernel on accelerometer data achieved 99.85% accuracy, 99.79% sensitivity, 99.94% specificity, and 99.96% for identifying standing activity. Additional postural metrics (time domain, frequency domain and JERK measures) were quantified from accelerometer data to help predict fall risk.