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Dissertation Defense

WHAT:
Stacy Morris:
"A Shoe-Integrated Sensor System for Wireless Gait Analysis and Real-Time Therapeutic Feedback"

WHEN:
Friday, April 16, 10:00 AM EST

WHERE:
Bartos Theatre, MIT Media Lab (E15)

DISSERTATION COMMITTEE:
Thesis Supervisor:
Joseph A. Paradiso
Sony Corporation Career Development Professor of Media Arts and Sciences
MIT Media Lab

Thesis Committee:
Neville Hogan
Professor
Mechanical Engineering and Brain and Cognitive Sciences, MIT

David Krebs
Professor and Director
The Massachusetts General Hospital Biomotion Laboratory

Rosalind Picard
Associate Professor of Media Arts and Sciences
MIT Media Lab

ABSTRACT:
Clinical gait analysis currently involves either an expensive analysis in a motion laboratory, using highly accurate, if cumbersome, optical systems, or a qualitative analysis with a physician or physical therapist making visual observations. There is a need for a low cost device that falls in between these two methods, which can provide quantitative and repeatable results. In addition, continuous monitoring of gait would be useful for real-time physical therapy and rehabilitation.

To free patients from the confines of a motion laboratory, this thesis has resulted in a wireless wearable system capable of measuring many parameters relevant to gait analysis. The extensive sensor suite includes three orthogonal accelerometers, and three orthogonal gyroscopes, four force sensors, two bi-directional bend sensors, two dynamic pressure sensors, as well as electric field height sensors. The "GaitShoe" was built to be worn on the shoes, without interfering with gait, and was designed to collect data unobtrusively, in any environment, and over long periods of time.

Subject testing of the GaitShoe was carried out on ten subjects with normal gait and five subjects with Parkinsonís disease. The calibrated sensor outputs were analyzed, and compared to results obtained simultaneously from The Massachusetts General Hospital Biomotion Lab; the GaitShoe proved highly capable of detecting heel strike and toe off, as well as estimating orientation and position of the subject. A wide variety of features were developed from the calibrated sensor outputs, for use with standard pattern recognition techniques to classify the gait of the subject. The results of the classification demonstrated the ability of the GaitShoe to identify the subjects with Parkinson's disease, as well as individual subjects. Real-time feedback methods were developed to investigate the feasibility of using the continuous monitoring of gait for physical therapy and rehabilitation.


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