Creator of adorable robots


Detection of mobility problems in geriatric patients through Machine Learning

Collaborators: Chris Gutierrez, Dr. Mark Williams

In the summer of 2007, I was chosen for an NSF Research Experience for Undergraduates at the University of Virginia focusing on computing in medicine. In a joint venture with the Department of Computer Sciences and School of Medicine, I spearheaded a project titled "Portable, Inexpensive, and Unobtrusive Accelerometer-based Geriatric Gait Analysis." Collaborating closely with a gerontologist, Dr. Mark Williams, we attached wireless accelerometers to the ankles, wrists, and waists of geriatric patients and recorded their walking movements. Using signal processing and supervised machine learning techniques, we were able to detect diseases such as Alzheimer's, spastic hemiparesis, and spastic paraparesis with surprising accuracy. We also developed GaitMate, a tool for aiding physicians in using this machine learning data for diagnosis in clinical gait analysis. Dr. Williams has continued to build on my work, and plans to release a commercial version in the near future. Applications of this research include prediction and confirmation of geriatric disorders, telemedicine, and long-term analysis.

Relevant Technologies

MATLAB, IMUs, Supervised Machine Learning

Supplemental Information