Healthcare Research, Social Science
Machine Learning
Relevant Projects:

NSF DARE: Learning to Automatically Evaluate Pathological Gait: A Data-Driven System for Characterizing Disability and Informing Therapeutic Interventions; Precision Health: Automating and Personalizing Home-Based Balance Training

Kathleen Sienko


Mechanical Engineering

Arthur F Thurnau Professor, Professor of Mechanical Engineering and Director Academic Programs, Design Science Program, Department of Integrative Systems and Design, College of Engineering

Age- and sensory-related deficits in balance function drastically impact quality of life and present long-term care challenges. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. Effective balance-training programs require frequent visits to the clinic and/or the supervision of a physical therapist; however, one-on-one guided training with a physical therapist is not scalable for long-term balance training preventative and therapeutic programs. To enable preventative and therapeutic at-home balance training, we aim to develop models for automatically 1) evaluating balance and, 2) delivering personalized training guidance for community dwelling OA and people with sensory disabilities.

Smart Phone Balance Trainer

Accomplishments and Awards