7346478249

Applications:
Clinical Research, Global Development, Human Subjects Trials and Intervention Studies, Precision Health
Methodologies:
Human-Computer Interaction, Machine Learning, Pattern Analysis and Classification, Real-time Data Processing, Signal Processing, Sparse Data Analysis, Time Series Analysis
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

Professor

Mechanical 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