Thurmon Lockhart



Ira A. Fulton Schools of Engineering
School of Biological and Health Systems Engineering
Arizona State University

Additional Information: 
Faculty page
Lab website


Thurmon Lockhart is the MORE Foundation Professor of Life in Motion professor in the biomedical engineering program in the School of Biological Health and Systems Engineering at Arizona State University. He is also a guest professor at Ghent University in Belgium and serves as a research affiliate faculty member with Mayo Clinic College of Medicine, Division of Endocrinology. Previously, Lockhart was a professor at Virginia Tech in the Industrial and Systems Engineering Department and with the Virginia Tech/Wake Forest School of Biomedical Engineering and Science (2000-2014). 

Professor Lockhart’s research and publications concern the identification of injury mechanisms and quantification of sensorimotor deficits and movement disorders associated with aging and neurological disorders on fall accidents. His academic grounding in biomechanical modeling, nonlinear dynamics, human postural control, gait mechanics, and wearable biosensor design underscore a fundamental capacity to provide unique clinical solutions to injury preventions utilizing both engineering and biomedical principles.  As a result of above initiatives, Lockhart has published five book chapters and more than 200 full-length manuscripts in a variety of journals and proceedings. Professor Lockhart was an editor for Ergonomics (2010-2016) and is currently an associate editor of the Annals of Biomedical Engineering (Springer) and Editorial Board of the Ergonomics (Taylor & Francis), Academic Editor of the Sensorsand Board of Consulting Editors of the Journal of Biomechanics (Elsevier). Lockhart is the editor-in-chief for Wearable Biomedical Systems section of the newly created journal – Sci.


  • Fall risk prediction and assessments
  • Movement Disorders (e.g., Parkinson’s disease, Stroke, Ataxia)
  • Gait and Posture, Locomotion and Postural Control, and Nonlinear Dynamics
  • Wireless wearable sensors for continuous, non-invasive gait monitoring to accurately detect and study fall events and predict future falls in the elderly population
  • Interventions (nutrition/exercise) to reduce falls in older adults
  • Occupational fall prevention training