GT Virtual Neuro Seminar Series
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Abstract:
In this presentation, previously funded NSF/BRAIN projects related to validating a wearable fall risk assessment tool will be discussed. These studies investigated capabilities of using a wearable sensor and, extracted linear and nonlinear gait/posture/heartrate variables along with a machine learning approach to predict fall risks among varied populations at risk of falls. The results indicate that the use of both linear and nonlinear variables can increase fall risk prediction accuracy, sensitivity, and specificity. Fall risk assessment methods estimate the probability of future falls through the identification of predictive fall risk factors.
Lockhart, T.E., Soangra, R., Yoon, H. et al. Prediction of fall risk among community-dwelling older adults using a wearable system. Sci Rep 11, 20976 (2021). https://doi.org/10.1038/s41598-021-00458-5
Thurmon Lockhart, PhD, More Foundation Professor of Life in Motion, School of Biological and Health Systems Engineering, Arizona State University.
Dr. Thurmon Lockhart is the Inaugural 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 at Mayo Clinic College of Medicine, Division of Endocrinology. Professor Lockhart’s research focuses on the identification of injury mechanisms and quantification of sensorimotor deficits and movement disorders associated with aging and neurological disorders on fall accidents utilizing wearable biosensors and nonlinear dynamics. His research interest includes wearable biomedical devices, gait and posture, chaos and ergonomics.
https://news.asu.edu/20210712-new-collaboration-advances-science-fall-prevention
Event Location:
https://gatech.bluejeans.com/824485104