Movement Complexity and Falls
Author(s)
Lockhart, Thurmon
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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
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Date
2022-02-14
Extent
49:47 minutes
Resource Type
Moving Image
Resource Subtype
Lecture