Journal article
Assessment of gait sensitivity norm as a predictor of risk of falling during walking in a neuromusculoskeletal model
Medical engineering & physics, v 35(10), pp 1483-1489
Oct 2013
PMID: 23669370
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Quantifying the risk of falling (falls risk) would be helpful in treating people with gait disorders. The gait sensitivity norm (GSN) is a stability measure that correlates well to risk of falling in passive dynamic walkers but has not been evaluated on humans or human-like walking models. We assessed the correlation of GSN to risk of falling in a neuromusculoskeletal (NMS) walking model. Specifically, we evaluated the correlation of GSN to the actual disturbance rejection (ADR) of the model and the sensitivity of this relationship to gait parameter, Poincaré section selection and steady state variability correction. Statistically significant results at p<0.05 were obtained for some of the gait indicators evaluated at the point in the gait cycle where they were most variable. The correlation between GSN and ADR was sensitive to gait indicator and Poincaré sections evaluated but not to steady state variability correction. The current work suggests some simple steps to reduce the sensitivity of GSN to arbitrary and subjective factors. Overall, the findings support the potential of GSN to be a clinically applicable measure of falls risk. Further study is required to identify methods to more definitively select the various factors within the GSN calculation and to confirm its ability to predict falls risk in human subjects.
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Details
- Title
- Assessment of gait sensitivity norm as a predictor of risk of falling during walking in a neuromusculoskeletal model
- Creators
- Sayed Naseel Mohamed Thangal - Drexel UniversityMukul Talaty - Gait and Motion Analysis Laboratory, Moss Rehab, Elkins Park, PA 19027, United StatesSriram Balasubramanian - Drexel University
- Publication Details
- Medical engineering & physics, v 35(10), pp 1483-1489
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000324605900012
- Scopus ID
- 2-s2.0-84883178372
- Other Identifier
- 991019167527904721
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- Web of Science research areas
- Engineering, Biomedical