Assessment of gait sensitivity norm as a predictor of risk of falling during walking in a neuromusculoskeletal model

Sayed Naseel Mohamed Thangal, Mukul Talaty, Sriram Balasubramanian

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1483-1489
Number of pages7
JournalMedical Engineering and Physics
Volume35
Issue number10
DOIs
StatePublished - Oct 1 2013

Fingerprint

Accidental Falls
Gait
Walking
Disturbance rejection
Walkers

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biomedical Engineering

Cite this

@article{c11137ddb491456995bb2d3f742ca001,
title = "Assessment of gait sensitivity norm as a predictor of risk of falling during walking in a neuromusculoskeletal model",
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, Poincar{\'e} 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 Poincar{\'e} 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.",
author = "Thangal, {Sayed Naseel Mohamed} and Mukul Talaty and Sriram Balasubramanian",
year = "2013",
month = "10",
day = "1",
doi = "10.1016/j.medengphy.2013.03.018",
language = "English (US)",
volume = "35",
pages = "1483--1489",
journal = "Medical Engineering and Physics",
issn = "1350-4533",
publisher = "Elsevier BV",
number = "10",

}

Assessment of gait sensitivity norm as a predictor of risk of falling during walking in a neuromusculoskeletal model. / Thangal, Sayed Naseel Mohamed; Talaty, Mukul; Balasubramanian, Sriram.

In: Medical Engineering and Physics, Vol. 35, No. 10, 01.10.2013, p. 1483-1489.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Assessment of gait sensitivity norm as a predictor of risk of falling during walking in a neuromusculoskeletal model

AU - Thangal, Sayed Naseel Mohamed

AU - Talaty, Mukul

AU - Balasubramanian, Sriram

PY - 2013/10/1

Y1 - 2013/10/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84883178372&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883178372&partnerID=8YFLogxK

U2 - 10.1016/j.medengphy.2013.03.018

DO - 10.1016/j.medengphy.2013.03.018

M3 - Article

C2 - 23669370

AN - SCOPUS:84883178372

VL - 35

SP - 1483

EP - 1489

JO - Medical Engineering and Physics

JF - Medical Engineering and Physics

SN - 1350-4533

IS - 10

ER -