Abstract
Measures that can predict risk of falling are essential for enrollment of older adults into fall prevention programs. Local and orbital stability directly quantify responses to very small perturbations and are therefore putative candidates for predicting fall risk. However, research to date is not conclusive on whether and how these measures relate to fall risk. Testing this empirically would be time consuming or may require high risk tripping experiments. Simulation studies therefore provide an important tool to initially explore potential measures to predict fall risk. This study performed simulations with a 3D dynamic walking model to explore if and how dynamic stability measures predict fall risk. The model incorporated a lateral step controller to maintain lateral stability. Neuronal noise of increasing amplitude was added to this controller to manipulate fall risk. Short-term (λSλ) local instability did predict fall risk, but long-term (λLλ) local instability and orbital stability (maxFM) did not. Additionally, λSλ was an early predictor for fall risk as it started increasing before fall risk increased. Therefore, λSλ could be a very useful tool to identify older adults whose fall risk is about to increase, so they can be enrolled in fall prevention programs before they actually fall.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1514-1520 |
| Number of pages | 7 |
| Journal | Journal of Biomechanics |
| Volume | 44 |
| Issue number | 8 |
| DOIs | |
| State | Published - May 17 2011 |
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All Science Journal Classification (ASJC) codes
- Biophysics
- Orthopedics and Sports Medicine
- Biomedical Engineering
- Rehabilitation
Cite this
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Influence of simulated neuromuscular noise on the dynamic stability and fall risk of a 3D dynamic walking model. / Roos, Paulien E.; Dingwell, Jonathan.
In: Journal of Biomechanics, Vol. 44, No. 8, 17.05.2011, p. 1514-1520.Research output: Contribution to journal › Article
TY - JOUR
T1 - Influence of simulated neuromuscular noise on the dynamic stability and fall risk of a 3D dynamic walking model
AU - Roos, Paulien E.
AU - Dingwell, Jonathan
PY - 2011/5/17
Y1 - 2011/5/17
N2 - Measures that can predict risk of falling are essential for enrollment of older adults into fall prevention programs. Local and orbital stability directly quantify responses to very small perturbations and are therefore putative candidates for predicting fall risk. However, research to date is not conclusive on whether and how these measures relate to fall risk. Testing this empirically would be time consuming or may require high risk tripping experiments. Simulation studies therefore provide an important tool to initially explore potential measures to predict fall risk. This study performed simulations with a 3D dynamic walking model to explore if and how dynamic stability measures predict fall risk. The model incorporated a lateral step controller to maintain lateral stability. Neuronal noise of increasing amplitude was added to this controller to manipulate fall risk. Short-term (λSλ) local instability did predict fall risk, but long-term (λLλ) local instability and orbital stability (maxFM) did not. Additionally, λSλ was an early predictor for fall risk as it started increasing before fall risk increased. Therefore, λSλ could be a very useful tool to identify older adults whose fall risk is about to increase, so they can be enrolled in fall prevention programs before they actually fall.
AB - Measures that can predict risk of falling are essential for enrollment of older adults into fall prevention programs. Local and orbital stability directly quantify responses to very small perturbations and are therefore putative candidates for predicting fall risk. However, research to date is not conclusive on whether and how these measures relate to fall risk. Testing this empirically would be time consuming or may require high risk tripping experiments. Simulation studies therefore provide an important tool to initially explore potential measures to predict fall risk. This study performed simulations with a 3D dynamic walking model to explore if and how dynamic stability measures predict fall risk. The model incorporated a lateral step controller to maintain lateral stability. Neuronal noise of increasing amplitude was added to this controller to manipulate fall risk. Short-term (λSλ) local instability did predict fall risk, but long-term (λLλ) local instability and orbital stability (maxFM) did not. Additionally, λSλ was an early predictor for fall risk as it started increasing before fall risk increased. Therefore, λSλ could be a very useful tool to identify older adults whose fall risk is about to increase, so they can be enrolled in fall prevention programs before they actually fall.
UR - http://www.scopus.com/inward/record.url?scp=79955475813&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955475813&partnerID=8YFLogxK
U2 - 10.1016/j.jbiomech.2011.03.003
DO - 10.1016/j.jbiomech.2011.03.003
M3 - Article
C2 - 21440895
AN - SCOPUS:79955475813
VL - 44
SP - 1514
EP - 1520
JO - Journal of Biomechanics
JF - Journal of Biomechanics
SN - 0021-9290
IS - 8
ER -