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Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
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Using nonlinear dynamic analysis to differentiate fall status in older women
A Amirpourabasi1, S E Lamb2, J Y Chow3
1Department of Public Health and Sports Sciences, University of Exeter, Exeter, United Kingdom; School of Life and Medical Sciences, University of Hertfordshire, United Kingdom.
Gait & Posture
|November 7, 2025
Summary
Nonlinear dynamic analysis of gait using short-term Lyapunov exponents from ankle and trunk movements can identify fall risk in older women. Inertial measurement units offer a scalable approach for fall screening.
Area of Science:
- Gerontology
- Biomechanics
- Data Science
Background:
- Falls pose a significant health risk for older adults.
- Nonlinear dynamic (NLD) analysis of gait reveals gait variability and complexity, offering insights into fall risk.
- Methodological inconsistencies in NLD analysis have hindered its clinical application.
Purpose of the Study:
- To identify Nonlinear Dynamic (NLD) measures and data sources that effectively differentiate between older adults who fall and those who do not.
- To evaluate the utility of different NLD metrics and data collection methods for fall risk assessment.
Main Methods:
- Thirty-four healthy older women (17 fallers, 17 non-fallers) underwent gait analysis on a treadmill under various conditions.
- Kinematic data were acquired using both motion capture and lower-back inertial measurement units (IMUs).
- Gait complexity and stability were quantified using Multiscale Entropy and Lyapunov Exponents (LyE), with subsequent analysis using PCA, logistic regression, and LDA.
Main Results:
- Short-term LyE (SLyE) from trunk anterior-posterior acceleration and sagittal-plane ankle angles were the most effective in distinguishing fallers.
- Receiver Operating Characteristic (ROC) analysis indicated high accuracy for ankle SLyE (AUC up to 0.88) and moderate accuracy for trunk SLyE (AUC up to 0.77).
- A Linear Discriminant Analysis (LDA) model achieved 85% cross-validated accuracy, with 82% sensitivity and 88% specificity.
Conclusions:
- Short-term Lyapunov exponent (SLyE) derived from ankle motion and trunk acceleration serve as reliable indicators of fall history in older women.
- The comparable performance of IMU and motion capture data supports the use of IMU-based NLD metrics for scalable fall risk screening.
