ArticleApplied sciences (Basel, Switzerland)2024
How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression.
Article in Applied sciences (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Dynamic stability is tied to gait assistance when walking with a hip exoskeleton.Scientific reports · 2026Article
- Gait stability in peripheral artery disease: a phase-dependent analysis using gait tube methodology.Frontiers in rehabilitation sciences · 2026Article
- Revascularization Enhances Walking Dynamics in Patients with Peripheral Artery Disease.Applied mechanics (Basel, Switzerland) · 2025Article
- Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks.Biomimetics (Basel, Switzerland) · 2025Article
- Optimizing hip exoskeleton assistance pattern based on machine learning and simulation algorithms: a personalized approach to metabolic cost reduction.Frontiers in robotics and AI · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics-Lyapunov Exponents based on Rosenstein's algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf's algorithm (LyEW)-were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg-Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.