How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression
Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith M. Burnfield · 2024
Read How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression by Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith M. Burnfield (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13520628.txt.
Description
Arash Gonabadi, Farahnaz Fallahtafti, and Judith Burnfield present a 2024 study on whether the wobble in a person's walk can predict how much energy walking costs. Ten healthy adults walked under thirteen conditions, some wearing hip exoskeletons. Six measures of gait irregularity were pulled from their movement data.
Two model types competed to predict metabolic cost from each measure: neural networks and standard linear regression. The networks won across the board. Two measures, one tracking long-range patterns in stride timing and one tracking local instability, gave the strongest predictions. The results suggest the nonlinear character of gait carries real information about effort, which could help tune assistive devices and rehab programs.
Catalog record
| CREATOR | Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith M. Burnfield |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |