Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model
Alessandro Mengarelli, Andrea Tigrini, Mara Scattolini, Rami Mobarak, Laura Burattini, Sandro Fioretti, Federica Verdini · 2024
Read Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model by Alessandro Mengarelli, Andrea Tigrini, Mara Scattolini, Rami Mobarak, Laura Burattini, Sandro Fioretti, Federica Verdini (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-11645049.txt.
Description
Alessandro Mengarelli and co-authors test a way to gauge walking forces from muscle signals in 2024. The push of the foot against the ground guides balance and forward drive. Powered aids could walk more naturally if they used that force in their control loop. The team asked whether leg muscle signals alone could predict the downward part of the force.
They compared two deep learning models fed with signals from thigh and shin muscles. The models tracked the force while people walked freely, without lab limits. Good tracking would let aids react to each step in real time. The study supports more natural and comfy walking for people who use such devices.
Catalog record
| CREATOR | Alessandro Mengarelli, Andrea Tigrini, Mara Scattolini, Rami Mobarak, Laura Burattini, Sandro Fioretti, Federica Verdini |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |