Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time.

ABSTRACT:

Background and objective

Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' a

SUBMITTER: Rosa LG 

PROVIDER: S-EPMC8153491 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature