English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper concerns the fully automatic direct in vivo measurement of activeand passive dynamic skeletal muscle states using ultrasound imaging. Despitethe long standing medical need (myopathies, neuropathies, pain, injury,ageing), currently technology (electromyography, dynamometry, shear waveimaging) provides no general, non-invasive method for online estimation ofskeletal intramuscular states. Ultrasound provides a technology in which staticand dynamic muscle states can be observed non-invasively, yet currentcomputational image understanding approaches are inadequate. We propose a newapproach in which deep learning methods are used for understanding the contentof ultrasound images of muscle in terms of its measured state. Ultrasound datasynchronized with electromyography of the calf muscles, with measures of jointtorque/angle were recorded from 19 healthy participants (6 female, ages: 30 +-7.7). A segmentation algorithm previously developed by our group was applied toextract a region of interest of the medial gastrocnemius. Then a deepconvolutional neural network was trained to predict the measured states (jointangle/torque, electromyography) directly from the segmented images. Resultsrevealed for the first time that active and passive muscle states can bemeasured directly from standard b-mode ultrasound images, accurately predictingfor a held out test participant changes in the joint angle, electromyography,and torque with as little error as 0.022{\deg}, 0.0001V, 0.256Nm (root meansquare error) respectively.

The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle