Can Sonography Be Used to Estimate Deep Abdominal Muscle Activation in Different Static Arm Positions While Standing?

Crommert, Martin Eriksson; Unsgaard-Tøndel, Monica; Vasseljen, Ottar

doi:10.7863/ultra.15.12057

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…This method enhances the accuracy and comprehensiveness of the measurements. Muscle activation levels were measured using the muscle thickness change (%) [ 24 – 28 ]. This was defined as the percentage difference in PMaj thickness between the resting state and maximum contraction and was calculated using Eq.…”

Section: Methodsmentioning

confidence: 99%

Automated analysis of pectoralis major thickness in pec-fly exercises: evolving from manual measurement to deep learning techniques

Cai,

Lin,

Chen

et al. 2024

Vis. Comput. Ind. Biomed. Art

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This study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training.

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Section: Methodsmentioning

confidence: 99%