基于深度学习和超声波的肌肉力量自动定量评估

IF 2.5 4区 医学 Q1 ACOUSTICS
Ultrasonic Imaging Pub Date : 2024-09-01 Epub Date: 2024-06-16 DOI:10.1177/01617346241255590
Xiao Yang, Beilei Zhang, Ying Liu, Qian Lv, Jianzhong Guo
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引用次数: 0

摘要

骨骼肌是促进人体运动和保持姿势的重要器官。准确评估肌肉力量有助于为运动员的康复和力量训练提供有价值的见解。然而,传统技术在很大程度上依赖于操作者的专业知识,这可能会影响结果的准确性。在本研究中,我们提出了一种利用超声波和深度学习技术评估肌肉力量的自动化方法。我们收集了不同力量水平的多名运动员的肱二头肌 B 型超声波数据,然后用于训练我们的深度学习模型。为了评估该方法的有效性,本研究测试了肱二头肌在不同力量水平下的收缩情况。该方法对四级和六级肌力的分类准确率分别达到 98% 和 96%,总体平均准确率分别为 93% 和 87%。实验结果证实,本文的创新方法可以准确有效地评估和分类肌肉力量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound.

Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valuable insights for athletes' rehabilitation and strength training. However, traditional techniques rely heavily on the operator's expertise, which may affect the accuracy of the results. In this study, we propose an automated method to evaluate muscle strength using ultrasound and deep learning techniques. B-mode ultrasound data of biceps brachii of multiple athletes at different strength levels were collected and then used to train our deep learning model. To evaluate the effectiveness of this method, this study tested the contraction of the biceps brachii under different force levels. The classification accuracy of this method for grade 4 and grade 6 muscle strength reached 98% and 96%, respectively, and the overall average accuracy was 93% and 87%, respectively. The experimental results confirm that the innovative methods in this paper can accurately and effectively evaluate and classify muscle strength.

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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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