A Convolutional Neural Network Model to Classify the Effects of Vibrations on Biceps Muscles

Jen Yung Tsai, Yih Kuen Jan, Ben Yi Liau, Raden Bagus Reinaldy Subiakto, Chih Yang Lin, Rimuljo Hendradi, Yi Chuan Hsu, Quanxin Lin, Hsin Ting Chang, Chi Wen Lung

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)

摘要

Muscle fatigue occurs after sports activities, repeated actions in a routine job, or a heavy-duty job. It causes soreness and reduces performance in athletes and workers. Various therapies have been developed to reduce muscle fatigue. Vibration therapy has been used to reduce muscle fatigue and delay muscle soreness. However, its effectiveness remains unclear. Ultrasound images provide a non-invasive diagnosis and instant visual examinations. However, it requires extensive training to analyze ultrasound images. The purpose of this study was to develop an automated classification system of ultrasound images using deep learning to assist clinical diagnosis. The ultrasound images of the biceps muscle were measured from four healthy people. The primary objective of the study was to use the convolutional neural network (CNN) models to classify between the vibration control condition (0 Hz) and vibration test conditions (5, 35, and 50 Hz) with subjects in different time duration the pattern (2 and 10-min). These images were preprocessed to resize to 224 × 224 pixels and augmentation to feed into the dataset, including the augmentation training dataset (74%), validation dataset (15%), and non-augmentation test dataset (11%). This study used the AlexNet, VGG-16, and VGG-19 of CNN models for recognition and classification ultrasound images. These models compared the differences of ultrasound images of biceps after various vibration between two conditions. The results showed that AlexNet has the best performance with the accuracy 82.5%, sensitivity 67.3%, and specificity 99.5% when 10-min 35 Hz local vibration was applied. The deep learning method, AlexNet, shows the potential for automated classification of biceps ultrasound images for assessing treatment outcomes of vibration therapy.

原文???core.languages.en_GB???
主出版物標題Advances in Physical, Social and Occupational Ergonomics - Proceedings of the AHFE 2020 Virtual Conferences on Physical Ergonomics and Human Factors, Social and Occupational Ergonomics and Cross-Cultural Decision Making
編輯Waldemar Karwowski, Ravindra S. Goonetilleke, Shuping Xiong, Richard H.M. Goossens, Atsuo Murata
發行者Springer
頁面56-62
頁數7
ISBN(列印)9783030515485
DOIs
出版狀態已出版 - 2020
事件AHFE Virtual Conference on Physical Ergonomics and Human Factors, the Virtual Conference on Social and Occupational Ergonomics, and the Virtual Conference on Cross-Cultural Decision Making, 2020 - San Diego, United States
持續時間: 16 7月 202020 7月 2020

出版系列

名字Advances in Intelligent Systems and Computing
1215 AISC
ISSN(列印)2194-5357
ISSN(電子)2194-5365

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???event.eventtypes.event.conference???AHFE Virtual Conference on Physical Ergonomics and Human Factors, the Virtual Conference on Social and Occupational Ergonomics, and the Virtual Conference on Cross-Cultural Decision Making, 2020
國家/地區United States
城市San Diego
期間16/07/2020/07/20

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