TY - GEN
T1 - A Convolutional Neural Network Model to Classify the Effects of Vibrations on Biceps Muscles
AU - Tsai, Jen Yung
AU - Jan, Yih Kuen
AU - Liau, Ben Yi
AU - Subiakto, Raden Bagus Reinaldy
AU - Lin, Chih Yang
AU - Hendradi, Rimuljo
AU - Hsu, Yi Chuan
AU - Lin, Quanxin
AU - Chang, Hsin Ting
AU - Lung, Chi Wen
N1 - Publisher Copyright:
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Deep learning
KW - Skeletal muscle fatigue
KW - Ultrasound images
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U2 - 10.1007/978-3-030-51549-2_8
DO - 10.1007/978-3-030-51549-2_8
M3 - Conference contribution
AN - SCOPUS:85088585085
SN - 9783030515485
T3 - Advances in Intelligent Systems and Computing
SP - 56
EP - 62
BT - 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
A2 - Karwowski, Waldemar
A2 - Goonetilleke, Ravindra S.
A2 - Xiong, Shuping
A2 - Goossens, Richard H.M.
A2 - Murata, Atsuo
PB - Springer
T2 - 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
Y2 - 16 July 2020 through 20 July 2020
ER -