TY - GEN
T1 - Using Deep Learning Methods to Predict Walking Intensity from Plantar Pressure Images
AU - Chen, Hsing Chung
AU - Sunardi,
AU - Jan, Yih Kuen
AU - Liau, Ben Yi
AU - Lin, Chih Yang
AU - Tsai, Jen Yung
AU - Li, Cheng Tsung
AU - Lung, Chi Wen
N1 - Funding Information:
Acknowledgments. The Authors wish to express gratitude to Mr. Fityanul Akhyar, M.Sc. and Mr. Prayitno, M.T. for their assistance. This study was supported by the Ministry of Science and Technology of the Republic of China (MOST 108-2221-E-241-008, MOST-108-2221-E-468-018 and MOST 110-2218-E-468-001-MBK), Kuang Tien General Hospital and Hungkuang University (HK-KTOH-109-04), and Asia University Hospital and China Medical University Hospital (ASIA-107-AUH-09).
Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - People with diabetes are recommended to perform exercise such as brisk walking to maintain their health. However, a fast walking speed can increase plantar pressure, especially at the forefoot and rearfoot areas, thereby increasing the risk of diabetic foot ulcers (DFU). The deep learning model can identify plantar pressure patterns for an early detection of DFU when performing various intensities of exercise. Therefore, this study aimed to identify differences in walking speeds to the plantar pressure response using deep learning methods, including Resnet50, InceptionV3, and MobileNets. The deep learning models were used to classify the plantar pressure images of healthy people walking on a treadmill. The design consisted of three walking speeds (1.8 mph, 3.6 mph, and 5.4 mph). Through 5-fold cross-validation, accuracy, and robustness, the Resnet50 model had a better performance compared to the other two models in the image classification with a mean F1 score of 0.8646 and a standard deviation of 0.0466. The results indicated that the Resnet50 model can be used to analyze plantar pressure images for assessing risks of DFU.
AB - People with diabetes are recommended to perform exercise such as brisk walking to maintain their health. However, a fast walking speed can increase plantar pressure, especially at the forefoot and rearfoot areas, thereby increasing the risk of diabetic foot ulcers (DFU). The deep learning model can identify plantar pressure patterns for an early detection of DFU when performing various intensities of exercise. Therefore, this study aimed to identify differences in walking speeds to the plantar pressure response using deep learning methods, including Resnet50, InceptionV3, and MobileNets. The deep learning models were used to classify the plantar pressure images of healthy people walking on a treadmill. The design consisted of three walking speeds (1.8 mph, 3.6 mph, and 5.4 mph). Through 5-fold cross-validation, accuracy, and robustness, the Resnet50 model had a better performance compared to the other two models in the image classification with a mean F1 score of 0.8646 and a standard deviation of 0.0466. The results indicated that the Resnet50 model can be used to analyze plantar pressure images for assessing risks of DFU.
KW - Diabetic foot ulcers
KW - InceptionV3
KW - MobileNets
KW - Resnet50
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U2 - 10.1007/978-3-030-80713-9_35
DO - 10.1007/978-3-030-80713-9_35
M3 - Conference contribution
AN - SCOPUS:85112260095
SN - 9783030807122
T3 - Lecture Notes in Networks and Systems
SP - 270
EP - 277
BT - Advances in Physical, Social and Occupational Ergonomics - Proceedings of the AHFE 2021 Virtual Conferences on Physical Ergonomics and Human Factors, Social and Occupational Ergonomics, and Cross-Cultural Decision Making, 2021
A2 - Goonetilleke, Ravindra S.
A2 - Xiong, Shuping
A2 - Kalkis, Henrijs
A2 - Roja, Zenija
A2 - Karwowski, Waldemar
A2 - Murata, Atsuo
PB - Springer
T2 - AHFE Conferences on Physical Ergonomics and Human Factors, Social and Occupational Ergonomics, and Cross-Cultural Decision Making, 2021
Y2 - 25 July 2021 through 29 July 2021
ER -