Using Deep Learning Methods to Predict Walking Intensity from Plantar Pressure Images

Hsing Chung Chen, Sunardi, Yih Kuen Jan, Ben Yi Liau, Chih Yang Lin, Jen Yung Tsai, Cheng Tsung Li, Chi Wen Lung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationAdvances 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
EditorsRavindra S. Goonetilleke, Shuping Xiong, Henrijs Kalkis, Zenija Roja, Waldemar Karwowski, Atsuo Murata
PublisherSpringer
Pages270-277
Number of pages8
ISBN (Print)9783030807122
DOIs
StatePublished - 2021
EventAHFE Conferences on Physical Ergonomics and Human Factors, Social and Occupational Ergonomics, and Cross-Cultural Decision Making, 2021 - Virtual, Online
Duration: Jul 25 2021Jul 29 2021

Publication series

NameLecture Notes in Networks and Systems
Volume273
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceAHFE Conferences on Physical Ergonomics and Human Factors, Social and Occupational Ergonomics, and Cross-Cultural Decision Making, 2021
CityVirtual, Online
Period7/25/217/29/21

Keywords

  • Diabetic foot ulcers
  • InceptionV3
  • MobileNets
  • Resnet50

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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