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Article

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

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Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
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Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404333, Taiwan
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Department of Mechanical Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia
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Department of Biomedical Engineering, Hungkuang University, Taichung 433304, Taiwan
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Department of Electrical Engineering, Yuan Ze University, Chungli 32003, Taiwan
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Department of Creative Product Design, Asia University, Taichung 41354, Taiwan
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Rehabilitation Engineering Lab, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
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Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
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Computational Science and Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
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Authors to whom correspondence should be addressed.
Academic Editors: Nunzio Cennamo, YangQuan Chen, Subhas Mukhopadhyay, M. Jamal Deen, Junseop Lee and Simone Morais
Sensors 2021, 21(19), 6513; https://doi.org/10.3390/s21196513
Received: 8 September 2021 / Revised: 25 September 2021 / Accepted: 26 September 2021 / Published: 29 September 2021
(This article belongs to the Topic Artificial Intelligence in Sensors)
Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations. View Full-Text
Keywords: artificial neural network; automatic classification; plantar region pressure image; walking speed; walking duration artificial neural network; automatic classification; plantar region pressure image; walking speed; walking duration
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MDPI and ACS Style

Chen, H.-C.; Sunardi; Liau, B.-Y.; Lin, C.-Y.; Akbari, V.B.H.; Lung, C.-W.; Jan, Y.-K. Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks. Sensors 2021, 21, 6513. https://doi.org/10.3390/s21196513

AMA Style

Chen H-C, Sunardi, Liau B-Y, Lin C-Y, Akbari VBH, Lung C-W, Jan Y-K. Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks. Sensors. 2021; 21(19):6513. https://doi.org/10.3390/s21196513

Chicago/Turabian Style

Chen, Hsing-Chung, Sunardi, Ben-Yi Liau, Chih-Yang Lin, Veit B.H. Akbari, Chi-Wen Lung, and Yih-Kuen Jan. 2021. "Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks" Sensors 21, no. 19: 6513. https://doi.org/10.3390/s21196513

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