Article
Estimation of Various Walking Intensities Based on Wearable
Plantar Pressure Sensors Using Artificial Neural Networks
Hsing-Chung Chen
1,2
, Sunardi
1,3
, Ben-Yi Liau
4
, Chih-Yang Lin
5
, Veit Babak Hamun Akbari
6
,
Chi-Wen Lung
6,7,
* and Yih-Kuen Jan
7,8,9,
*
Citation: 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
Academic Editors: Nunzio Cennamo,
YangQuan Chen,
Subhas Mukhopadhyay, M.
Jamal Deen, Junseop Lee and
Simone Morais
Received: 8 September 2021
Accepted: 26 September 2021
Published: 29 September 2021
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4.0/).
1
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan;
cdma2000@asia.edu.tw or shin8409@ms6.hinet.net (H.-C.C.); sunardi@umy.ac.id (S.)
2
Department of Medical Research, China Medical University Hospital, China Medical University,
Taichung 404333, Taiwan
3
Department of Mechanical Engineering, Universitas Muhammadiyah Yogyakarta,
Yogyakarta 55183, Indonesia
4
Department of Biomedical Engineering, Hungkuang University, Taichung 433304, Taiwan; byliau@hk.edu.tw
5
Department of Electrical Engineering, Yuan Ze University, Chungli 32003, Taiwan;
andrewlin@saturn.yzu.edu.tw
6
Department of Creative Product Design, Asia University, Taichung 41354, Taiwan; 109711569@live.asia.edu.tw
7
Rehabilitation Engineering Lab, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
8
Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
9
Computational Science and Engineering, University of Illinois at Urbana-Champaign,
Champaign, IL 61820, USA
* Correspondence: cwlung@asia.edu.tw (C.-W.L.); yjan@illinois.edu (Y.-K.J.)
Abstract:
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.
Keywords:
artificial neural network; automatic classification; plantar region pressure image; walking
speed; walking duration
1. Introduction
Walking has been universally recommended as a rehabilitation strategy to improve
physical and psychological health in people with Parkinson’s disease [
1
], diabetes mellitus
(DM), and peripheral arterial disease [
2
,
3
]. Regarding the characterization of the walking
Sensors 2021, 21, 6513. https://doi.org/10.3390/s21196513 https://www.mdpi.com/journal/sensors
