{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:50:11Z","timestamp":1778107811426,"version":"3.51.4"},"reference-count":93,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of the Republic of China","award":["MOST 110-2221-E-468-005"],"award-info":[{"award-number":["MOST 110-2221-E-468-005"]}]},{"name":"Ministry of Science and Technology of the Republic of China","award":["MOST 110-2221-E-155-039-MY3"],"award-info":[{"award-number":["MOST 110-2221-E-155-039-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 \u00b1 0.10\u00b0 vs. 5.86 \u00b1 0.09\u00b0, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 \u00b1 0.10\u00b0 vs. 6.07 \u00b1 0.06\u00b0, p &lt; 0.001), and ground-truth vs. YOLOv5x (5.58 \u00b1 0.10\u00b0 vs. 6.75 \u00b1 0.06\u00b0, p &lt; 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.<\/jats:p>","DOI":"10.3390\/s22072786","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T11:26:07Z","timestamp":1649157967000},"page":"2786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1492-7689","authenticated-orcid":false,"given":"Peter","family":"Ardhianto","sequence":"first","affiliation":[{"name":"Department of Visual Communication Design, Soegijapranata Catholic University, Semarang 50234, Indonesia"},{"name":"Department of Digital Media Design, Asia University, Taichung 413305, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raden Bagus Reinaldy","family":"Subiakto","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Airlangga University, Surabaya 60115, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0401-8473","authenticated-orcid":false,"given":"Chih-Yang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Yuan Ze University, Chung-Li 32003, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7149-4034","authenticated-orcid":false,"given":"Yih-Kuen","family":"Jan","sequence":"additional","affiliation":[{"name":"Rehabilitation Engineering Lab, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA"},{"name":"Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA"},{"name":"Computational Science and Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6857-8656","authenticated-orcid":false,"given":"Ben-Yi","family":"Liau","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Hungkuang University, Taichung 433304, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jen-Yung","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Digital Media Design, Asia University, Taichung 413305, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veit Babak Hamun","family":"Akbari","sequence":"additional","affiliation":[{"name":"Department of Creative Product Design, Asia University, Taichung 413305, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7048-2493","authenticated-orcid":false,"given":"Chi-Wen","family":"Lung","sequence":"additional","affiliation":[{"name":"Rehabilitation Engineering Lab, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA"},{"name":"Department of Creative Product Design, Asia University, Taichung 413305, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.bbe.2018.02.004","article-title":"Review on plantar data analysis for disease diagnosis","volume":"38","year":"2018","journal-title":"Biocybern. 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