{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:22:31Z","timestamp":1775856151885,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&amp;D program","award":["P0026190"],"award-info":[{"award-number":["P0026190"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer\u2019s gait and diagnose balance issues. This approach can be utilized to improve a user\u2019s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.<\/jats:p>","DOI":"10.3390\/s24154765","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T07:57:23Z","timestamp":1721721443000},"page":"4765","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure"],"prefix":"10.3390","volume":"24","author":[{"given":"Ho Seon","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seokjin","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Software, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2548-5214","authenticated-orcid":false,"given":"Jangkyum","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2195-6995","authenticated-orcid":false,"given":"Hyeonseok","family":"Seo","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun Kyun","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2478\/v10237-011-0036-5","article-title":"Review of methods for the evaluation of human body balance","volume":"19","author":"Panjan","year":"2010","journal-title":"Sport Sci. 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