{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:56:08Z","timestamp":1760241368091,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T00:00:00Z","timestamp":1515974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article presents a new data-driven model design for rendering force responses from elastic tool deformation. The new design incorporates a six-dimensional input describing the initial position of the contact, as well as the state of the tool deformation. The input-output relationship of the model was represented by a radial basis functions network, which was optimized based on training data collected from real tool-surface contact. Since the input space of the model is represented in the local coordinate system of a tool, the model is independent of recording and rendering devices and can be easily deployed to an existing simulator. The model also supports complex interactions, such as self and multi-contact collisions. In order to assess the proposed data-driven model, we built a custom data acquisition setup and developed a proof-of-concept rendering simulator. The simulator was evaluated through numerical and psychophysical experiments with four different real tools. The numerical evaluation demonstrated the perceptual soundness of the proposed model, meanwhile the user study revealed the force feedback of the proposed simulator to be realistic.<\/jats:p>","DOI":"10.3390\/s18010237","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T12:30:36Z","timestamp":1516019436000},"page":"237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Data-Driven Modeling and Rendering of Force Responses from Elastic Tool Deformation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6874-7782","authenticated-orcid":false,"given":"Arsen","family":"Abdulali","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruslan","family":"Rakhmatov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatyana","family":"Ogay","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seokhee","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/S1470-2045(09)70079-8","article-title":"The surgical learning curve for laparoscopic radical prostatectomy: A retrospective cohort study","volume":"10","author":"Vickers","year":"2009","journal-title":"Lancet Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s10916-016-0459-8","article-title":"A review of simulators with haptic devices for medical training","volume":"40","author":"Noguez","year":"2016","journal-title":"J. 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