{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:17:00Z","timestamp":1776115020449,"version":"3.50.1"},"reference-count":21,"publisher":"Cambridge University Press (CUP)","issue":"7","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>The study of dexterous manipulation has provided important insights into human sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions. Here, we describe a method using the deformation and color distribution of the fingernail and its surrounding skin to estimate the fingertip forces, torques, and contact surface curvatures for various objects, including the shape and material of the contact surfaces and the weight of the objects. The proposed method circumvents limitations associated with sensorized objects, gloves, or fixed contact surface type. In addition, compared with previous single finger estimation in an experimental environment, we extend the approach to multiple finger force estimation, which can be used for applications such as human grasping analysis. Four algorithms are used, c.q., Gaussian process, convolutional neural networks, neural networks with fast dropout, and recurrent neural networks with fast dropout, to model a mapping from images to the corresponding labels. The results further show that the proposed method has high accuracy to predict force, torque, and contact surface.<\/jats:p>","DOI":"10.1017\/s0263574719001383","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T08:14:00Z","timestamp":1569485640000},"page":"1242-1262","source":"Crossref","is-referenced-by-count":15,"title":["Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images"],"prefix":"10.1017","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7571-0742","authenticated-orcid":false,"given":"Nutan","family":"Chen","sequence":"first","affiliation":[]},{"given":"G\u00f6ran","family":"Westling","sequence":"additional","affiliation":[]},{"given":"Benoni B.","family":"Edin","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"van der Smagt","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"S0263574719001383_ref21","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.47"},{"key":"S0263574719001383_ref12","first-page":"3251","volume-title":"ICRA","author":"Sun","year":"2007"},{"key":"S0263574719001383_ref11","doi-asserted-by":"publisher","DOI":"10.1109\/WHC.2013.6548405"},{"key":"S0263574719001383_ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TOH.2015.2468229"},{"key":"S0263574719001383_ref8","doi-asserted-by":"publisher","DOI":"10.1109\/HAPTIC.2010.5444669"},{"key":"S0263574719001383_ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907310"},{"key":"S0263574719001383_ref6","volume-title":"IROS","author":"Chen","year":"2015"},{"key":"S0263574719001383_ref20","unstructured":"20. Bayer, J. , Osendorfer, C. , Korhammer, D. , Chen, N. , Urban, S. and van der Smagt, P. , \u201cOn Fast Dropout and Its Applicability to Recurrent Networks,\u201d In: Proc. ICLR (2014)."},{"key":"S0263574719001383_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2009.2032954"},{"key":"S0263574719001383_ref4","volume-title":"IROS","author":"Urban","year":"2013"},{"key":"S0263574719001383_ref1","first-page":"593","volume-title":"Sensorimotor Control of Manipulation","author":"Johansson","year":"2009"},{"key":"S0263574719001383_ref16","first-page":"1257","volume-title":"Advances in Neural Information Processing Systems","author":"Snelson","year":"2006"},{"key":"S0263574719001383_ref14","unstructured":"14. Comaniciu, D. , Ramesh, V. and Meer, P. , \u201cReal-Time Tracking of Non-Rigid Objects Using Mean Shift,\u201d In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 2 (IEEE, 2000) pp. 142\u2013149."},{"key":"S0263574719001383_ref17","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"S0263574719001383_ref2","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00322.2010"},{"key":"S0263574719001383_ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2008.925691"},{"key":"S0263574719001383_ref15","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen","year":"2006"},{"key":"S0263574719001383_ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TOH.2018.2803053"},{"key":"S0263574719001383_ref19","unstructured":"19. Wang, S. and Manning, C. , \u201cFast Dropout Training,\u201d In: Proceedings of the 30th International Conference on Machine Learning (ICML-2013) ( Dasgupta, S. and Mcallester, D. , eds.), vol. 28(2). JMLR Workshop and Conference Proceedings (2013) pp. 118\u2013126."},{"key":"S0263574719001383_ref18","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"Hinton","year":"2012","journal-title":"CoRR"},{"key":"S0263574719001383_ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2053043"}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574719001383","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T05:01:09Z","timestamp":1592456469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574719001383\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,26]]},"references-count":21,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["S0263574719001383"],"URL":"https:\/\/doi.org\/10.1017\/s0263574719001383","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"value":"0263-5747","type":"print"},{"value":"1469-8668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,26]]}}}