{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:03:39Z","timestamp":1762956219830},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030342548"},{"type":"electronic","value":"9783030342555"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-34255-5_24","type":"book-chapter","created":{"date-parts":[[2019,11,5]],"date-time":"2019-11-05T19:11:49Z","timestamp":1572981109000},"page":"330-336","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Ranking Robot-Assisted Surgery Skills Using Kinematic Sensors"],"prefix":"10.1007","author":[{"given":"Bur\u00e7in Buket","family":"O\u011ful","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias Felix","family":"Gilgien","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P\u0131nar Duygulu","family":"\u015eahin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,4]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Burges, C.J., Shaked, T., Renshaw, E., et al.: Learning to rank using gradient descent. In: International Conference on Machine Learning, pp. 89\u201396 (2005)","key":"24_CR1","DOI":"10.1145\/1102351.1102363"},{"doi-asserted-by":"crossref","unstructured":"Doughty, H., Damen, D., Mayol-Cuevas, W.: Who\u2019s better? Who\u2019s best? Pairwise deep ranking for skill determination. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","key":"24_CR2","DOI":"10.1109\/CVPR.2018.00634"},{"issue":"1","key":"24_CR3","doi-asserted-by":"publisher","first-page":"e1850","DOI":"10.1002\/rcs.1850","volume":"14","author":"MJ Fard","year":"2018","unstructured":"Fard, M.J., Ameri, S., Darin, E.R., et al.: Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int. J. Med. Robot. Comput. Assist. Surg. 14(1), e1850 (2018)","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"24_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-030-00937-3_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Ismail Fawaz","year":"2018","unstructured":"Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Evaluating surgical skills from kinematic data using convolutional neural networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 214\u2013221. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_25"},{"doi-asserted-by":"crossref","unstructured":"Funke, I., Mees, S.T., Weitz, J., Speidel, S.: Video-based surgical skill assessment using 3D convolutional neural networks. arXiv preprint arXiv:1903.02306 (2019)","key":"24_CR5","DOI":"10.1007\/s11548-019-01995-1"},{"unstructured":"Gao, Y., Vedula, S.S., Reiley, C.E., et al.: JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modelling. In: MICCAI Workshop (2014)","key":"24_CR6"},{"key":"24_CR7","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1080\/110241502320127739","volume":"168","author":"TP Grantcharov","year":"2002","unstructured":"Grantcharov, T.P., Bardram, L., Funch-Jensen, P., et al.: Assessment of technical surgical skills. Eur. J. Surg. 168, 139\u2013144 (2002)","journal-title":"Eur. J. Surg."},{"key":"24_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/11550907_126","volume-title":"Artificial Neural Networks: Formal Models and Their Applications \u2013 ICANN 2005","author":"A Graves","year":"2005","unstructured":"Graves, A., Fern\u00e1ndez, S., Schmidhuber, J.: Bidirectional LSTM networks for improved phoneme classification and recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadro\u017cny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 799\u2013804. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11550907_126"},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, Y., Cai, M., Sato, Y.: Manipulation-skill assessment from videos with spatial attention network. arXiv preprint arXiv:1901.02579 (2019)","key":"24_CR10","DOI":"10.1109\/ICCVW.2019.00539"},{"key":"24_CR11","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1002\/bjs.1800840237","volume":"84","author":"J Martin","year":"1997","unstructured":"Martin, J., Regehr, G., Reznick, R., et al.: Objective structured assessment of technical skill (OSATS) for surgical residents. Br. J. Surg. 84, 273\u2013278 (1997)","journal-title":"Br. J. Surg."},{"issue":"4","key":"24_CR12","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1007\/s00464-018-6079-2","volume":"32","author":"BS Peters","year":"2018","unstructured":"Peters, B.S., Armijo, P.R., Krause, C., et al.: Review of emerging surgical robotic technology. Surg. Endosc. 32(4), 1636\u20131655 (2018)","journal-title":"Surg. Endosc."},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Fey, A.I.: SATR-DL: improving surgical skill assessment and task recognition in robot-assisted surgery with deep neural networks. In: IEEE Conference of the Engineering in Medicine and Biology Society, pp. 1793\u20131796 (2018)","key":"24_CR13","DOI":"10.1109\/EMBC.2018.8512575"},{"key":"24_CR14","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s11548-018-1860-1","volume":"13","author":"Z Wang","year":"2018","unstructured":"Wang, Z., Fey, A.M.: Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int. J. Comput. Assist. Radiol. Surg. 13, 1959\u20131970 (2018)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"24_CR15","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s11548-018-1735-5","volume":"13","author":"A Zia","year":"2018","unstructured":"Zia, A., Essa, I.: Automated surgical skill assessment in RMIS training. Int. J. Comput. Assist. Radiol. Surg. 13, 731\u2013739 (2018)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."}],"container-title":["Lecture Notes in Computer Science","Ambient Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34255-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T05:48:16Z","timestamp":1611726496000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-34255-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030342548","9783030342555"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34255-5_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"4 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AmI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Ambient Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ami2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ami2019.diag.uniroma1.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}