{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:04:23Z","timestamp":1743113063012,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030862299"},{"type":"electronic","value":"9783030862305"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86230-5_36","type":"book-chapter","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:03:00Z","timestamp":1631005380000},"page":"456-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Compound Movement Recognition Using Dynamic Movement Primitives"],"prefix":"10.1007","author":[{"given":"Ali H.","family":"Kordia","sequence":"first","affiliation":[]},{"given":"Francisco S.","family":"Melo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"36_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/BFb0055712","volume-title":"Computer Vision \u2014 ECCV\u201998","author":"MJ Black","year":"1998","unstructured":"Black, M.J., Jepson, A.D.: A probabilistic framework for matching temporal trajectories: condensation-based recognition of gestures and expressions. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 909\u2013924. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0055712"},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Black, M., Jepson, A.: Recognizing temporal trajectories using the condensation algorithm. In: Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 16\u201321 (1998)","DOI":"10.1109\/AFGR.1998.670919"},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3389\/frobt.2017.00045","volume":"4","author":"O Dermy","year":"2017","unstructured":"Dermy, O., Paraschos, A., Ewerton, M., Peters, J., Charpillet, F., Ivaldi, S.: Prediction of intention during interaction with iCub with probabilistic movement primitives. Front. Robot. AI 4, 45 (2017)","journal-title":"Front. Robot. AI"},{"issue":"2","key":"36_CR4","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1162\/NECO_a_00393","volume":"25","author":"A Ijspeert","year":"2013","unstructured":"Ijspeert, A., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives Learning attractor models for motor behaviors. Neural Comput. 25(2), 328\u2013373 (2013)","journal-title":"Neural Comput."},{"key":"36_CR5","volume-title":"Dynamic Patterns: The Self-Organization of Brain and Behavior","author":"J Kelso","year":"1995","unstructured":"Kelso, J.: Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press, Cambridge (1995)"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Kordia, A., Melo, F.: An end-to-end approach for learning and generating complex robot motions from demonstration. In: Proceedings of the 16th IEEE International Conference on Control, Automation, Robotics and Vision, pp. 1008\u20131014 (2020)","DOI":"10.1109\/ICARCV50220.2020.9305399"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Kordia, A., Melo, F.: Movement recognition and prediction using DMPs. In: Proceedings of the 2021 IEEE International Conference on Robotics and Automation (2021)","DOI":"10.1109\/ICRA48506.2021.9562001"},{"issue":"1","key":"36_CR8","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/TRO.2011.2163863","volume":"28","author":"T Kulvicius","year":"2012","unstructured":"Kulvicius, T., Ning, K., Tamosiunaite, M., Worg\u00f6tter, F.: Joining movement sequences: modified dynamic movement primitives for robotics applications exemplified on handwriting. IEEE Trans. Robot. 28(1), 145\u2013157 (2012)","journal-title":"IEEE Trans. Robot."},{"issue":"13","key":"36_CR9","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1177\/0278364910364164","volume":"29","author":"D Lee","year":"2010","unstructured":"Lee, D., Ott, C., Nakamura, Y.: Mimetic communication model with compliant physical contact in human-humanoid interaction. Int. J. Robot. Res. 29(13), 1684\u20131704 (2010)","journal-title":"Int. J. Robot. Res."},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Loshchilov, I.: A computationally efficient limited memory CMA-ES for large scale optimization. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 397\u2013404 (2014)","DOI":"10.1145\/2576768.2598294"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Ma, S., Zhang, J., Ikizler-Cinbis, N., Sclaroff, S.: Action recognition and localization by hierarchical space-time segments. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2744\u20132751 (2013)","DOI":"10.1109\/ICCV.2013.341"},{"issue":"3","key":"36_CR12","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/s10514-016-9556-2","volume":"41","author":"G Maeda","year":"2017","unstructured":"Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J.: Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks. Auton. Robot. 41(3), 593\u2013612 (2017)","journal-title":"Auton. Robot."},{"key":"36_CR13","unstructured":"Meier, F., Theodorou, E., Schaal, S.: Movement segmentation and recognition for imitation learning. In: Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, pp. 761\u2013769 (2012)"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Mulling, K., Kober, J., Peters, J.: Learning table tennis with a mixture of motor primitives. In: Proceedings of the 2010 IEEE-RAS International Conference on Humanoid Robots, pp. 411\u2013416 (2010)","DOI":"10.1109\/ICHR.2010.5686298"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Nemec, B., Tamosiunaite, M., Woergoetter, F., Ude, A.: Task adaptation through exploration and action sequencing. In: Proceedings of the 9th IEEE-RAS International Conference on Humanoid Robots, pp. 610\u2013616 (2009)","DOI":"10.1109\/ICHR.2009.5379568"},{"issue":"8","key":"36_CR16","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1109\/34.868684","volume":"22","author":"N Oliver","year":"2000","unstructured":"Oliver, N., Rosario, B., Pentland, A.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831\u2013843 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR17","unstructured":"Stulp, F., Sigaud, O.: Path integral policy improvement with covariance matrix adaptation. In: Proceedings of the 29th International Conference on Machine Learning, pp. 1547\u20131554 (2012)"},{"issue":"1","key":"36_CR18","first-page":"49","volume":"4","author":"F Stulp","year":"2013","unstructured":"Stulp, F., Sigaud, O.: Robot skill learning: from reinforcement learning to evolution strategies. PALADYN J. Behav. Robot. 4(1), 49\u201361 (2013)","journal-title":"PALADYN J. Behav. Robot."},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Tanaka, Y., Kinugawa, J., Sugahara, Y., Kosuge, K.: Motion planning with worker\u2019s trajectory prediction for assembly task partner robot. In: Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 1525\u20131532 (2012)","DOI":"10.1109\/IROS.2012.6386043"},{"issue":"7","key":"36_CR20","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1016\/S0031-3203(00)00096-0","volume":"34","author":"H Yoon","year":"2001","unstructured":"Yoon, H., Soh, J., Bae, Y., Yang, H.: Hand gesture recognition using combined features of location, angle and velocity. Pattern Recogn. 34(7), 1491\u20131501 (2001)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86230-5_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T23:05:38Z","timestamp":1725750338000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86230-5_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030862299","9783030862305"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86230-5_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.appia.pt\/epia2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.47","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.36","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}