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The TTSNet framework relies on implicit and explicit multimodal communication cues (physical, neurological and physiological) to be able to predict when the turn-taking event will occur in a robust and unambiguous fashion. To test the theories proposed, the TTSNet framework was implemented on an assistant robotic nurse, which predicts surgeon\u2019s turn-taking intentions and delivers surgical instruments accordingly. Experiments were conducted to evaluate TTSNet\u2019s performance in early turn-taking prediction. It was found to reach an [Formula: see text] score of 0.683 given 10% of completed action, and an [Formula: see text] score of 0.852 at 50% and 0.894 at 100% of the completed action. This performance outperformed multiple state-of-the-art algorithms, and surpassed human performance when limited partial observation is given (&lt;40%). Such early turn-taking prediction capability would allow robots to perform collaborative actions proactively, in order to facilitate collaboration and increase team efficiency.<\/jats:p>","DOI":"10.1177\/0278364919872252","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T09:33:32Z","timestamp":1568972012000},"page":"1619-1643","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Spiking Neural Networks for early prediction in human\u2013robot collaboration"],"prefix":"10.1177","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5762-1292","authenticated-orcid":false,"given":"Tian","family":"Zhou","sequence":"first","affiliation":[{"name":"Industrial Engineering, Purdue University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan P","family":"Wachs","sequence":"additional","affiliation":[{"name":"Industrial Engineering, Purdue 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