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Percept."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>Cognitive load assessment is crucial for user studies and human--computer interaction designs. As a noninvasive and easy-to-use category of measures, current photoplethysmogram- (PPG) based assessment methods rely on single or small-scale predefined features to recognize responses induced by people\u2019s cognitive load, which are not stable in assessment accuracy. In this study, we propose a machine-learning method by using 46 kinds of PPG features together to improve the measurement accuracy for cognitive load. We test the method on 16 participants through the classical n-back tasks (0-back, 1-back, and 2-back). The accuracy of the machine-learning method in differentiating different levels of cognitive loads induced by task difficulties can reach 100% in 0-back vs. 2-back tasks, which outperformed the traditional HRV-based and single-PPG-feature-based methods by 12--55%. When using \u201cleave-one-participant-out\u201d subject-independent cross validation, 87.5% binary classification accuracy was reached, which is at the state-of-the-art level. The proposed method can also support real-time cognitive load assessment by beat-to-beat classifications with better performance than the traditional single-feature-based real-time evaluation method.<\/jats:p>","DOI":"10.1145\/3340962","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T12:10:12Z","timestamp":1569240612000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Photoplethysmogram-based Cognitive Load Assessment Using Multi-Feature Fusion Model"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7307-8624","authenticated-orcid":false,"given":"Xiao","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing University of Technology, China and Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqiang","family":"Lyu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Qu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Qiu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomin","family":"Luo","sequence":"additional","affiliation":[{"name":"Beijing Genomics Institute, Lhasa, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunjie","family":"Fan","sequence":"additional","affiliation":[{"name":"Siemens Ltd., China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanchun","family":"Shi","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,9,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130898"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2007.897805"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10648-010-9130-y"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNE.2007.369680"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/9\/4\/045008"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2014.05.004"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1207\/S15326985EP3801_7"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2395123.2395127"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 2017 7th International Conference on Affective Computing and Intelligent Interaction (ACII\u201917)","author":"Cho Y.","year":"2017"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1037\/0022-0663.100.1.223"},{"key":"e_1_2_1_11_1","volume-title":"Picard","author":"Dennerlein Jack","year":"2003"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbr.2013.10.042"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.2174\/157340312801215782"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2395131.2395138"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-8986.2006.00490.x"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI\u201908)","author":"Grimes David"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the 12th ACM International Conference on Ubiquitous Computing (UbiComp\u201910)","author":"Haapalainen Eija","year":"1864"},{"key":"e_1_2_1_18_1","first-page":"405","article-title":"Effects of mental stress on brachial artery flow-mediated vasodilation in healthy normal individuals","volume":"139","author":"Harris C. 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