{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:59:33Z","timestamp":1785340773632,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Amazon Research Award","award":["R201737"],"award-info":[{"award-number":["R201737"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819363","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2014-2749","authenticated-orcid":false,"given":"Desta Haileselassie","family":"Hagos","sequence":"first","affiliation":[{"name":"Howard University, Washington, DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9815-9295","authenticated-orcid":false,"given":"Saurav Keshari","family":"Aryal","sequence":"additional","affiliation":[{"name":"Howard University, Washington, DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-0540","authenticated-orcid":false,"given":"Patrick","family":"Ymele-Leki","sequence":"additional","affiliation":[{"name":"Howard University, Washington, DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7043-3042","authenticated-orcid":false,"given":"Anietie","family":"Andy","sequence":"additional","affiliation":[{"name":"Howard University, Washington, DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9876-7135","authenticated-orcid":false,"given":"Legand L.","family":"Burge","sequence":"additional","affiliation":[{"name":"Howard University, Washington, DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/I2MTC62753.2025.11079085"},{"key":"e_1_3_3_2_3_2","unstructured":"Shaojie Bai J\u00a0Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.01271 (2018)."},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Rafael\u00a0A Calvo and Sidney D\u2019Mello. 2010. Affect detection: An interdisciplinary review of models methods and their applications. IEEE Transactions on affective computing 1 1 (2010) 18\u201337.","DOI":"10.1109\/T-AFFC.2010.1"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Hyun-Sik Choi. 2025. Emotion recognition using a Siamese model and a late fusion-based multimodal method in the WESAD dataset with hardware accelerators. Electronics 14 4 (2025) 723.","DOI":"10.3390\/electronics14040723"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Yi Ding Su Zhang Chuangao Tang and Cuntai Guan. 2024. MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition. IEEE Journal of Biomedical and Health Informatics 28 7 (2024) 3953\u20133964.","DOI":"10.1109\/JBHI.2024.3392564"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Jennifer\u00a0A Healey and Rosalind\u00a0W Picard. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on intelligent transportation systems 6 2 (2005) 156\u2013166.","DOI":"10.1109\/TITS.2005.848368"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation 9 8 (1997) 1735\u20131780.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS51828.2021.9458520"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Tomas Kulvicius Dajie Zhang Luise Poustka Sven B\u00f6lte Lennart Jahn Sarah Fl\u00fcgge Marc Kraft Markus Zweckstetter Karin Nielsen-Saines Florentin W\u00f6rg\u00f6tter et\u00a0al. 2025. Deep learning empowered sensor fusion boosts infant movement classification. Communications medicine 5 1 (2025) 16.","DOI":"10.1038\/s43856-024-00701-w"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Fanny Larradet Radoslaw Niewiadomski Giacinto Barresi Darwin\u00a0G Caldwell and Leonardo\u00a0S Mattos. 2020. Toward emotion recognition from physiological signals in the wild: approaching the methodological issues in real-life data collection. Frontiers in psychology 11 (2020) 1111.","DOI":"10.3389\/fpsyg.2020.01111"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Fang Li and Dan Zhang. 2025. Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts. Sensors 25 3 (2025).","DOI":"10.3390\/s25030761"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Yihao Li Mostafa El\u00a0Habib Daho Pierre-Henri Conze et\u00a0al. 2024. A review of deep learning-based information fusion techniques for multimodal medical image classification. Computers in Biology and Medicine 177 (2024) 108635.","DOI":"10.1016\/j.compbiomed.2024.108635"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Yilong Liao Yuan Gao Fang Wang Li Zhang Zhenrong Xu and Yifan Wu. 2025. Emotion recognition with multiple physiological parameters based on ensemble learning. Scientific Reports 15 1 (2025) 19869.","DOI":"10.1038\/s41598-025-96616-0"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-7615-5_22"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3170427.3188480"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Amin Rostami Koorosh Motaman Bahram Tarvirdizadeh Khalil Alipour and Mohammad Ghamari. 2024. LSTM-based real-time stress detection using PPG signals on raspberry Pi. IET Wireless Sensor Systems 14 6 (2024) 333\u2013347.","DOI":"10.1049\/wss2.12083"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3242969.3242985"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Philip Schmidt Attila Reiss Robert D\u00fcrichen and Kristof Van\u00a0Laerhoven. 2019. Wearable-based affect recognition\u2014A review. Sensors 19 19 (2019) 4079.","DOI":"10.3390\/s19194079"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Neil Schneiderman Gail Ironson and Scott\u00a0D Siegel. 2005. Stress and health: psychological behavioral and biological determinants. Annu. Rev. Clin. Psychol. 1 1 (2005) 607\u2013628.","DOI":"10.1146\/annurev.clinpsy.1.102803.144141"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Mahsa Sheikh Meha Qassem and Panicos\u00a0A Kyriacou. 2021. Wearable environmental and smartphone-based passive sensing for mental health monitoring. Frontiers in digital health 3 (2021) 662811.","DOI":"10.3389\/fdgth.2021.662811"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Sabine Sonnentag and Charlotte Fritz. 2015. Recovery from Job Stress: The Stressor-Detachment Model as an Integrative Framework. Journal of organizational behavior 36 S1 (2015) S72\u2013S103.","DOI":"10.1002\/job.1924"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Ritu Tanwar Orchid\u00a0Chetia Phukan Ghanapriya Singh et\u00a0al. 2024. Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence 127 (2024) 107391.","DOI":"10.1016\/j.engappai.2023.107391"},{"key":"e_1_3_3_2_25_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention Is All You Need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACII55700.2022.9953852"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Yujin Wu Mohamed Daoudi and Ali Amad. 2023. Transformer-based self-supervised multimodal representation learning for wearable emotion recognition. IEEE Transactions on Affective Computing 15 1 (2023) 157\u2013172.","DOI":"10.1109\/TAFFC.2023.3263907"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"M\u00a0Sami Zitouni Cheul\u00a0Young Park Uichin Lee Leontios\u00a0J Hadjileontiadis and Ahsan Khandoker. 2022. LSTM-modeling of emotion recognition using peripheral physiological signals in naturalistic conversations. IEEE Journal of Biomedical and Health Informatics 27 2 (2022) 912\u2013923.","DOI":"10.1109\/JBHI.2022.3225330"}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819363","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:14:59Z","timestamp":1785338099000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819363"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":27,"alternative-id":["10.1145\/3807503.3819363","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819363","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}