{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:36:04Z","timestamp":1743060964967,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030805678"},{"type":"electronic","value":"9783030805685"}],"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\/978-3-030-80568-5_12","type":"book-chapter","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T17:04:53Z","timestamp":1624467893000},"page":"141-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Fusion for Deep Learning on Transport Mode Detection: A Case Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0569-4190","authenticated-orcid":false,"given":"Hugues","family":"Moreau","sequence":"first","affiliation":[]},{"given":"Andrea","family":"Vassilev","sequence":"additional","affiliation":[]},{"given":"Liming","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"145552","DOI":"10.1109\/ACCESS.2020.3014901","volume":"8","author":"B Alotaibi","year":"2020","unstructured":"Alotaibi, B.: Transportation mode detection by embedded sensors based on ensemble learning. IEEE Access 8, 145552\u2013145563 (2020)","journal-title":"IEEE Access"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Chen, C., et al.: Selective Sensor Fusion for Neural Visual-Inertial Odometry. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10534\u201310543. IEEE (June 2019)","DOI":"10.1109\/CVPR.2019.01079"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Choi, J.H., Lee, J.S.: Confidence-based Deep Multimodal Fusion for Activity Recognition. In: UbiComp 2018, pp. 1548\u20131556. ACM Press (2018)","DOI":"10.1145\/3267305.3267522"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Feng, D., Cao, Y., Rosenbaum, L., Timm, F., Dietmayer, K.: Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving. arXiv:2002.00216 [cs] (February 2020)","DOI":"10.1109\/IV47402.2020.9304551"},{"issue":"5","key":"12_CR5","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1162\/neco_a_01273","volume":"32","author":"J Gao","year":"2020","unstructured":"Gao, J., Li, P., Chen, Z., Zhang, J.: A survey on deep learning for multimodal data fusion. Neural Comput. 32(5), 829\u2013864 (2020)","journal-title":"Neural Comput."},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Gjoreski, M., et al.: Applying multiple knowledge to sussex-huawei locomotion challenge. In: UbiComp 2018, pp. 1488\u20131496. ACM Press (2018)","DOI":"10.1145\/3267305.3267515"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Gjoreski, M., et al.: Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors. Inf. Fusion 62, 47\u201362 (2020)","DOI":"10.1016\/j.inffus.2020.04.004"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Ito, C., Cao, X., Shuzo, M., Maeda, E.: Application of CNN for human activity recognition with FFT spectrogram of acceleration and gyro sensors. In: UbiComp 2018, pp. 1503\u20131510. ACM Press (2018)","DOI":"10.1145\/3267305.3267517"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Janko, V., et al.: Cross-location transfer learning for the Sussex-Huawei locomotion recognition challenge. In: UbiComp\/ISWC 2019, pp. 730\u2013735. ACM Press (2019)","DOI":"10.1145\/3341162.3344856"},{"key":"12_CR10","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.inffus.2019.06.016","volume":"53","author":"J Liu","year":"2020","unstructured":"Liu, J., Li, T., Xie, P., Du, S., Teng, F., Yang, X.: Urban big data fusion based on deep learning: an overview. Inf. Fusion 53, 123\u2013133 (2020)","journal-title":"Inf. Fusion"},{"key":"12_CR11","unstructured":"Liu, K., Li, Y., Xu, N., Natarajan, P.: Learn to Combine Modalities in Multimodal Deep Learning. arXiv:1805.11730 [cs, stat] (May 2018)"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Ma, L., Lu, Z., Shang, L., Li, H.: Multimodal Convolutional Neural Networks for Matching Image and Sentence. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2623\u20132631 (2015)","DOI":"10.1109\/ICCV.2015.301"},{"issue":"2","key":"12_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/informatics5020026","volume":"5","author":"F Moya Rueda","year":"2018","unstructured":"Moya Rueda, F., Grzeszick, R., Fink, G.A., Feldhorst, S., Ten Hompel, M.: Convolutional neural networks for human activity recognition using body-worn sensors. Informatics 5(2), 26 (2018)","journal-title":"Informatics"},{"key":"12_CR14","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal Deep Learning. In: ICML (January 2011)"},{"issue":"1","key":"12_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJ Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez, F.J., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)","journal-title":"Sensors"},{"issue":"16","key":"12_CR16","first-page":"9314","volume":"20","author":"S Richoz","year":"2020","unstructured":"Richoz, S., Wang, L., Birch, P., Roggen, D.: Transportation mode recognition fusing wearable motion, sound, and vision sensors. IEEE Sens. J. 20(16), 9314\u20139328 (2020)","journal-title":"IEEE Sens. J."},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Wang, B., Lei, Y., Li, N., Yan, T.: Deep separable convolutional network for remaining useful life prediction of machinery. Mech. Syst. Sig. Process. 134, 106330 (2019)","DOI":"10.1016\/j.ymssp.2019.106330"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019. In: UbiComp\/ISWC 2019, pp. 849\u2013856. ACM Press (2019)","DOI":"10.1145\/3341162.3344872"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Summary of the sussex-huawei locomotion-transportation recognition challenge 2020. In: Ubicomp 2020, pp. 351\u2013358. ACM (September 2020)","DOI":"10.1145\/3410530.3414341"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Wang, L., Gjoreskia, H., Murao, K., Okita, T., Roggen, D.: Summary of the Sussex-Huawei locomotion-transportation recognition challenge. In: UbiComp 2018, pp. 1521\u20131530. ACM Press (2018)","DOI":"10.1145\/3267305.3267519"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Wang, W., Tran, D., Feiszli, M.: What Makes Training Multi-Modal Networks Hard? arXiv:1905.12681 [cs] (May 2019)","DOI":"10.1109\/CVPR42600.2020.01271"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Widhalm, P., Leodolter, M., Br\u00e4ndle, N.: Top in the Lab, Flop in the Field?: Evaluation of a Sensor-based Travel Activity Classifier with the SHL Dataset. In: Ubicomp 2018, pp. 1479\u20131487. UbiComp 2018, ACM (2018)","DOI":"10.1145\/3267305.3267514"},{"key":"12_CR23","unstructured":"Xu, K., et al.: Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. arXiv:1502.03044 [cs] (April 2016)"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Zeng, M., et al.: Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors (November 2014)","DOI":"10.4108\/icst.mobicase.2014.257786"}],"container-title":["Proceedings of the International Neural Networks Society","Proceedings of the 22nd Engineering Applications of Neural Networks Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80568-5_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T07:07:29Z","timestamp":1656400049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80568-5_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030805678","9783030805685"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80568-5_12","relation":{},"ISSN":["2661-8141","2661-815X"],"issn-type":[{"type":"print","value":"2661-8141"},{"type":"electronic","value":"2661-815X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.eann2021.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}