{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:17:35Z","timestamp":1776147455550,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030983574","type":"print"},{"value":"9783030983581","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-98358-1_23","type":"book-chapter","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T12:06:56Z","timestamp":1647259616000},"page":"287-298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Human Activity Recognition with IMU and Vital Signs Feature Fusion"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7394-8412","authenticated-orcid":false,"given":"Vasileios-Rafail","family":"Xefteris","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athina","family":"Tsanousa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanassis","family":"Mavropoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Meditskos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"issue":"3","key":"23_CR1","doi-asserted-by":"publisher","first-page":"692","DOI":"10.3390\/s21030692","volume":"21","author":"J Chen","year":"2021","unstructured":"Chen, J., Sun, Y., Sun, S.: Improving human activity recognition performance by data fusion and feature engineering. Sensors 21(3), 692 (2021)","journal-title":"Sensors"},{"issue":"6","key":"23_CR2","doi-asserted-by":"publisher","first-page":"4029","DOI":"10.1007\/s10489-020-02005-7","volume":"51","author":"L Chen","year":"2020","unstructured":"Chen, L., Liu, X., Peng, L., Wu, M.: Deep learning based multimodal complex human activity recognition using wearable devices. Appl. Intell. 51(6), 4029\u20134042 (2020). https:\/\/doi.org\/10.1007\/s10489-020-02005-7","journal-title":"Appl. Intell."},{"issue":"2","key":"23_CR3","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/JSEN.2016.2628346","volume":"17","author":"M Cornacchia","year":"2016","unstructured":"Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386\u2013403 (2016)","journal-title":"IEEE Sens. J."},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Doewes, A., Swasono, S.E., Harjito, B.: Feature selection on human activity recognition dataset using minimum redundancy maximum relevance. In: 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 171\u2013172. IEEE (2017)","DOI":"10.1109\/ICCE-China.2017.7991050"},{"issue":"7","key":"23_CR5","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s00607-021-00928-8","volume":"103","author":"N Dua","year":"2021","unstructured":"Dua, N., Singh, S.N., Semwal, V.B.: Multi-input CNN-GRU based human activity recognition using wearable sensors. Computing 103(7), 1461\u20131478 (2021). https:\/\/doi.org\/10.1007\/s00607-021-00928-8","journal-title":"Computing"},{"key":"23_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/978-3-030-67835-7_31","volume-title":"MultiMedia Modeling","author":"P Giannakeris","year":"2021","unstructured":"Giannakeris, P., et al.: Fusion of multimodal sensor data for effective human action recognition in\u00a0the service of medical platforms. In: Loko\u010d, J., et al. (eds.) MMM 2021. LNCS, vol. 12573, pp. 367\u2013378. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67835-7_31"},{"key":"23_CR7","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.patrec.2021.03.003","volume":"146","author":"P Kasnesis","year":"2021","unstructured":"Kasnesis, P., Chatzigeorgiou, C., Patrikakis, C.Z., Rangoussi, M.: Modality-wise relational reasoning for one-shot sensor-based activity recognition. Pattern Recogn. Lett. 146, 90\u201399 (2021)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"23_CR8","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","volume":"15","author":"OD Lara","year":"2012","unstructured":"Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192\u20131209 (2012)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"5","key":"23_CR9","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.pmcj.2011.06.004","volume":"8","author":"OD Lara","year":"2012","unstructured":"Lara, O.D., P\u00e9rez, A.J., Labrador, M.A., Posada, J.D.: Centinela: a human activity recognition system based on acceleration and vital sign data. Pervasive Mob. Comput. 8(5), 717\u2013729 (2012)","journal-title":"Pervasive Mob. Comput."},{"key":"23_CR10","unstructured":"Maghsoudi, Y., Alimohammadi, A., Zoej, M.V., Mojaradi, B.: Weighted combination of multiple classifiers for the classification of hyperspectral images using a genetic algorithm. In: ISPRS Commission I Symposium From Sensors to Imagery (2006)"},{"key":"23_CR11","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.inffus.2018.06.002","volume":"46","author":"HF Nweke","year":"2019","unstructured":"Nweke, H.F., Teh, Y.W., Mujtaba, G., Al-Garadi, M.A.: Data fusion and multiple classifier systems for human activity detection and health monitoring: review and open research directions. Inf. Fusion 46, 147\u2013170 (2019)","journal-title":"Inf. Fusion"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108\u2013109. IEEE (2012)","DOI":"10.1109\/ISWC.2012.13"},{"issue":"12","key":"23_CR13","doi-asserted-by":"publisher","first-page":"4189","DOI":"10.3390\/s18124189","volume":"18","author":"S Rosati","year":"2018","unstructured":"Rosati, S., Balestra, G., Knaflitz, M.: Comparison of different sets of features for human activity recognition by wearable sensors. Sensors 18(12), 4189 (2018)","journal-title":"Sensors"},{"issue":"6","key":"23_CR14","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1007\/s00542-018-3802-9","volume":"24","author":"J Saha","year":"2018","unstructured":"Saha, J., Chowdhury, C., Biswas, S.: Two phase ensemble classifier for smartphone based human activity recognition independent of hardware configuration and usage behaviour. Microsyst. Technol. 24(6), 2737\u20132752 (2018). https:\/\/doi.org\/10.1007\/s00542-018-3802-9","journal-title":"Microsyst. Technol."},{"key":"23_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/978-3-030-55789-8_61","volume-title":"Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices","author":"D Sapra","year":"2020","unstructured":"Sapra, D., Pimentel, A.D.: Constrained evolutionary piecemeal training to design convolutional neural networks. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds.) IEA\/AIE 2020. LNCS (LNAI), vol. 12144, pp. 709\u2013721. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-55789-8_61"},{"issue":"9","key":"23_CR16","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.3390\/s18092892","volume":"18","author":"O Steven Eyobu","year":"2018","unstructured":"Steven Eyobu, O., Han, D.S.: Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network. Sensors 18(9), 2892 (2018)","journal-title":"Sensors"},{"issue":"2","key":"23_CR17","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s11036-019-01445-x","volume":"25","author":"S Wan","year":"2020","unstructured":"Wan, S., Qi, L., Xu, X., Tong, C., Gu, Z.: Deep learning models for real-time human activity recognition with smartphones. Mob. Netw. Appl. 25(2), 743\u2013755 (2020)","journal-title":"Mob. Netw. Appl."},{"key":"23_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-030-47426-3_17","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"T Wu","year":"2020","unstructured":"Wu, T., Chen, Y., Gu, Y., Wang, J., Zhang, S., Zhechen, Z.: Multi-layer cross loss model for zero-shot human activity recognition. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 210\u2013221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47426-3_17"},{"key":"23_CR19","doi-asserted-by":"publisher","first-page":"18398","DOI":"10.1109\/JSEN.2021.3090454","volume":"21","author":"VR Xefteris","year":"2021","unstructured":"Xefteris, V.R., Tsanousa, A., Meditskos, G., Vrochidis, S., Kompatsiaris, I.: Performance, challenges, and limitations in multimodal fall detection systems: a review. IEEE Sens. J. 21, 18398\u201318409 (2021)","journal-title":"IEEE Sens. J."},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, M., Sawchuk, A.A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: BodyNets, pp. 92\u201398 (2011)","DOI":"10.4108\/icst.bodynets.2011.247018"},{"issue":"1","key":"23_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13673-017-0097-2","volume":"7","author":"J Zhu","year":"2017","unstructured":"Zhu, J., San-Segundo, R., Pardo, J.M.: Feature extraction for robust physical activity recognition. HCIS 7(1), 1\u201316 (2017). https:\/\/doi.org\/10.1186\/s13673-017-0097-2","journal-title":"HCIS"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98358-1_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T10:11:23Z","timestamp":1708337483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98358-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030983574","9783030983581"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98358-1_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Phu Quoc","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2022","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool Pro","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"212","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":"84","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":"40% - 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","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":"4","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)"}}]}}