{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:35:13Z","timestamp":1763202913491,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031639883"},{"type":"electronic","value":"9783031639890"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63989-0_19","type":"book-chapter","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T21:01:50Z","timestamp":1721336510000},"page":"375-391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SelfAct: Personalized Activity Recognition Based on\u00a0Self-Supervised and\u00a0Active Learning"],"prefix":"10.1007","author":[{"given":"Luca","family":"Arrotta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriele","family":"Civitarese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Bettini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: Streamar: incremental and active learning with evolving sensory data for activity recognition. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol.\u00a01, pp. 1163\u20131170. IEEE (2012)","DOI":"10.1109\/ICTAI.2012.169"},{"issue":"4","key":"19_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3158645","volume":"51","author":"ZS Abdallah","year":"2018","unstructured":"Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: Activity recognition with evolving data streams: a review. ACM Comput. Surv. (CSUR) 51(4), 1\u201336 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Abedin, A., Motlagh, F., Shi, Q., Rezatofighi, H., Ranasinghe, D.: Towards deep clustering of human activities from wearables. In: Proceedings of the 2020 ACM International Symposium on Wearable Computers, pp. 1\u20136 (2020)","DOI":"10.1145\/3410531.3414312"},{"issue":"3","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3351228","volume":"3","author":"R Adaimi","year":"2019","unstructured":"Adaimi, R., Thomaz, E.: Leveraging active learning and conditional mutual information to minimize data annotation in human activity recognition. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 3(3), 1\u201323 (2019)","journal-title":"Proc. ACM Interact. Mobile Wearable Ubiq. Technol."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Haresamudram, H., Ploetz, T.: Clustering of human activities from wearables by adopting nearest neighbors. In: Proceedings of the 2022 ACM International Symposium on Wearable Computers, pp. 1\u20135 (2022)","DOI":"10.1145\/3544794.3558477"},{"key":"19_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/978-3-030-51935-3_34","volume-title":"Image and Signal Processing","author":"M Allaoui","year":"2020","unstructured":"Allaoui, M., Kherfi, M.L., Cheriet, A.: Considerably improving clustering algorithms using UMAP dimensionality reduction technique: a comparative study. In: El Moataz, A., Mammass, D., Mansouri, A., Nouboud, F. (eds.) ICISP 2020. LNCS, vol. 12119, pp. 317\u2013325. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-51935-3_34"},{"issue":"4","key":"19_CR7","first-page":"160","volume":"17","author":"B Almaslukh","year":"2017","unstructured":"Almaslukh, B., AlMuhtadi, J., Artoli, A.: An effective deep autoencoder approach for online smartphone-based human activity recognition. Int. J. Comput. Sci. Netw. Secur 17(4), 160\u2013165 (2017)","journal-title":"Int. J. Comput. Sci. Netw. Secur"},{"key":"19_CR8","unstructured":"Baevski, A., Hsu, W.N., Xu, Q., Babu, A., Gu, J., Auli, M.: Data2vec: a general framework for self-supervised learning in speech, vision and language. In: International Conference on Machine Learning, pp. 1298\u20131312. PMLR (2022)"},{"issue":"20","key":"19_CR9","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.3390\/electronics10202498","volume":"10","author":"D Bouchabou","year":"2021","unstructured":"Bouchabou, D., Nguyen, S.M., Lohr, C., LeDuc, B., Kanellos, I.: Using language model to bootstrap human activity recognition ambient sensors based in smart homes. Electronics 10(20), 2498 (2021)","journal-title":"Electronics"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Chakma, A., Gangopadhyay, A., Roy, N., Mitra, B., Chakraborty, S.: Laso: exploiting locomotive and acoustic signatures over the edge to annotate IMU data for human activity recognition. In: Proceedings of the 2020 International Conference on Multimodal Interaction, pp. 333\u2013342 (2020)","DOI":"10.1145\/3382507.3418826"},{"issue":"4","key":"19_CR11","first-page":"1","volume":"54","author":"K Chen","year":"2021","unstructured":"Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., Liu, Y.: Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities. ACM Comput. Surv. (CSUR) 54(4), 1\u201340 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"19_CR12","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"issue":"7","key":"19_CR13","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.3390\/s18072203","volume":"18","author":"F Cruciani","year":"2018","unstructured":"Cruciani, F., Cleland, I., Nugent, C., McCullagh, P., Synnes, K., Hallberg, J.: Automatic annotation for human activity recognition in free living using a smartphone. Sensors 18(7), 2203 (2018)","journal-title":"Sensors"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Cui, Y., Hiremath, S.K., Ploetz, T.: Reinforcement learning based online active learning for human activity recognition. In: Proceedings of the 2022 ACM International Symposium on Wearable Computers, pp. 23\u201327 (2022)","DOI":"10.1145\/3544794.3558457"},{"issue":"3","key":"19_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550299","volume":"6","author":"H Haresamudram","year":"2022","unstructured":"Haresamudram, H., Essa, I., Pl\u00f6tz, T.: Assessing the state of self-supervised human activity recognition using wearables. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 6(3), 1\u201347 (2022)","journal-title":"Proc. ACM Interact. Mobile Wearable Ubiq. Technol."},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Hassan, I., Mursalin, A., Salam, R.B., Sakib, N., Haque, H.Z.: Autoact: an auto labeling approach based on activities of daily living in the wild domain. In: 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/ICIEVicIVPR52578.2021.9564211"},{"issue":"3","key":"19_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550294","volume":"6","author":"SK Hiremath","year":"2022","unstructured":"Hiremath, S.K., Nishimura, Y., Chernova, S., Pl\u00f6tz, T.: Bootstrapping human activity recognition systems for smart homes from scratch. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 6(3), 1\u201327 (2022)","journal-title":"Proc. ACM Interact. Mobile Wearable Ubiq. Technol."},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Hossain, H.S., Roy, N.: Active deep learning for activity recognition with context aware annotator selection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1862\u20131870 (2019)","DOI":"10.1145\/3292500.3330688"},{"issue":"1","key":"19_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3517246","volume":"6","author":"Y Jain","year":"2022","unstructured":"Jain, Y., Tang, C.I., Min, C., Kawsar, F., Mathur, A.: Collossl: collaborative self-supervised learning for human activity recognition. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 6(1), 1\u201328 (2022)","journal-title":"Proc. ACM Interact. Mobile Wearable Ubiq. Technol."},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Khaertdinov, B., Ghaleb, E., Asteriadis, S.: Contrastive self-supervised learning for sensor-based human activity recognition. In: 2021 IEEE International Joint Conference on Biometrics (IJCB), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCB52358.2021.9484410"},{"issue":"14","key":"19_CR21","doi-asserted-by":"publisher","first-page":"6067","DOI":"10.1016\/j.eswa.2014.04.037","volume":"41","author":"Y Kwon","year":"2014","unstructured":"Kwon, Y., Kang, K., Bae, C.: Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst. Appl. 41(14), 6067\u20136074 (2014)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"19_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3448074","volume":"5","author":"H Ma","year":"2021","unstructured":"Ma, H., Zhang, Z., Li, W., Lu, S.: Unsupervised human activity representation learning with multi-task deep clustering. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 5(1), 1\u201325 (2021)","journal-title":"Proc. ACM Interact. Mobile Wearable Ubiq. Technol."},{"key":"19_CR23","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Clegg, R.G., Cavallaro, A., Haddadi, H.: Protecting sensory data against sensitive inferences. In: Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems, pp. 1\u20136 (2018)","DOI":"10.1145\/3195258.3195260"},{"issue":"11","key":"19_CR25","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes, L., Healy, J., Astels, S.: hdbscan: Hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017)","journal-title":"J. Open Source Softw."},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A., Sander, J.: Density-based clustering validation. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 839\u2013847. SIAM (2014)","DOI":"10.1137\/1.9781611973440.96"},{"issue":"5","key":"19_CR28","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s00779-022-01688-8","volume":"26","author":"R Presotto","year":"2022","unstructured":"Presotto, R., Civitarese, G., Bettini, C.: Semi-supervised and personalized federated activity recognition based on active learning and label propagation. Pers. Ubiq. Comput. 26(5), 1281\u20131298 (2022)","journal-title":"Pers. Ubiq. Comput."},{"key":"19_CR29","doi-asserted-by":"publisher","first-page":"68985","DOI":"10.1109\/ACCESS.2021.3078184","volume":"9","author":"M Ronald","year":"2021","unstructured":"Ronald, M., Poulose, A., Han, D.S.: iSPLinception: an inception-resnet deep learning architecture for human activity recognition. IEEE Access 9, 68985\u201369001 (2021)","journal-title":"IEEE Access"},{"issue":"2","key":"19_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3328932","volume":"3","author":"A Saeed","year":"2019","unstructured":"Saeed, A., Ozcelebi, T., Lukkien, J.: Multi-task self-supervised learning for human activity detection. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 3(2), 1\u201330 (2019)","journal-title":"Proc. ACM Interact. Mobile Wearable Ubiq. Technol."},{"key":"19_CR31","doi-asserted-by":"publisher","first-page":"19421","DOI":"10.1109\/ACCESS.2021.3053704","volume":"9","author":"AR Sanabria","year":"2021","unstructured":"Sanabria, A.R., Zambonelli, F., Ye, J.: Unsupervised domain adaptation in activity recognition: a GAN-based approach. IEEE Access 9, 19421\u201319438 (2021)","journal-title":"IEEE Access"},{"key":"19_CR32","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.neucom.2020.10.056","volume":"426","author":"E Soleimani","year":"2021","unstructured":"Soleimani, E., Nazerfard, E.: Cross-subject transfer learning in human activity recognition systems using generative adversarial networks. Neurocomputing 426, 26\u201334 (2021)","journal-title":"Neurocomputing"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Stisen, A., et al.: Smart devices are different: assessing and mitigatingmobile sensing heterogeneities for activity recognition. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pp. 127\u2013140 (2015)","DOI":"10.1145\/2809695.2809718"},{"key":"19_CR34","unstructured":"Tang, C.I., Perez-Pozuelo, I., Spathis, D., Mascolo, C.: Exploring contrastive learning in human activity recognition for healthcare. arXiv preprint arXiv:2011.11542 (2020)"},{"issue":"3","key":"19_CR35","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1109\/TASE.2013.2256349","volume":"10","author":"D Trabelsi","year":"2013","unstructured":"Trabelsi, D., Mohammed, S., Chamroukhi, F., Oukhellou, L., Amirat, Y.: An unsupervised approach for automatic activity recognition based on hidden Markov model regression. IEEE Trans. Autom. Sci. Eng. 10(3), 829\u2013835 (2013)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"19_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","volume":"119","author":"J Wang","year":"2019","unstructured":"Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3\u201311 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"19_CR37","unstructured":"Weiss, G.M., Lockhart, J.: The impact of personalization on smartphone-based activity recognition. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Mobile and Ubiquitous Systems: Computing, Networking and Services"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63989-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T21:10:00Z","timestamp":1721337000000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63989-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031639883","9783031639890"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63989-0_19","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MobiQuitous","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mobiquitous2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mobiquitous.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}