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Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning.

, , , and . UbiComp, page 1-12. ACM, (2016)

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SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning., , , and . CoRR, (2023)Is Querying Users Acceptable for Human Activity Recognition Based on Active Learning?, , and . PerCom Workshops, page 521-526. IEEE, (2023)Let the objects tell what you are doing., , and . UbiComp Adjunct, page 773-782. ACM, (2016)The MARBLE Dataset: Multi-inhabitant Activities of Daily Living Combining Wearable and Environmental Sensors Data., , and . MobiQuitous, volume 419 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 451-468. Springer, (2021)Towards Active Learning Interfaces for Multi-Inhabitant Activity Recognition., and . PerCom Workshops, page 1-6. IEEE, (2020)Hybrid data-driven and context-aware activity recognition with mobile devices., , and . UbiComp/ISWC Adjunct, page 266-267. ACM, (2019)CAVIAR: Context-driven Active and Incremental Activity Recognition., , and . Knowl. Based Syst., (2020)Federated Clustering and Semi-Supervised learning: A new partnership for personalized Human Activity Recognition., , and . Pervasive Mob. Comput., (January 2023)Probabilistic knowledge infusion through symbolic features for context-aware activity recognition., , and . Pervasive Mob. Comput., (2023)POLARIS: Probabilistic and Ontological Activity Recognition in Smart-Homes., , , , and . IEEE Trans. Knowl. Data Eng., 33 (1): 209-223 (2021)