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Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity.

, , , , , and . SMARTCOMP, page 33-40. IEEE, (2023)

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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)Let the objects tell what you are doing., , and . UbiComp Adjunct, page 773-782. ACM, (2016)Is Querying Users Acceptable for Human Activity Recognition Based on Active Learning?, , and . PerCom Workshops, page 521-526. IEEE, (2023)Towards Active Learning Interfaces for Multi-Inhabitant Activity Recognition., and . PerCom Workshops, page 1-6. IEEE, (2020)Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition., , , , , and . CoRR, (2024)Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning., , , and . UbiComp, page 1-12. ACM, (2016)Explainable Activity Recognition over Interpretable Models., , and . PerCom Workshops, page 32-37. IEEE, (2021)Preliminary Results on Sensitive Data Leakage in Federated Human Activity Recognition., , and . PerCom Workshops, page 304-309. IEEE, (2022)FedCLAR: Federated Clustering for Personalized Sensor-Based Human Activity Recognition., , and . PerCom, page 227-236. IEEE, (2022)NECTAR: Knowledge-based Collaborative Active Learning for Activity Recognition., , , , and . PerCom, page 1-10. IEEE Computer Society, (2018)