A multi-sensor dataset with annotated activities of daily living recorded in a residential setting

Autor: Emma L. Tonkin, Michael Holmes, Hao Song, Niall Twomey, Tom Diethe, Meelis Kull, Miquel Perello Nieto, Massimo Camplani, Sion Hannuna, Xenofon Fafoutis, Ni Zhu, Przemysław R. Woznowski, Gregory J. L. Tourte, Raúl Santos-Rodríguez, Peter A. Flach, Ian Craddock

Publikováno v: Scientific Data, Vol 10, Iss 1, Pp 1-15 (2023)

Abstract SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered

Externí odkaz: https://doaj.org/article/d1d084e0ce80496b94a893bfbd15cd73

Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study (Preprint)

Autor: Louise A C Millard, Laura Johnson, Samuel R Neaves, Peter A Flach, Kate Tilling, Deborah A Lawlor

BACKGROUND Voice-based systems such as Amazon Alexa may be useful for collecting self-reported information in real time from participants of epidemiology studies using verbal input. In epidemiological research studies, self-reported data tend to be c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::334fc10a0f3135caab61326565b36b9a
https://doi.org/10.2196/preprints.41117

Reflections on reciprocity in research

Autor: Peter A. Flach

Publikováno v: Machine Learning

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0e0f3364b06978e4bd174be642b22cd
https://doi.org/10.1007/s10994-020-05892-6

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Autor: Przemysław R. Woznowski, Emma L. Tonkin, Peter A. Flach

Publikováno v: Sensors, Vol 18, Iss 7, p 2361 (2018)

Ubiquitous eHealth systems based on sensor technologies are seen as key enablers in the effort to reduce the financial impact of an ageing society. At the heart of such systems sit activity recognition algorithms, which need sensor data to reason ove

Externí odkaz: https://doaj.org/article/8c54cb42d30d41599cf64df46e6cb630

CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

Autor: Lidia Contreras-Ochando, Meelis Kull, José Hernández-Orallo, Cèsar Ferri, María José Ramírez-Quintana, Nicolas Lachiche, Fernando Martínez-Plumed, Peter A. Flach

Publikováno v: Martínez-Plumed, F, Contreras-Ochando, L, Ferri, C, Hernandez-Orallo, J, Kull, M, Lachiche, N J A H, Ramírez-Quintana, M J & Flach, P A 2019, ' CRISP-DM Twenty Years Later : From Data Mining Processes to Data Science Trajectories ', IEEE Transactions on Knowledge and Data Engineering . https://doi.org/10.1109/TKDE.2019.2962680
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering, 2021, 33 (8), ⟨10.1109/TKDE.2019.2962680⟩
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname

[EN] CRISP-DM (CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still thede factostandard for developing data mining

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8add3d861ae9eac3cc250c463be60a3
https://doi.org/10.1109/tkde.2019.2962680