| 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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Scientific Data, Vol 10, Iss 1, Pp 1-15 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2052-4463 |
| DOI: |
10.1038/s41597-023-02017-1 |
| Popis: |
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 sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the ‘SPHERE House’ in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink