Abstract
Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models)
(ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities
and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques.
Document type: Article
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The different versions of the original document can be found in:
- http://dx.doi.org/10.3390/s18061951 under the license https://creativecommons.org/licenses/by
- http://europepmc.org/articles/PMC6021856 under the license https://creativecommons.org/licenses/by/4.0/
- https://doaj.org/toc/1424-8220 under the license cc-by
Document information
Published on 01/01/2018
Volume 2018, 2018
DOI: 10.3390/s18061951
Licence: Other
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