Abstract
The aim of this work is to discuss abnormality detection and explanation challenges motivated by Medical Internet of Things. First, any feature is a measurement taken by a sensor at a time moment, so abnormality detection also becomes a sequential process. Second, an anomaly detection process could not rely on having a large collection of data records, but instead there is a knowledge provided by the experts.
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Acknowledgements
This work was supported by Technology Integrated Health Management (TIHM) project awarded to the School of Mathematics and Information Security at Royal Holloway as part of an initiative by NHS England supported by InnovateUK, by European Union grant 671555 (“ExCAPE"), and AstraZeneca grant R10911.
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Nouretdinov, I., Darwish, S., Wolthusen, S. (2018). Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Smart Homes and Health Telematics, Designing a Better Future: Urban Assisted Living. ICOST 2018. Lecture Notes in Computer Science(), vol 10898. Springer, Cham. https://doi.org/10.1007/978-3-319-94523-1_31
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DOI: https://doi.org/10.1007/978-3-319-94523-1_31
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