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Multi-Resident human behaviour identification in ambient assisted living environments
Date
2014-11-12
Author
Alemdar, Hande
Metadata
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Multimodal interactions in ambient assisted living environments require human behaviour to be recognized and monitored automatically. The complex nature of human behaviour makes it extremely difficult to infer and adapt to, especially in multi-resident environments. This proposed research aims to contribute to the multimodal interaction community by (i) providing publicly available, naturalistic, rich and annotated datasets for human behaviour modeling, (ii) introducing evaluation methods of several inference methods from a behaviour monitoring perspective, (iii) developing novel methods for recognizing individual behaviour in multi-resident smart environments without assuming any person identification, (iv) proposing methods for mitigating the scalability issues by using transfer, active, and semi-supervised learning techniques. The proposed studies will address both practical and methodological aspects of human behaviour recognition in smart interactive environments.
URI
https://hdl.handle.net/11511/35850
DOI
https://doi.org/10.1145/2663204.2666286
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Department of Computer Engineering, Conference / Seminar