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Keeping the Resident in the Loop: Adapting the Smart Home to the User
Abstract
Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
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Metadata
- Authors
- Rashidi, P., & Cook, D. J.
- Affiliations
- Washington State University (US)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Published
- 2009-09
- Volume
- 39
- Issue
- 5
- Pages
- 949-959
- ISSN
- 1083-4427 (ISSN-L)
- 1558-2426
- OpenAlex
- W2121269968
- Cited by
- 443
- References
- 25
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 13 · 2023: 16 · 2022: 19 · 2021: 24 · 2020: 31 · 2019: 41 · 2018: 30 · 2017: 37 · 2016: 21 · 2015: 35 · 2014: 36 · 2013: 34 · 2012: 30
- FWCI
- 29.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- home automation, CASAS, user modeling, implicit feedback, model updating
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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