Explainable Activity Recognition for Smart Home Systems
Open accessAbstract
Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output of an underlying activity recognition system. However, activity recognition systems may not be perfectly accurate, and therefore inconsistencies in smart home operations can lead users reliant on smart home predictions to wonder “Why did the smart home do that?” In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods (Local Interpretable Model-agnostic Explanations, SHapley Additive exPlanations (SHAP), Anchors) to generate natural language explanations that explain what about an activity led to the given classification. We evaluate our framework in the context of a commonly targeted smart home scenario: autonomous remote caregiver monitoring for individuals who are living alone or need assistance. Within the context of remote caregiver monitoring, we perform a two-step evaluation: (a) utilize Machine Learning experts to assess the sensibility of explanations and (b) recruit non-experts in two user remote caregiver monitoring scenarios, synchronous and asynchronous, to assess the effectiveness of explanations generated via our framework. Our results show that the XAI approach, SHAP, has a 92% success rate in generating sensible explanations. Moreover, in 83% of sampled scenarios users preferred natural language explanations over a simple activity label, underscoring the need for explainable activity recognition systems. Finally, we show that explanations generated by some XAI methods can lead users to lose confidence in the accuracy of the underlying activity recognition model, while others lead users to gain confidence. Taking all studied factors into consideration, we make a recommendation regarding which existing XAI method leads to the best performance in the domain of smart home automation and discuss a range of topics for future work to further improve explainable activity recognition.
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Explanations grounded in contextual information significantly improve understanding.
“explanations grounded in such contextual information significantly improve understanding [3, 7, 33].”
From "Who wants to be nagged by AI?": Investigating the Effects of Agreeableness on Older Adults' Perception of LLM-Based Voice Assistants' Explanations (2026) · cites this paper as [7]
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- Authors
- Das, D., Nishimura, Y., Vivek, R. P., Takeda, N., Fish, S. T., Plötz, T., & Chernova, S.
- Affiliations
- Georgia Institute of Technology (US)
- KDDI Research (Japan) (JP)
- ORCID
- Venue
- ACM Transactions on Interactive Intelligent Systems
- Published
- 2023-05-05
- Volume
- 13
- Issue
- 2
- Pages
- 1-39
- ISSN
- 2160-6455 (ISSN-L)
- 2160-6463
- OpenAlex
- W3162602085
- Cited by
- 70
- References
- 84
- Citations by year
- 2026: 12 · 2025: 29 · 2024: 24 · 2023: 5
- FWCI
- 6.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Interactive Intelligent Systems (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- KDDI Research
- NSF Graduate Research Fellowship (DGE-1650044)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- 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
- Keywords
- smart home automation, explainable AI, SHAP, lime, anchors, natural language explanations, user trust in AI, living alone
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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.
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