DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data
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arXiv
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| Format: | Preprint |
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2025
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| author | Karpekov, Alexander Iyer, Archith Dhekane, Sourish Gunesh Chernova, Sonia Plötz, Thomas |
| author_facet | Karpekov, Alexander Iyer, Archith Dhekane, Sourish Gunesh Chernova, Sonia Plötz, Thomas |
| contents | Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific nuances essential, for example, for tracking individual health over time. To address this, we propose DISCOVER, a framework for discovering and annotating Patterns of Daily Living (PDL) - fine-grained, recurring sequences of sensor events that emerge directly from a resident's unique routines. DISCOVER utilizes a self-supervised feature extraction and representation-aware clustering pipeline, supported by a custom visualization interface that enables experts to interpret and label discovered patterns with minimal effort. Our evaluation across multiple smart-home environments demonstrates that DISCOVER identifies cohesive behavioral clusters with high inter-rater agreement while achieving classification performance comparable to fully-supervised baselines using only 0.01% of the labels. Beyond reducing annotation overhead, DISCOVER establishes a foundation for longitudinal analysis. By grounding behavior in a resident's specific environment rather than rigid semantic categories, our framework facilitates the observation of within-person habitual drift. This capability positions the system as a potential tool for identifying subtle behavioral indicators associated with early-stage cognitive decline in future longitudinal studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data Karpekov, Alexander Iyer, Archith Dhekane, Sourish Gunesh Chernova, Sonia Plötz, Thomas Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition I.2.6; C.3; I.2.10; H.5.2; J.3 Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific nuances essential, for example, for tracking individual health over time. To address this, we propose DISCOVER, a framework for discovering and annotating Patterns of Daily Living (PDL) - fine-grained, recurring sequences of sensor events that emerge directly from a resident's unique routines. DISCOVER utilizes a self-supervised feature extraction and representation-aware clustering pipeline, supported by a custom visualization interface that enables experts to interpret and label discovered patterns with minimal effort. Our evaluation across multiple smart-home environments demonstrates that DISCOVER identifies cohesive behavioral clusters with high inter-rater agreement while achieving classification performance comparable to fully-supervised baselines using only 0.01% of the labels. Beyond reducing annotation overhead, DISCOVER establishes a foundation for longitudinal analysis. By grounding behavior in a resident's specific environment rather than rigid semantic categories, our framework facilitates the observation of within-person habitual drift. This capability positions the system as a potential tool for identifying subtle behavioral indicators associated with early-stage cognitive decline in future longitudinal studies. |
| title | DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition I.2.6; C.3; I.2.10; H.5.2; J.3 |
| url | https://arxiv.org/abs/2503.01733 |