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2024
Journal Article
Titel
Ubiquitous Multi-Occupant Detection in Smart Environments
Abstract
Recent advancements in ubiquitous computing have emphasized the need for privacy-preserving occupancy detection in smart environments to enhance security. This work presents a novel occupancy detection solution utilizing privacy-aware sensing technologies. The solution analyzes time-series data to detect not only occupancy as a binary problem, but also determines whether one or multiple individuals are present in an indoor environment. On three real-world datasets, our models outperformed various state-of-the-art algorithms, achieving F1-scores up to 94.91% in single-occupancy detection and a macro F1-score of 91.55% in multi-occupancy detection. This makes our approach a promising solution for improving security in smart environments.
Author(s)
Tags
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Branche: Information Technology
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Research Line: Human computer interaction (HCI)
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Research Line: Machine learning (ML)
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LTA: Monitoring and control of processes and systems
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LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
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Smart environments
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Multivariate time series
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Machine learning
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ATHENE
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CRISP