Fährmann, DanielDanielFährmannBoutros, FadiFadiBoutrosKubon, PhilippPhilippKubonKirchbuchner, FlorianFlorianKirchbuchnerKuijper, ArjanArjanKuijperDamer, NaserNaserDamer2023-11-292023-11-292024https://publica.fraunhofer.de/handle/publica/45733210.1007/s00521-023-09162-zRecent 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.enBranche: Information TechnologyResearch Line: Human computer interaction (HCI)Research Line: Machine learning (ML)LTA: Monitoring and control of processes and systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)Smart environmentsMultivariate time seriesMachine learningATHENECRISPjournal article