Daily Life Pattern Extraction in Single Person Households

Recently, research utilizing smart home technology for anomaly detection in single-person households has been actively conducted. However, real-world sensor data flaws, such as noise and battery discharge, cause the 'Garbage In, Garbage Out (GIGO)' problem, thereby undermining model reliability. Furthermore, existing research on fixed sensors faces limitations in logically explaining complex life patterns, such as 'sleep', as it relies solely on simple activation status. To address these issues and lay the foundation for anomaly detection based on daily life patterns, this study proposes a robust extraction methodology that fuses heterogeneous sensors including respiration, heart rate and illuminance and transforms raw data into 'Interpretable Representations', referred to as daily life patterns such as sleep, outing, and activity. Specifically, we construct these patterns by determining dynamic thresholds via K-Means clustering and comprehensively considering global configuration of all sensors. Case Study results demonstrate that the proposed method resolves the GIGO problem by clearly distinguishing 'long-term absences' from 'long sleep' and accurately differentiating actual missing data marked as ' NaN ' from non-occurrences represented as '0', thereby enabling the extraction of robust daily life patterns.