Detection of Temporal Changes in the Behavior of Older Adults Living Alone by Means of Continuous Data Collection in the Home

Background: In Japan, the number of older adults living alone has been increasing, raising the risk of unnoticed health decline or solitary death. Continuous monitoring using sensors can help detect behavioral changes indicating health issues and has the potential to support both older adults and their families. Methods: We obtained behavior and temperature data, continuously recorded over a long period at 15-min intervals from sensors installed in the homes of nine older adults living alone. After data cleaning, behavioral signals were analyzed using Fourier spectral analysis and multiple regression to extract 13-dimensional behavioral feature vectors. We attempted to detect temporal changes and behavioral characteristics by whitening these data and performing correspondence analysis. Results: Spectral analysis revealed 24-hour periodicity in all users’ behavior. Based on changes in the maximum component value and adjusted R2, individuals were classified into a stable group (SG) and a fluctuating group (FG). Boundary variance and false error analyses confirmed that behavioral temporal changes and individual characteristics could be detected objectively. Conclusions: The findings showed that temporal changes in daily behavior among older adults living alone can be detected using simple continuous sensor data, suggesting potential for early detection of health-related changes and preventive support in home.