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With the increase of the number of elderly people living alone around theworld, there is a growing demand for sensor-based detection of anomalousbehaviors. Although smart homes with ambient sensors could be useful fordetecting such anomalies, there is a problem of lack of sufficient real datafor developing detection algorithms. For coping with this problem, severalsensor data simulators have been proposed, but they have not been able to modelappropriately the long-term transitions and correlations between anomalies thatexist in reality. In this paper, therefore, we propose a novel sensor datasimulator that can model these factors in generation of sensor data. Anomaliesconsidered in this study were classified into three types of \textit{stateanomalies}, \textit{activity anomalies}, and \textit{moving anomalies}. Thesimulator produces 10 years data in 100 min. including six anomalies, two foreach type. Numerical evaluations show that this simulator is superior to thepast simulators in the sense that it simulates well day-to-day variations ofreal data.

Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone