With the increase of the number of elderly people living alone around the world, there is a growing demand for sensor-based detection of anomalous behaviors. Although smart homes with ambient sensors could be useful for detecting such anomalies, there is a problem of lack of sufficient real data for developing detection algorithms. For coping with this problem, several sensor data simulators have been proposed, but they have not been able to model appropriately the long-term transitions and correlations between anomalies that exist in reality. In this paper, therefore, we propose a novel sensor data simulator that can model these factors in generation of sensor data. Anomalies considered in this study were classified into three types of \textit{state anomalies}, \textit{activity anomalies}, and \textit{moving anomalies}. The simulator produces 10 years data in 100 min. including six anomalies, two for each type. Numerical evaluations show that this simulator is superior to the past simulators in the sense that it simulates well day-to-day variations of real data.

翻译：随着全球独居老人数量的增加，基于传感器的异常行为检测需求日益增长。虽然配备环境传感器的智能家居有助于检测此类异常，但开发检测算法时面临真实数据不足的问题。为解决这一难题，已有多种传感器数据仿真器被提出，但它们未能恰当模拟现实场景中异常状态的长期演变规律及其关联性。因此，本文提出一种新型传感器数据仿真器，能够在数据生成过程中建模这些因素。本研究将异常行为分为三类：状态异常、活动异常和移动异常。该仿真器可在100分钟内生成包含六类异常（每类两种）的十年期数据。数值评估表明，该仿真器在模拟真实数据的逐日变化方面优于已有方案。