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By Anil K. Jain

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Identification of outliers Pre-processed data are displayed as histograms showing the frequency of occurrence of sensor active state. 2. The data were grouped into six classes corresponding to different times of the day. The time periods are 0–4, 4–8, 8–12, 12–16, 16–20, and 20–24. With some prior knowledge of the patterns of behavior of the occupant, outliers can easily be identified and removed from the data set as anomalous data points if explained by any one of the error sources described above.

Conf. on Sensors 2004, 575–578 (2004). 10. jp/toukei/saikin/hw/fukushi/ 04/, (2004). 11. : The Aware Home: A Living Laboratory for Ubiquitous Computing Research. In: Proc. of the 2nd Int. Workshop on Cooperative Buildings, 191–198 (1999). 12. : Behavior prediction based on daily-life record database in distributed sensing space. In: Proc. of the IEEE/RSJ Int. Conf. on Intell. Robots and Systems, 1833–1839 (2005). 2 A Pervasive Sensor System for Nursing Care Support 33 13. : 3D ultrasonic tagging system for observing human activity.

41, No. 5, 509–518 (2003). 20. : Assistive intelligent environments for automatic in-home health monitoring. D. Dissertation, Robotics Institute, Carnegie Mellon University, Pittsburgh (2005). Chapter 3 Anomalous Behavior Detection: Supporting Independent Living Dorothy N. Monekosso and Paolo Remagnino Abstract In this chapter we describe a system for supporting independent living and enhancing quality of life of older persons. The home is equipped with non-intrusive standard home automation technology and an array of sensors that captures the status of appliances.

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