Entropy-based abnormal activity detection fusing RGB-D and domotic sensors

Fernandez-Carmona, Manuel and Cosar, Serhan and Coppola, Claudio and Bellotto, Nicola (2017) Entropy-based abnormal activity detection fusing RGB-D and domotic sensors. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 16-18 Nov 2017, Daegu, Korea.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

The automatic detection of anomalies in Active and Assisted Living (AAL) environments is important for monitoring the wellbeing and safety of the elderly at home. The integration of smart domotic sensors (e.g. presence detectors) with those ones equipping modern mobile robots (e.g. RGBD camera) provides new opportunities for addressing this challenge. In this paper, we propose a novel solution to combine local activity levels detected by a single RGBD camera with the global activity perceived by a network of domotic sensors. Our approach relies on a new method for computing such a global activity using various presence detectors, based on the concept of entropy from information theory. This entropy effectively shows how active a particular room or environment’s area is. The solution includes also a new application of Hybrid Markov Logic Networks (HMLNs) to merge different information sources for local and global anomaly detection. The system has been tested with RGBD data and a comprehensive domotic dataset containing data entries from 37 different domotic sensors (presence, temperature, light, energy consumption, door contact), which is made publicly available. The experimental results show the effectiveness of our approach and the potential for complex anomaly detection in AAL settings.

Keywords:robotics, smart-home, Human Activity Recognition, AAL
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:28779
Deposited On:02 Oct 2017 12:56

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