Human behavioural analysis with self-organizing map for ambient assisted living

Appiah, Kofi and Hunter, Andrew and Lotfi, A. and Waltham, Chris and Dickinson, Patrick (2014) Human behavioural analysis with self-organizing map for ambient assisted living. In: 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, 6-11 July 2014, Beijing, China.

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Human behavioural analysis with self-organizing map for ambient assisted living

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Abstract

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints. © 2014 IEEE.

Keywords:Behavioral research, Conformal mapping, Low power electronics, Self organizing maps, Ambient assisted living, Classification errors, Monitoring activities, Object detection algorithms, Probabilistic modeling, Self-organising features, Surveillance systems, Unsupervised neural networks, Monitoring
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:17010
Deposited On:10 Apr 2015 08:20

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