JACIII Vol.15 No.7 pp. 793-799
doi: 10.20965/jaciii.2011.p0793


4W1H and Particle Swarm Optimization for Human Activity Recognition

Leon Palafox and Hideki Hashimoto

Institute of Industrial Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

February 21, 2011
May 27, 2011
September 20, 2011
self organizing maps, particle swarm optimization, 4W1H, activity recognition

This paper proposes a paradigm in the forensic area for detecting and categorizing human activities. The presented approach uses five base variables, referred to as 4W1H (“Who,” “When,” “What,” “Where,” and “How”) to describe the context in an environment. The proposed system uses self-organizing maps to classify movements for the “How” variable of 4W1H, as well as particle swarm optimization clustering techniques for the grouping (clustering) of data obtained from observations. The paper describes the hardware settings required for detecting these variables and the system designed to do the sensing.

Cite this article as:
Leon Palafox and Hideki Hashimoto, “4W1H and Particle Swarm Optimization for Human Activity Recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.7, pp. 793-799, 2011.
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