A hybrid self-organizing maps and particle swarm optimization approach

2004 ◽  
Vol 16 (9) ◽  
pp. 895-915 ◽  
Author(s):  
Xiang Xiao ◽  
Ernst R. Dow ◽  
Russell Eberhart ◽  
Zina Ben Miled ◽  
Robert J. Oppelt
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Self Organizing Maps ◽  
Swarm Optimization ◽  
Self Organizing

scholarly journals 4W1H and Particle Swarm Optimization for Human Activity Recognition

2011 ◽  
Vol 15 (7) ◽  
pp. 793-799 ◽  
Author(s):  
Leon Palafox ◽  
◽  
Hideki Hashimoto
Keyword(s):  
Particle Swarm Optimization ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Particle Swarm ◽  
Human Activity Recognition ◽  
Self Organizing Maps ◽  
Swarm Optimization ◽  
Self Organizing

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 usesself-organizing mapsto classify movements for the “How” variable of 4W1H, as well asparticle swarm optimization clusteringtechniques 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.

Sign in / Sign up

Export Citation Format

Share Document