Abstract. This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.

-Early intervention approaches for facilitating motor development in infants and children with Down syndrome have traditionally emphasised the acquisition of motor milestones. As increasing evidence suggests that motor milestones have limited predictive power for long-term motor outcomes, researchers have shifted their focus to understanding the underlying perceptual-motor competencies that influence motor behaviour in Down syndrome. This paper outlines a series of studies designed to evaluate the nature and extent of perceptual-motor impairments present in children with Down syndrome. 12 children with Down syndrome between the ages of 8-15 years with adaptive ages between 3-7 years (mean age = 5.6 years +/-1.45 years) and a group of 12 typically developing children between the ages of 4-8 years (mean age = 5.4 +/-1.31 years) were tested on their ability to make increasingly complex perceptual discriminations of motor behaviours. The results indicate that children with Down syndrome are able to make basic perceptual discriminations but show impairments in the perception of complex visual motion cues. The implications of these results for early intervention are discussed.