Abstract
Analysis of human movement data is becoming more popular in several applications. Particularly, analyzing sport movement data has been demanding. Most of the attempts made on this are, however, have focused on spatial aspects of the movement to extract some movement characteristics, such as positional pattern and similarities. This paper analyses walking observations to extract behavioural pattern of attributes (such as speed and heart rate) of a person to examine the effects of different contextual conditions on behavioural movement patterns. Particularly, experiments were conducted to explore the effect of day time, tiredness, and gender of the person on “movement parameter profiles”. The key element of this research is projection of movement parameter profiles into an informative pattern that describes the behavioural movement pattern of a person. To illustrate the effect of different conditions, a simple distance function has been used to compare patterns considering the change of mentioned conditions. The results show that the gender of a person is among the contexts that considerably affect behavioural movement patterns in the case study.
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Amouzandeh, K., Goudarzi, S., Karimipour, F. (2018). Contextual Analysis of Spatio-Temporal Walking Observations. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_32
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