Visual Confusion Recognition in Movement Patterns from Walking Path and Motion Energy (oral)

Published in International Conference on Smart Homes and Health Telematics (ICOST), 2017

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Abstract: For elderly people healthcare in ambient living environments, recognizing confusion states in an automatic and non-contact manner is essential. In this work we provide a visual approach to confusion recognition consisting of behavior monitoring and movement pattern analysis. To collect data for evaluation, we created a dataset from a search experiment. After extracting and analyzing the movement patterns, we achieved a recognition rate of 89.6% when cross-validating over different subjects and 88.9% when testing on a new set of samples. To our knowledge, we are the first to investigate confusion recognition using visual information. Our work shows that the mental confusion can be effectively recognized based on the movement pattern.

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