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Event Cognition-based Daily Activity Prediction Using Wearable Sensors
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 7,  2016, pp.781-785
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.7.781
 Title & Authors
Event Cognition-based Daily Activity Prediction Using Wearable Sensors
Lee, Chung-Yeon; Kwak, Dong Hyun; Lee, Beom-Jin; Zhang, Byoung-Tak;
Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.
wearable sensors;daily activity prediction;event cognition;heterogeneous data learning;event-activity mapping table;
 Cited by
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