@article{MAB660A29,
title = "Event Cognition-based Daily Activity Prediction Using Wearable Sensors",
journal = "Journal of KIISE, JOK",
year = "2016",
issn = "2383-630X",
doi = "",
author = "Chung-Yeon Lee,Dong Hyun Kwak,Beom-Jin Lee,Byoung-Tak Zhang",
keywords = "wearable sensors,daily activity prediction,event cognition,heterogeneous data learning,event-activity mapping table",
abstract = "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."
}