TY  - JOUR
T1  - Event Cognition-based Daily Activity Prediction Using Wearable Sensors
AU  - Lee, Chung-Yeon 
AU  - Kwak, Dong Hyun 
AU  - Lee, Beom-Jin 
AU  - Zhang, Byoung-Tak 
JO  - Journal of KIISE, JOK
PY  - 2016
DA  - 2016/1/14
DO  - 
KW  - wearable sensors
KW  - daily activity prediction
KW  - event cognition
KW  - heterogeneous data learning
KW  - event-activity mapping table
AB  - 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.