A Dining Context-Aware System with Mobile and Wearable Devices
Kee-Hoon Kim, Sung‐Bae Cho · 2015
With development of various sensors attached to mobile and wearable devices, recognizing user's current context and giving an appropriate service come to hot issue. In this paper, we propose the context-aware system recognizing user's dining context that can occur within a great variety of contexts. The model uses low-level sensor data from mobile and wristwearable devices that can be widely available in daily life. To cope with innate complexity and fuzziness in high-level contexts like dining, a context model represents the related contexts systemically based on 4 components of activity theory and 5 W's, and tree-structured Bayesian network can recognizes the dining context probabilistically. To verify the proposed system, we collected 383 minutes of data from 4 people in a week and found that the proposed method outperforms the conventional machine learning methods, as to accuracy (94.57%). Also we built an Android application to investigate its practicality, and conducted a scenario-based test to investigate the effect of individual context for recognition.