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A Context Recognition System for Various Food Intake using Mobile and Wearable Sensor Data
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 5,  2016, pp.531-540
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.5.531
 Title & Authors
A Context Recognition System for Various Food Intake using Mobile and Wearable Sensor Data
Kim, Kee-Hoon; Cho, Sung-Bae;
Development of various sensors attached to mobile and wearable devices has led to increasing recognition of current context-based service to the user. In this study, we proposed a probabilistic model for recognizing user's food intake context, which can occur in a great variety of contexts. The model uses low-level sensor data from mobile and wrist-wearable devices that can be widely available in daily life. To cope with innate complexity and fuzziness in high-level activities like food intake, a context model represents the relevant contexts systematically based on 4 components of activity theory and 5 W's, and tree-structured Bayesian network recognizes the probabilistic state. To verify the proposed method, we collected 383 minutes of data from 4 people in a week and found that the proposed method outperforms the conventional machine learning methods in accuracy (93.21%). Also, we conducted a scenario-based test and investigated the effect contribution of individual components for recognition.
context awareness;activity recognition;Bayesian network;wearable computing;sensor data mining;
 Cited by
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