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A Context Recognition System for Various Food Intake using Mobile and Wearable Sensor Data

모바일 및 웨어러블 센서 데이터를 이용한 다양한 식사상황 인식 시스템

Kim, Kee-Hoon;Cho, Sung-Bae
김기훈;조성배

  • Received : 2015.08.17
  • Accepted : 2016.02.22
  • Published : 2016.05.15

Abstract

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.

Keywords

context awareness;activity recognition;Bayesian network;wearable computing;sensor data mining

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