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

모바일 및 웨어러블 센서 데이터를 이용한 다양한 식사상황 인식 시스템

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


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


  1. M. Li, V. Rozgic, G. Thatte, S. Lee, A. Emken, M. Annavaram, U. Mitra, M. D. Spruij and S. Narayanan, "Multimodal physical activity recognition by fusing temporal and cepstral information," IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol. 18, No. 4, pp. 369-380, 2010.
  2. Y. J. Hong, I. J. Kim, S. C. Ahn and H. G. Kim, "Mobile health monitoring system based on activity recognition using accelerometer," Simulation Modelling Practice and Theory, Vol. 18, No. 4, pp. 446-455, 2010.
  3. A. Mannini and A. M. Sabatini, "Machine learning methods for classifying human physical activity from on-body accelerometers," Sensors, Vol. 10, No. 2, pp. 1154-1175, 2010.
  4. J. R. Kwapisz, G. M. Weiss and S. A. Moore, "Activity recognition using cell phone accelerometers," ACM SigKDD Explorations Newsletter, Vol. 12, No. 2, pp. 74-82, 2011.
  5. A. M. Khan, Y. K. Lee, S. Y. Lee and T. S. Kim, "A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer," IEEE Trans. on Information Technology in Biomedicine, Vol. 14, No. 5, pp. 1166-1172, 2010.
  6. J. H. Hong, S. L. Yang and S.-B. Cho, "ConaMSN: A context-aware messenger using dynamic Bayesian networks with wearable sensors," Expert Systems with Applications, Vol. 37, No. 6, pp. 4680-4686, 2010.
  7. S. Dernbach, B. Das, N. C. Krishnan, B. L. Thomas and D. J. Cook, "Simple and complex activity recognition through smart phones," 8th Int. Conf. on IEEE in Intelligent Environments, pp. 214-221, 2012.
  8. M. Marchiori, "W5: The five w's of the world wide web," Trust Management, pp. 7-32, 2004.
  9. S. Jang and W. Woo, "Ubi-UCAM: A unified context-aware application model," Modeling and Using Context, pp. 178-189, 2003.
  10. A. N. Leont'ev, "The problem of activity in psychology," Soviet Psychology, Vol. 13, No. 2, pp. 4-33, 1974.
  11. B. A. Nardi, "Studying context: A comparison of activity theory, situated action models, and distributed cognition," Context and Consciousness: Activity Theory and Human-Computer Interaction, pp. 69-102, 1996.
  12. L. Suchman and Human-Machine Reconfigurations, Plans and Situated Actions, Cambridge University, 1986.
  13. A. Hofleitner, R. Herring, P. Abbeel and A. Bayen, "Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network," IEEE Trans. on Intelligent Transportation Systems, Vol. 13, No. 4, pp. 1679-1693, 2012.
  14. Y. S. Lee and S.-B. Cho, "Mobile context inference using two-layered Bayesian networks for smartphones," Expert Systems with Applications, Vol. 40, No. 11, pp. 4333-4345, 2013.
  15. D. Kasper, G. Weidl, T. Dang, G. Breuel, A. Tamke, A. Wedel and W. Rosenstiel, "Object-oriented Bayesian networks for detection of lane change maneuvers," IEEE Intelligent Transportation Systems Magazine, Vol. 4, No. 3, pp. 19-31, 2012.