- Volume 43 Issue 5
DOI QR Code
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
- 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. https://doi.org/10.1109/TNSRE.2010.2053217
- 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. https://doi.org/10.1016/j.simpat.2009.09.002
- 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. https://doi.org/10.3390/s100201154
- 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. https://doi.org/10.1145/1964897.1964918
- 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. https://doi.org/10.1109/TITB.2010.2051955
- 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. https://doi.org/10.1016/j.eswa.2009.12.040
- 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.
- M. Marchiori, "W5: The five w's of the world wide web," Trust Management, pp. 7-32, 2004.
- S. Jang and W. Woo, "Ubi-UCAM: A unified context-aware application model," Modeling and Using Context, pp. 178-189, 2003.
- A. N. Leont'ev, "The problem of activity in psychology," Soviet Psychology, Vol. 13, No. 2, pp. 4-33, 1974.
- 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.
- L. Suchman and Human-Machine Reconfigurations, Plans and Situated Actions, Cambridge University, 1986.
- 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. https://doi.org/10.1109/TITS.2012.2200474
- 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. https://doi.org/10.1016/j.eswa.2013.01.018
- 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. https://doi.org/10.1109/MITS.2012.2203229