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MOnCa2: 지능형 스마트폰 어플리케이션을 위한 사용자 이동 행위 인지와 경로 예측 기반의 고수준 콘텍스트 추론 프레임워크

MOnCa2: High-Level Context Reasoning Framework based on User Travel Behavior Recognition and Route Prediction for Intelligent Smartphone Applications

  • 김제민 (명지대학교 방목기초교육대학) ;
  • 박영택 (숭실대학교 컴퓨터학부)
  • 투고 : 2014.06.12
  • 심사 : 2014.12.19
  • 발행 : 2015.03.15

초록

MOnCa2는 스마트폰에 장착된 센서와 온톨로지 추론 기반의 지능형 스마트폰 어플리케이션 구축을 위한 프레임워크다. 기존에 연구되었던 MOnCa는 온톨로지 인스턴스로 등록된 센서 값에 대한 정보를 바탕으로 사용자의 현재 상황을 판단 및 추론하였다. 이러한 방식은 사용자의 공간 정보나 주변에 존재하는 객체가 무엇인지 판단하는 것은 가능하나 사용자의 물리적인 콘텍스트(이동 행위, 이동할 목적지 등등) 판단하는 것은 불가능했다. 본 논문에서 설명하는 MOnCa2는 사용자 개개인의 물리적인 콘텍스트를 판단 및 추론하기 위해 스마트폰의 장착된 센서를 바탕으로 행위 및 이동 상황에 대응하는 인지 모델을 구축하고, 구축된 모델을 기반으로 사용자의 실시간 행위 및 이동 상황에 대해 1차적인 추론을 수행하며, 추론된 1차적인 콘텍스트에 대해 온톨로지 기반의 2차 추론을 통해 지능형 어플리케이션에 필요한 고수준 사용자 콘텍스트를 생산한다. 따라서 본 논문은 스마트폰의 가속도 센서를 기반으로 사용자의 이동에 필요한 행위를 인지하는 기법, 스마트폰의 GPS 신호를 바탕으로 이동 목적지와 경로를 예측하는 기법, 온톨로지 실체화를 적용하여 고수준 콘텍스트를 추론하는 과정에 초점을 맞추어 설명을 한다.

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

키워드

과제정보

연구 과제번호 : 모바일 플랫폼 기반 계획 및 학습 인지 모델 프레임워크 기술 개발

연구 과제 주관 기관 : 정보통신기술진흥센터

참고문헌

  1. A. K. Dey., "Providing Architectural Support for Building Context-Aware Applications," PhD thesis, College of Computing, Georgia Institute of Technology, Dec. 2000.
  2. J.-M. Kim, M.-H. Kim, and Y.-T. Park, "MOnCa: Framework for Ontology-based Context Aware Smart Phone Applications," Proc. of the KIISE Korea Computer Congress 2011, Vol. 38, No. 7(C), pp. 369-381, 2011. (in Korean)
  3. Y. Her, S.-K. Kim, and Y.-T. Jin, "A Context- Aware Framework using Ontology for Smart Phone Platform," Journal of Digital Content Technology and its Applications, Vol. 4, No. 5, pp. 159-167, 2010. https://doi.org/10.4156/jdcta.vol4.issue5.19
  4. H. Chen, T. Finin, and A. Joshi, "An Ontology for Context-Aware Pervasive Computing Environments," Journal of The Knowledge Engineering Review, Vol. 18, No. 3, pp. 197-207, 2003. https://doi.org/10.1017/S0269888904000025
  5. D. Ejigu, M. Scuturici, L. Brunie, "Hybrid Approach to Collaborative Context-Aware Service Platform for Pervasive Computing," Journal of Computers, Vol. 3, No. 1, pp. 40-50, Jan. 2008.
  6. V. Manzoni, D. Maniloff, K. Kloeckl, C. Ratti, "Transportation mode identification and real-time co2 emission estimation using smartphones," Technical Report, Massachusetts Institute of Technology (MIT), 2011.
  7. L. Liao, D. J. Patterson, D. Fox, and H. Kautz, "Learning and Inferring Transportation Routines," Journal of Artificial Intelligence, Vol. 171, No. 5-6, pp. 311-331, 2007. https://doi.org/10.1016/j.artint.2007.01.006
  8. D. Ashbrook, and T. Starner, "Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users," Journal of Personal and Ubiquitous Computing, Vol. 7, No. 5, pp. 275-286, 2003. https://doi.org/10.1007/s00779-003-0240-0
  9. K. Tanaka, Y. Kishino, T. Terada, and S. Nishio, "A Destination Prediction Method using Driving Contexts and Trajectory for Car Navigation Systems," Proc. of the 2009 ACM symposium on Applied Computing, pp. 190-195, 2009.
  10. J. Krumm, "A Markov Model for Driver Turn Prediction," Proc. of SAE 2008 World Congress, 2008.
  11. D. Arthur, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, Series B (Methodological), pp. 1-38, 1977.
  12. A. Doucet, N. d. Freitas, K. Murphy, and S. Russell, "Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks," Proc. of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 176-183, 2000.
  13. J. Krumm, and E. Horvitz, "Predestination: Inferring Destinations from Partial Trajectories," Proc. of the 8th international conference on Ubiquitous Computing, pp. 243-260, 2006.
  14. Y.-C. Yang, T. Toida, C.-M. Hong, "Transportations Prediction Using Build-in Triaxial Accelerometer in Cell Phone," 2010.
  15. B. Nham, K. Siangliulue, and S. Yeung, "Predicting Mode of Transport from iPhone Accelerometer Data," Technical Report, Stanford University CS229 Machine Learning, 2008.
  16. A. Thiagarajan, J. Biagioni, T. Gerlich, and J. Eriksson, "Cooperative Transit Tracking using Smartphones," Proc. of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 85-98, 2010.
  17. S. Hemminki, P. Nurmi, and S. Tarkoma, "Accelerometer- Based Transportation Mode Detection on Smartphones," Proc. of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013.
  18. J.-M. Kim, H.-J. Beak, and Y.-T. Park, "An Approach of Learning Probabilistic Graphical Models for Smartphone Users' Travel Routes," Proc. of the KIISE Korea Computer Congress 2014, Vol. 41, No. 2(C), pp. 153-163, 2014. (in Korean)
  19. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking," Journal of the IEEE Transactions on Signal Processing, Vol. 50, No. 3, pp. 174-188, 2002. https://doi.org/10.1109/78.978374
  20. S. Russell, and P. Norbig, Artificial Intelligence A Modern Approach, 4th Ed., pp. 570-574, Prentice Hall, New Jersey, 2010.
  21. B. Motik, B. C. Grau, I. Horrocks, Z. Wu, A. Fokoue, and C. Lutz, (2012, Dec 11). OWL 2 Web Ontology Language Profiles [online]. Avaliable: http://www.w3.org/TR/owl2-profiles/ (downloaded 2012, Dec 11).
  22. N. Ravi, D. Nikhil, P. Mysore, and M. L. Littman, "Activity recognition from accelerometer data," Proc. of the Seventeenth Conference on Innovative Applications of Artificial Intelligence, pp. 1541-1546, 2005.
  23. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society-Series B, Vol. 39, No. 1, pp. 1-38, 1977.