IOT 멀티 센서 기반 빅데이터 추출을 위한 적응적 센싱 및 모니터링 기술

  • Published : 2017.07.14

Abstract

Keywords

References

  1. "실시간&IoT: 빅데이터 분석의 새로운 도전과 해법", IDG, 2015.
  2. Lee Seung Hun. "Trillion 센서 IoT 시대 열고 있다" LGERI Report, 2017.
  3. A. Rasooly, "Biosensor technologies", Methods, vol. 37, pp. 1-3, 2005 https://doi.org/10.1016/j.ymeth.2005.05.004
  4. 황교선, 김상경, 김태송. "바이오센서". J. Kor. Sensors Soc 18.4 (2009): 251-262.
  5. A. Sadeh, "The role and validity of actigraphy in sleep medicine: An update", Sleep Medicine Reviews, Vol. 15 pp. 259-267, 2011. https://doi.org/10.1016/j.smrv.2010.10.001
  6. Taekyum Kim,et al. "Implementation of Indoor Positioning System using Low Cost Inertial Measurement Unit", THE INSTITUTE OF ELECTRONICS ENGINEERS OF KOREA(2009): 720-721.
  7. 이우재, 조성환 "생체 신호처리 센서IC 기술동향", 전자공학회지 40.6 (2013): 39-45.
  8. 민명기, 최선탁, 조위덕, "피에조 센서를 이용한 수면 중 심박 신호 검출에 관한 연구", 2014한국통신학회동계종합학술발표회, 2014.1, 1-2 (2 pages)
  9. 조위덕, et al. "IoT를 사용한 라이프로그 빅데이터기반 라이프스타일(생활패턴) 분석 및 웰니스 예측케어 서비스시스템", 한국통신학회지 (정보와통신) 31.12 (2014): 17-24.
  10. 신동규, et al. "유비쿼터스 홈네트워크 시스템에서 은닉 마르코프 모델을 이용한 사용자 행동 상태 분석 및 예측 알고리즘", 인터넷정보학회논문지 제12권 제2호, 2011.4, 9-17 (9 pages)
  11. 서효석, et al. "컨텍스트 인식 기반 개인화 추천 서비스를 위한 사용자 행동 패턴 추론 모델", 디지털정책연구 10(2), 293-297, 2012
  12. Lara, Oscar D., and Miguel A. Labrador. "A survey on human activity recognition using wearable sensors", IEEE Communications Surveys and Tutorials 15.3 (2013): 1192-1209. https://doi.org/10.1109/SURV.2012.110112.00192
  13. J. Iglesias, J. Cano, A. M. Bernardos, and J. R. Casar, "A ubiquitous activity-monitor to prevent sedentariness", in Proc. IEEE Conference on Pervasive Computing and Communications, 2011.
  14. D. Choujaa and N. Dulay, "Tracme: Temporal activity recognition using mobile phone data," in IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, vol. 1, pp. 119-126, 2008.
  15. J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, and I. Korhonen, "Activity classification using realistic data from wearable sensors," IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 1, pp. 119-128, 2006. https://doi.org/10.1109/TITB.2005.856863
  16. L. C. Jatoba, U. Grossmann, C. Kunze, J. Ottenbacher, and W. Stork, "Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity," in 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5250-5253, 2008.
  17. T. Brezmes, J.-L. Gorricho, and J. Cotrina, "Activity recognition from accelerometer data on a mobile phone," in Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, vol. 5518, pp. 796-799, Springer Berlin/Heidelberg, 2009.
  18. K. Oh, H.-S. Park, and S.-B. Cho, "A mobile context sharing system using activity and emotion recognition with bayesian networks," in International Conference on Ubiquitous Intelligence Computing, pp. 244-249, 2010.
  19. E. M. Tapia, S. S. Intille, W. Haskell, K. Larson, J. Wright, A. King, and R. Friedman, "Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor," in Proc. International Symposium on Wearable Computers, 2007.
  20. T.-P. Kao, C.-W. Lin, and J.-S. Wang, "Development of a portable activity detector for daily activity recognition," in IEEE International Symposium on Industrial Electronics, pp. 115-120, 2009.
  21. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher, "Activity recognition and monitoring using multiple sensors on different body positions," in Proc. International Workshop on Wearable and Implantable Body Sensor Networks, (Washington, DC, USA), IEEE Computer Society, 2006.
  22. O. D. Lara, A. J. Perez, M. A. Labrador, and J. D. Posada, "Centinela: A human activity recognition system based on acceleration and vital sign data," Journal on Pervasive and Mobile Computing, 2011.
  23. L. Bao and S. S. Intille, "Activity recognition from user-annotated acceleration data," in Pervasive, pp. 1-17, 2004.
  24. Y. Hanai, J. Nishimura, and T. Kuroda, "Haar-like filtering for human activity recognition using 3d accelerometer," in IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 675-678, 2009.
  25. Z.-Y. He and L.-W. Jin, "Activity recognition from acceleration data using ar model representation and svm," in International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2245-2250, 2008.
  26. M. Berchtold, M. Budde, H. Schmidtke, and M. Beigl, "An extensible modular recognition concept that makes activity recognition practical," in Advances in Artificial Intelligence, Lecture Notes in Computer Science, pp. 400-409, Springer Berlin/Heidelberg, 2010.
  27. D. Riboni and C. Bettini, "Cosar: hybrid reasoning for context-aware activity recognition," Personal and Ubiquitous Computing, vol. 15, pp. 271-289, 2011. https://doi.org/10.1007/s00779-010-0331-7
  28. T. Brezmes, J.-L. Gorricho, and J. Cotrina, "Activity recognition from accelerometer data on a mobile phone," in Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, vol. 5518 of Lecture Notes in Computer Science, pp. 796-799, Springer Berlin / Heidelberg, 2009.
  29. M. Berchtold, M. Budde, D. Gordon, H. Schmidtke, and M. Beigl, "Actiserv: Activity recognition service for mobile phones," in International Symposium on Wearable Computers, pp. 1-8, 2010.
  30. M. Stikic, D. Larlus, and B. Schiele, "Multi-graph based semisupervised learning for activity recognition," in International Symposium on Wearable Computers, pp. 85-92, 2009.
  31. M. Stikic, D. Larlus, S. Ebert, and B. Schiele, "Weakly supervised recognition of daily life activities with wearable sensors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2521-2537, 2011. https://doi.org/10.1109/TPAMI.2011.36
  32. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. eleventh annual conference on Computational learning theory, pp. 92-100, ACM, 1998.
  33. J. Quinlan, C4.5: programs for machine learning. Morgan Kaufmann series in machine learning, Morgan Kaufmann Publishers, 1993.
  34. P. Antal, "Construction of a classifier with prior domain knowledge formalised as bayesian network," in Proc. 24th Annual Conference of the IEEE Industrial Electronics Society, vol. 4, pp. 2527-2531, 1998.
  35. H. Zhang, "The Optimality of Naive Bayes.," in FLAIRS Conference, AAAI Press, 2004.
  36. I. H. Witten and E. Frank, Data Mining, Practical Machine Learning Tools and Techniques. Elsevier, 2 ed., 2005.
  37. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
  38. S. Gallant, "Perceptron-based learning algorithms," IEEE Trans. Neural Netw., vol. 1, no. 2, pp. 179-191, 1990. https://doi.org/10.1109/72.80230