Generating Training Dataset of Machine Learning Model for Context-Awareness in a Health Status Notification Service

사용자 건강 상태알림 서비스의 상황인지를 위한 기계학습 모델의 학습 데이터 생성 방법

  • Received : 2019.10.11
  • Accepted : 2019.12.13
  • Published : 2020.01.31


In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.

다양한 분야에서 활용되는 상황인지 시스템은 상황정보를 획득하기 위한 추상화 과정에서 규칙 기반의 인공기능 기술이 기존에 사용되었다. 그러나 서비스에 대한 사용자의 요구사항이 다양해지고 사용되는 데이터의 증대로 규칙이 복잡해지면서 규칙 기반 모델의 유지보수와 비정형 데이터를 처리하는데 어려움이 있다. 이러한 한계점을 극복하기 위해 많은 연구들에서는 상황인지 시스템에 기계학습 기술을 적용하였으며, 이러한 기계학습 기반의 모델을 상황인지 시스템에 사용하기 위해서는 주기적으로 학습 데이터를 제공해야 한다. 이에 기계학습 기반 상황인지 시스템에 대한 선행연구에서는 여러 개의 기계학습 모델을 적용하기 위한 학습 데이터 생성, 제공 등의 과정을 보였으나 제한된 종류의 기계학습 모델만을 적용 가능하여 확장성이 고려되어야 한다. 본 논문은 기계학습 기반의 상황인지 시스템의 확장성을 고려한 기계학습 모델의 학습 데이터 생성 방법을 제안한다. 제안하는 방법은 시스템의 확장성을 고려하여 기계학습 모델의 요구사항을 반영할 수 있는 학습 데이터 생성 모델을 정의하고 학습 데이터 생성 모듈을 바탕으로 각각의 기계학습 모델의 학습 데이터를 생성하는 것이다. 시스템의 확장성의 검증을 위해 실험에서는 노인의 건강상태 알림 서비스를 위한 심박상태 분석 모델을 대상으로 한 학습데이터 생성 스키마를 기반으로 학습데이터 생성 모델을 정의하고 실환경에서 정의된 모델을 S/W에 적용하여 학습데이터를 생성한다. 또한 생성된 학습데이터의 유효성을 검증하기 위해 사용되는 기계학습 모델에 생성한 학습데이터를 학습시켜 정확도를 비교하는 과정을 보인다.



Supported by : 한국연구재단


  1. G. Manogaran and D. Lopez, "Health data analytics using scalable logistic regression with stochastic gradient descent," International Journal of Advanced Intelligence Paradigms, Vol.10, No.1-2, pp.118-132, 2018.
  2. J.-W. Lee, H.-S. Lim, D.-W. Kim, S.-A. Shin, J. Kim, B. Yoo, and K.-H. Cho, "The development and implementation of stroke risk prediction model in National Health Insurance Service's personal health record," Computer Methods and Programs in Biomedicine, Vol.153, pp.253-257, 2018.
  3. N. Bouri and S. Ravi, "Going mobile: how mobile personal health records can improve health care during emergencies," JMIR mHealth uHealth, Vol.2, No.1, e8, 2014.
  4. N. Khozouie, F. Fotouhi-Ghazvini, and B. Minaei-Bidgoli, "Ontological MobiHealth system," Indonesian J. Elect. Eng. Comput. Sci., Vol.10, No.1, pp.309-319, 2018.
  5. S. Huang, L. Li, H. Cai, B. Xu, G. Li, and L. Jiang, "A Configurable WoT Application Platform Based on Spatiotemporal Semantic Scenarios," IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.49, No.1, pp.123-135, 2017.
  6. L. Mainetti, V. Mighali, L. Patrono, and P. Rametta, "A novel Rule-based Semantic Architecture for IoT Building Automation Systems," Software, Telecommunications and Computer Networks (SoftCOM), pp.124-131, 2015.
  7. L. Pessoa, P. Fernandes, T. Castro, V. Alves, G. N. Rodrigues, and H. Carvalho, "Building reliable and maintainable Dynamic Software Product Lines: An investigation in the Body Sensor Network domain," Information and Software Technology, Vol.86, pp.54-70, 2017.
  8. J. G. Nalepa and S. Bobek, "Rule-based solution for context-aware reasoning on mobile devices," Computer Science and Information Systems, Vol.11, No.1, pp.171-193, 2014.
  9. N. Kolbe, A. Zaslavsky, S. Kubler, J. Robert, and Y. Le Traon, "Enriching a Situation Awareness Framework for IoT with Knowledge Base and Reasoning Components," In International and Interdisciplinary Conference on Modeling and Using Context, Springer, Cham, pp.41-54, 2017.
  10. Z. Bahramian, R. Ali Abbaspour, and C. Claramunt, "A cold start context-aware recommender system for tour planning using artificial neural network and case based reasoning," Mobile Information Systems, 2017.
  11. M. Shin, W. Paik, B. Kim, and S. Hwang, "An IoT Platform with Monitoring Robot Applying CNN-Based Context-Aware Learning," Sensors, Vol.19, No.11, pp.1-13, 2019.
  12. L. Gomes, C., Ramos, A., Jozi, B., Serra, L., Paiva, and Z. Vale, "IoH: A Platform for the Intelligence of Home with a Context Awareness and Ambient Intelligence Approach," Future Internet, Vol.11, No.3, pp.58, 2019.
  13. N. Polyzotis, S. Roy, S. E. Whang, and M. Zinkevich, "Data Management Challenges in Production Machine Learning," Proceedings of the 2017 ACM International Conference on Management of Data, ACM, pp.1723-1726, 2017.
  14. M. H. Kabir, M. R. Hoque, H. Seo, and S. H. Yang, "Machine learning based adaptive context-aware system for smart home environment," International Journal of Smart Home, Vol.9, No.11, pp.55-62, 2015.
  15. M. H., Kabir, M. R., Hoque, H., Seo, and S. H. Yang, "Boolean Control Network Based Modeling for Context-Aware System in Smart Home," International Journal of Smart Home, Vol.10, No.4, pp.65-76, 2016.
  16. B. Ospan, N. Khan, J. Augusto, M. Quinde, and K. Nurgaliyev, "Context aware virtual assistant with casebased conflict resolution in multi-user smart home environment," 2018 International Conference on Computing and Network Communications (CoCoNet), IEEE, pp.36-44, 2018.
  17. P. Jiang, J. Winkley, C. Zhao, R. Munnoch, G. Min, and L. T. Yang, "An intelligent information forwarder for healthcare big data systems with distributed wearable sensors," IEEE systems journal, Vol.10, No.3, pp.1147-1159, 2014.
  18. O. Banos, R. Garcia, J. A. Holgado-Terriza, M. Damas, H. Pomares, I. Rojas, A. Saez, and C. Villalonga, "mHealthDroid: a novel framework for agile development of mobile health applications," International Workshop on Ambient Assisted Living, Springer, Cham, pp.91-98, 2014.
  19. T. Zoppi, A. Ceccarelli, and A. Bondavalli, "Contextawareness to improve anomaly detection in dynamic service oriented architectures," International Conference on Computer Safety, Reliability, and Security, Springer, Cham, pp.145-158, 2016.
  20. Y. Bai, H. Ji, Q. Han, J. Huang, and D. Qian, "MidCASE: a service oriented middleware enabling context awareness for smart environment," 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), IEEE, pp.946-951, 2007.
  21. L. O. Colombo-Mendoza, R. Valencia-Garcia, A. Rodriguez- Gonzalez, G. Alor-Hernandez, and J. J. Samper-Zapater, "RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes," Expert Systems with Applications, Vol.42, No.3, pp.1202-1222, 2015.
  22. P. H. Wu, G. J. Hwang, and W. H. Tsai, "An expert systembased context-aware ubiquitous learning approach for conducting science learning activities," Journal of Educational Technology & Society, Vol.16, No. 4, pp.217-230, 2013.
  23. D. Galar, A. Thaduri, M. Catelani, and L. Ciani, "Context awareness for maintenance decision making: A diagnosis and prognosis approach," Measurement, pp.137-150, 2015.
  24. A. I. Wang, and Q. K. Ahmad, "Camf-context-aware machine learning framework for android," Proceedings of the International Conference on Software Engineering and Applications (SEA 2010), CA, USA, 2010.
  25. H. Eldardiry, K. Sricharan, J. Liu, J. Hanley, B. Price, O. Brdiczka, and E. Bart, "Multi-source fusion for anomaly detection: using across-domain and across-time peergroup consistency checks," JoWUA, Vol.5, No.2, pp.39-58, 2014.