具自我適應能力之家庭行為辨識系統研究

由於近年智慧家庭的興起，為了提供使用者更好的服務，了解使用者的情境與需求成為一個很重要的課題。得知使用者的行為有助於智慧家庭更了解使用者的需求，提供更適合的服務。使用者行為呈現的方式充滿變化，無法事先完整地定義。因此，本研究以貝氏網路將使用者的行為分開獨立建立網路，再將個別網路集合建構行為模型，然後以此模型為基礎完成行為辨識系統，使在情境資訊不確定時仍能做出有效的推論。因為使用者的習性與所處環境不盡相同，本研究透過收集訓練樣本，以機器學習的方式達到系統個人化的效果。本研究建立一個具有調整模型能力的行為辨識系統，以適應動態環境的變化。最後，因為在一般家庭中往往不只一位使用者，系統需要同時辨識不同使用者的行為，所以本研究提出一套方法輸出多種辨識結果，使系統在多名使用者的家庭中也能進行辨識。

關鍵字

行為辨識；機率推論；貝式網路；智慧家庭

並列摘要

In order to provide better service for the user in a smart home, how to understand the user's situation and needs becomes an important issue. Knowing the user's activity contributes to the smart home that knows the needs of the user and provides more appropriate service. Because different users may conduct activities in different ways, we cannot completely define all possible activities in advance. This study proposes an activity recognition system by using Bayesian network so that system can make inferences even when context information is uncertain. We have first created separate networks about each user's activity, and then we have combined each network to construct the activity model to complete the activity recognition process. Because each user’s habits and the environment are not the same, we have built a personalized system by using a machine learning technique through collecting training data. In addition, the system has the ability to adjust the model parameters, so that the system can adapt to dynamic environment. Finally, considering that a family often consists of more than one member in general, a smart home needs to support different users who may perform different activities at the same time. Therefore, this thesis presents an algorithm to produce multiple recognition results, so the system can recognize activities performed by multiple users.

並列關鍵字

Activity Recognition ； Probability Inference ； Bayesian Network ； Smart Home

參考文獻

[1]J. York and P. C. Pendharkar, “Human–computer interaction issues for mobile computing in a variable work context,” International Journal of Human-Computer Studies, vol. 60, no. 5, pp. 771–797, 2004.

[7]C.-L. Wu, C.-F. Liao, and L.-C. Fu, “Service-Oriented Smart-Home Architecture Based on OSGi and Mobile-Agent Technology,” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 2, pp. 193–205, Mar. 2007.

[8]J. Xu, Y.-H. Lee, W.-T. Tsai, W. Li, Y.-S. Son, J.-H. Park, and K.-D. Moon, “Ontology-Based Smart Home Solution and Service Composition,” in Proceedings of International Conference on Embedded Software and Systems (ICESS), 2009, pp. 297–304.

[11]D. Guan, T. Ma, W. Yuan, and Y. Lee, “Review of Sensor-based Activity Recognition Systems,” IETE Technical Review, vol. 28, no. 5, p. 418, 2011.

[12]J. Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.

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具自我適應能力之家庭行為辨識系統研究

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