近年來，人口老化一直是個被關切與重視的問題，而隨著社會逐漸朝向高齡化社會發展，因此針對老年人口的關懷與照護，是相當重要的議題，而如何在平時尚未發生狀況時，就有一套建立於人體感測網路上有效且長期照護的機制，達到保障高齡人口的行動安全與危急監控，進而促進老人福祉與提供後端醫療人員取得必要的資訊與協助，是本論文設計的目的。
本研究特別針對人體感測網路中的照護來探討，並且透過感測器與感測器間接收到的資訊，導入學習的概念與機制，用來實現後續相關處理及應用，學習機制會使用貝氏推論與相關係數兩個具有指標性意義的相關性分析理論，來求得感測器與感測器間的危急關聯程度，並且透過關聯性的高低，反饋調控當每一次危急發生時，需應對及處理的狀況，而經由不斷學習的結果，準確率確實呈現大幅度之提升，並且使用學習的好處，在於當經由學習得知某感測器發生危急時，另外哪幾個感測器的資訊對其相對重要，並且能夠在第一時間，就對其他感測器做學習後結果的調控，經由實作過程發現此方法能達到預測其他感測器是否同時會發生危急的效果，遠比等到其他感測器主動偵測到自身真的發生危急，學習的方法來得更有效率且有效達到事前預測的水準。
在省電方面，因人體感測網路使用電池來供給電源，所以在省電的議題套用本論文提出的學習機制，可有效達到相當程度的省電，對比正常狀況全速傳送時，省電幅度達到45%，故本論文能達到長期有效監控之目的。

Aging of population has been a concerned and valued problem in recent years. The concern of the elderly has been a quite important issue as the society developing to an aging society gradually. Thus, the purpose of proposed system established an efficient mechanism in body sensor networks which used for long-term caring to promote the welfare of the elderly and provide the medical professional with necessary information.
This research especially discusses health care in body sensor networks, through the received information by sensors, implying the mechanism, which uses two significant analyzing theories, Bayesian theorem and correlation coefficient, to obtain the critical correlation between sensors.
Through the correlation between sensors, adjust every time as critical situations happen. By insistent learning, the accuracy indeed increases significantly. The advantage of mechanism is that when one sensor appears to be dangerous, other sensors’ information is going to be relatively important, and it can immediately adjust for other sensors as a result. Moreover, through the implementation process realizes this method can predict if other sensors is in emergency at the same time. In this way, the result of learning method is more efficient than other sensors that detect its own crisis truly happen and it can achieve the standard of predicting effectively.
In power saving side, for body sensor networks use batteries to supply power, applying the mechanism in this paper can efficiently achieve the power saving goal which can be up to 45% saving compared to the normal condition. As the result, this paper is able to achieve the purpose of efficient monitoring in the long run.

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