隨著感測器與無線網路技術的進步，相關應用服務也相繼地衍生，例如資訊娛樂、運動健身、醫療照護及工業/軍事等應用領域，感測器被內置於身體內或或者在身體周圍收集資訊形成無線人體區域網路，感測器可透過人體區域網路將收集到的生理資料傳輸至雲端伺服器，等待雲端伺服器分析運算完後再反饋至使用者，讓使用者可以適時地獲得所需要的資訊。然而因為人體感測器必須盡可能微小化、輕量化，而使得電池的尺寸不可能太大，因此能源效率就變成無線人體區域網路中最重要的問題了，許多研究開始將雲端網路跟人體區域網路進行連結，透過雲端網路的運算能力來降低人體區域網路的能源消耗，然而如何透過人體區域網路來搭配雲端網路進行協同運算將會是本研究的重點。本研究特別針對人體區域網路進行系統的設計與實現，並針對人體區域網路所收集到的生理資訊進行分析與探討，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，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，且可有效達到相當程度的省電。

With the advancement of sensor and wireless network technologies, related application services have sprung up nearly everywhere in people’s daily lives. For example, in the information entertainment, fitness, medical care, and industrial/military application domains, sensors are embedded in the body or located around the body to collect information, forming a wireless body area network. The sensor transfers the collected physiological data via Wireless Body Area Network (WBAN) to a cloud server, the data are analyzed by the cloud server and fed back to the user, so that the user can obtain the required information in a timely fashion. However, the human body sensor must be as miniature and light as possible, and the cell size shall not be too large. Therefore, energy efficiency becomes the most important point of wireless WBAN.

Many studies have connected the cloud network with WBAN, using the arithmetic capability of the former to reduce the energy consumption of the latter. Thus, the key point of this study is how to combine WBAN with a cloud network for collaborative computing. This study designs and implements a system for WBAN, as well as analyzes and discusses the physiological information collected by WBAN. The concept and mechanism of learning are imported into the sensor with the information received by the sensor indirectly, so as to implement subsequent processing and application. This is more efficient than the learning method when other sensors detect actual emergencies actively, the level of ex ante forecast is effectively reached, and energy is saved to a greater extent.

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