隨著老年人口的增加，無線身體感應器(wireless body sensor)被廣泛地應用在老人健康照護，也因為感應器體積小、重量輕，所以適合應用在監控人類行為的動作偵測，在身體感應網路(BSN)中，由於能量有限，使得關鍵的挑戰是如何去延長所有感應器的存活時間。在先前以叢集為基礎(cluster-based)的文獻中，叢集頭(cluster head)是由機率或剩餘能量來決定，但這些方法無法同時達到叢集內溝通能量的最小化和能量負載的分配，因此這篇論文的目的是同時滿足這二項備受關注的議題，以求最大化整個網路的存活時間。我們利用馬可夫鏈(Markov chain)作為我們的預測器來估計下一個姿勢，並且觀察每個姿勢的特性，找出一個能最小化叢集內溝通能量消耗的叢集頭，我們同時平衡叢集頭跟叢集節點的執行次數來考慮能量負載分配，再納入叢集頭選擇和能量負載分配方法，提出一個基於高準確率動作偵測加上預測的有效節能傳輸方式，最後藉由包含二種分類以上的k個鄰近節點演算法(k-nearest neighbors algorithm)，我們在身體感應網路中使用加速度感測器結合有效節能的傳輸，實作一個偵測動作的想法，此外，同時也考慮完整資料傳輸和部份資料傳輸。實驗結果顯示這二種情況在動作偵測中，皆能獲得高的準確率(超過百分之九十)。

With the increase in the population of elderly people, wireless body sensors are broadly applied in healthcare applications for elders while they are also suitable for motion detection to monitor human behaviors because of its small size and light weight. A critical challenge for BSNs is how to prolong all sensor nodes’ lifetime due to the energy restriction. In previous cluster-based papers, the cluster heads (CHs) are selected by probability or residual energy. However, these approaches can’t achieve both the minimization of intra-cluster communication energy and energy load distribution. Thus, the aim of our paper is to meet both of two concerned issues to maximize the lifetime of the entire network. In our work, we exploit Markov chain as our predictor for estimating the next posture as well as observe the character of each posture to find a CH minimizing the intra-cluster communication energy. Simultaneously we also take energy load distribution into account balancing the numbers of times of being CHs and cluster nodes. We incorporate the methods of CH selection and energy load distribution to present the process of the energy-efficient transmission with prediction based on the high-accuracy motion detection. Finally, we combine the energy-efficient transmission to implement an idea of detecting motions using accelerometer sensors in body sensor networks by k-nearest neighbors algorithm (KNN) including more than two categories. Moreover, full-transmission (FT) and partial-transmission (PT) are considered and the experiment result shows both two cases have high accuracy (above 90%) for motion detection.

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