本文選題：無線體域網(WBAN)　切入點：點云數(shù)據(jù)　出處：《西安電子科技大學》2014年碩士論文

【摘要】：近年來,隨著傳感器技術和無線通信技術的發(fā)展,以人體為中心,由各種用于監(jiān)測人體生理指標信號的傳感器節(jié)點組成的無線體域網(Wireless Body Area Network,WBAN)成為人們關注的焦點。無線體域網作為一種新的無線通信網絡,具有與其它無線通信網絡不同的特點和要求。其微型性和實用性使得它在醫(yī)療診斷、健康監(jiān)護和消費電子等領域具有廣闊的應用前景。目前對無線體域網的研究還處于起步階段,離技術成熟和廣泛應用還有很大一段距離,面臨的挑戰(zhàn)還很多,還有很多關鍵性的技術問題有待進一步的研究和解決。無線體域網是架設在人體身上的無線通信網絡,人體作為組建網絡的傳感器節(jié)點的主要承載體,是無線體域網的研究工作中必不可少的一部分。由于利用真實的物理人體體模進行相應的研究實驗缺乏一定的安全性,而且成本高、效率低,這就需要一種能夠取代真實的物理人體體模在無線體域網中的角色的東西——數(shù)字化3D人體模型。數(shù)字化3D人體模型也稱為計算機人,它是通過計算機技術和圖像處理技術虛擬出來的能夠反應出人體三維形貌特征的3D模型。這種3D人體模型能夠在計算機中存儲和可視化,并且能夠任意調節(jié)和控制,相比于真實的物理人體體模,它更加安全、方便和靈活。因此它可以用來進行無線體域網的各種實驗仿真,極大地方便了無線體域網的研究,在某種程度上促進了無線體域網的發(fā)展。本文首先通過3D激光掃描儀對人體進行掃描測量,獲取人體的三維點云數(shù)據(jù),接著對其做一些簡單的預處理,包括踢除孤立點、濾波去噪等操作。然后利用這些經過處理后的人體三維點云數(shù)據(jù)用Delaunay三角剖分的方法重構出三角網格化的數(shù)字化3D人體模型。然而,由于3D激光掃描儀的精度很高,使得獲取的人體三維點云數(shù)據(jù)非常大,不易直接對其進行處理和重構,因此就需要對這些人體三維點云數(shù)據(jù)進行簡化處理�？紤]到在利用3D激光掃描儀對人體進行掃描測量的時候,采集人體表面的特征點非常密集,就會導致得到的這些人體三維點云數(shù)據(jù)之間或多或少的包含一些重復的特征信息,即這些點云數(shù)據(jù)之間會有一定的相關性。而主元分析(Principal Component Analysis,PCA)正好是一種處理這類問題的統(tǒng)計分析方法。它能夠從多個特征指標變量的變化中找出其主要的變化方向,用較少的互不相關的綜合特征指標變量來代替原始相關的特征指標變量進行分析和理解,從而使問題變得更加簡單和容易處理。因此,本文隨后又研究了主元分析算法,并利用該方法對獲取的3D人體模型數(shù)據(jù)進行了簡化,為未來無線體域網的研究做出了一點貢獻。
[Abstract]:In recent years, with the development of sensor technology and wireless communication technology, taking the human body as the center, Wireless Body Area Network (WBAN), which is composed of sensor nodes used to monitor human physiological index signals, has become the focus of attention. As a new wireless communication network, Wireless body area Network (WLAN) is a new wireless communication network. It has different characteristics and requirements from other wireless communication networks. Its miniaturization and practicability make it useful in medical diagnostics. The research on wireless body area network is still in its infancy, and there is still a long way to go before the technology is mature and widely used, and the challenges are still many. There are still many key technical problems to be further studied and solved. Wireless body area network is a wireless communication network built on the human body, and the human body is the main carrier of sensor nodes to build the network. It is an indispensable part of the research work of wireless body-area network. Due to the lack of safety, high cost and low efficiency in the corresponding research experiment using real physical human body model, This requires something that can take the place of the real physical human body model in the wireless body-area network-digital 3D human body model, also known as a computer human. It is a 3D model, which is virtual by computer technology and image processing technology, which can reflect the 3D features of human body. The 3D human body model can be stored and visualized in the computer, and can be adjusted and controlled arbitrarily. Compared with the real physical body phantom, it is more secure, convenient and flexible. Therefore, it can be used to carry out all kinds of experimental simulation of wireless body area network, which greatly facilitates the research of wireless body area network. To some extent, it promotes the development of wireless body-area network. Firstly, the 3D laser scanner is used to scan and measure the human body to obtain the 3D point cloud data of the human body, and then to do some simple preprocessing, including kicking out the isolated point. Filtering and denoising are used to reconstruct the triangulated digital 3D human body model by using the Delaunay triangulation method. However, because of the high precision of 3D laser scanner, the 3D laser scanner can be used to reconstruct the 3D human body model by using the Delaunay triangulation method. Make the human body 3D point cloud data very large, it is not easy to directly process and reconstruct it, So we need to simplify these 3D point cloud data. Considering that when we use 3D laser scanner to scan and measure the human body, the feature points on the human surface are very dense. The resulting 3D point cloud data of the human body contains more or less duplicated feature information. The principal component analysis (PCA) is a kind of statistical analysis method to deal with this kind of problem. It can find out the main direction of change from the change of multiple characteristic index variables. In order to make the problem easier and easier to deal with, the problem is analyzed and understood by using fewer independent synthetic feature index variables instead of the original related feature index variables. Therefore, this paper then studies the principal component analysis algorithm. The method is used to simplify the 3D human body model data and make a contribution to the future research of wireless body area network.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN92

【共引文獻】

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