本文選題：無線傳感器網(wǎng)絡(luò)　切入點(diǎn)：行為識別　出處：《南京郵電大學(xué)》2017年碩士論文

【摘要】：隨著無線通信技術(shù)以及傳感器制造技術(shù)的快速發(fā)展,無線傳感器網(wǎng)絡(luò)應(yīng)用領(lǐng)域涉及生活休閑、醫(yī)療保健、軍事通信、應(yīng)急救災(zāi)及航空航天等方面。目前,無線傳感器網(wǎng)絡(luò)中的人體行為識別具有便攜性好、成本低、抗干擾等優(yōu)點(diǎn),受到了人們的廣泛關(guān)注。人體行為的識別方法種類很多,從數(shù)據(jù)采集方式到識別分類算法,行為識別系統(tǒng)中的各個(gè)環(huán)節(jié)都具有不同的特點(diǎn)。本文主要針對特征提取、識別分類算法進(jìn)行了研究,并在公開的人體動(dòng)作識別數(shù)據(jù)庫上進(jìn)行了實(shí)驗(yàn),對實(shí)驗(yàn)結(jié)果進(jìn)行了評估與分析。本文主要工作如下:(1)分析了基于無線傳感器網(wǎng)絡(luò)的人體行為識別課題的研究背景和意義,對基于三軸加速度傳感器的人體行為識別的信號采集方式、數(shù)據(jù)預(yù)處理方法、特征提取等幾個(gè)模塊進(jìn)行了概述和分析。(2)分析比較了SVM、KNN、NB等算法用于行為識別時(shí)的優(yōu)缺點(diǎn)。考慮到KNN算法模型的優(yōu)勢,本文通過Relief F算法計(jì)算特征的權(quán)重,改進(jìn)了KNN算法,通過實(shí)驗(yàn)分析了其改進(jìn)后的識別準(zhǔn)確率。(3)將鄰近類作為局部基,在稀疏表示的人體行為識別算法中引入KNN算法的思想,給出了一種新的稀疏近鄰表示分類方法。實(shí)驗(yàn)結(jié)果表明了這種方法可以有效彌補(bǔ)原算法存在的缺點(diǎn),降低復(fù)雜度且得到了很好的識別準(zhǔn)確率。(4)采用一種基于塊稀疏模型的人體行為識別方法,充分利用了人體行為模型內(nèi)在塊稀疏結(jié)構(gòu),將人體行為識別問題轉(zhuǎn)化為稀疏表示問題。通過塊稀疏貝葉斯學(xué)習(xí)算法,求解待測樣本的稀疏系數(shù)并進(jìn)行分類,實(shí)驗(yàn)結(jié)果說明了該種方法能有效提高人體行為識別率。
[Abstract]:With the rapid development of wireless communication technology and sensor manufacturing technology, wireless sensor network applications are related to life and leisure, medical care, military communications, emergency relief and aerospace. Human behavior recognition in wireless sensor networks has many advantages, such as good portability, low cost, anti-jamming and so on. There are many kinds of human behavior recognition methods, from data acquisition to recognition and classification algorithm. Each link in the behavior recognition system has different characteristics. This paper mainly focuses on the feature extraction, recognition and classification algorithm, and carries on the experiment on the open human body motion recognition database. The main work of this paper is as follows: 1) the background and significance of the research on human behavior recognition based on wireless sensor networks are analyzed. The signal acquisition method and data preprocessing method of human body behavior recognition based on triaxial acceleration sensor are discussed. Several modules such as feature extraction are summarized and analyzed. (2) the advantages and disadvantages of SVMKNNNNB and other algorithms in behavior recognition are compared. Considering the advantages of KNN algorithm model, this paper improves the KNN algorithm by calculating the weight of features by Relief F algorithm. The improved recognition accuracy is analyzed experimentally. (3) the neighborhood class is used as the local basis, and the idea of KNN algorithm is introduced into the sparse representation of human behavior recognition algorithm. A new sparse nearest neighbor representation classification method is presented. The experimental results show that this method can effectively compensate for the shortcomings of the original algorithm. A method of human behavior recognition based on block sparse model is proposed, which makes full use of the block sparse structure of human behavior model. The problem of human behavior recognition is transformed into a sparse representation problem. The sparse coefficients of samples under test are solved and classified by block sparse Bayesian learning algorithm. The experimental results show that this method can effectively improve the recognition rate of human behavior.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP212.9;TN929.5

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本文編號：1677813