本文選題：智能家居 + 無(wú)線傳感器網(wǎng)絡(luò)　； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：目前我國(guó)正處于老齡化社會(huì)階段，由于子女大多出外工作，老人家庭空巢率也在不斷增加，對(duì)智能化的看護(hù)系統(tǒng)的需求更加緊迫。而智能化看護(hù)系統(tǒng)的關(guān)鍵問(wèn)題就在于對(duì)老人在日常生活中的活動(dòng)進(jìn)行識(shí)別和理解。目前，針對(duì)活動(dòng)識(shí)別的研究主要可分為兩大類：基于視覺(jué)監(jiān)控設(shè)備的方式和基于傳感器設(shè)備的方式�；谝曈X(jué)監(jiān)控設(shè)備的方式雖然在實(shí)現(xiàn)技術(shù)上已經(jīng)比較成熟，但由于其在采集數(shù)據(jù)的過(guò)程中侵犯了觀察對(duì)象的隱私，因此并不適合在家居環(huán)境中采用。而隨著無(wú)線傳感網(wǎng)絡(luò)的發(fā)展，，利用無(wú)線傳感設(shè)備收集活動(dòng)數(shù)據(jù)，進(jìn)行活動(dòng)識(shí)別，已經(jīng)吸引了越來(lái)越多的研究者的注意力。 本文將活動(dòng)識(shí)別和無(wú)線傳感器網(wǎng)絡(luò)技術(shù)結(jié)合起來(lái)，針對(duì)家居環(huán)境中日常生活的智能監(jiān)護(hù)問(wèn)題，提出了異常活動(dòng)分布式檢測(cè)方法DetectingAct。由于正常活動(dòng)與異�；顒�(dòng)的區(qū)分是一個(gè)比較主觀的問(wèn)題，本文中的正�；顒�(dòng)被定義為在活動(dòng)數(shù)據(jù)中反復(fù)出現(xiàn)的活動(dòng)，而異�；顒�(dòng)被定義為在時(shí)空數(shù)據(jù)上與正常活動(dòng)存在著較大偏差的活動(dòng)。 本文的主要貢獻(xiàn)體現(xiàn)在以下幾個(gè)方面： ①針對(duì)日常生活智能監(jiān)護(hù)中對(duì)實(shí)時(shí)性的要求，本文設(shè)計(jì)了分布式異�；顒�(dòng)檢測(cè)方法DetectingAct。該方法在檢測(cè)時(shí)充分利用了傳感器節(jié)點(diǎn)自身有限的計(jì)算資源和存儲(chǔ)資源，避免了集中式檢測(cè)方法帶寬需求大和反應(yīng)時(shí)間長(zhǎng)的缺點(diǎn)，同時(shí)保證了檢測(cè)精度，提高了檢測(cè)速度。 ②針對(duì)傳統(tǒng)活動(dòng)識(shí)別中對(duì)活動(dòng)定義的缺陷，本文在活動(dòng)模型的原有軌跡信息的基礎(chǔ)上，引入了觸發(fā)數(shù)據(jù)中的持續(xù)時(shí)間信息。改進(jìn)后的模型對(duì)活動(dòng)的定義更準(zhǔn)確，提高了檢測(cè)精度。 ③針對(duì)當(dāng)前研究中缺乏面向運(yùn)動(dòng)傳感器的智能環(huán)境仿真系統(tǒng)的問(wèn)題，開(kāi)發(fā)設(shè)計(jì)了日常生活仿真及統(tǒng)計(jì)系統(tǒng)作為實(shí)驗(yàn)基礎(chǔ)平臺(tái)。該系統(tǒng)可對(duì)真實(shí)環(huán)境下的智能環(huán)境進(jìn)行仿真，所獲取到的仿真數(shù)據(jù)的可信度較高。 ④利用從日常生活仿真及統(tǒng)計(jì)系統(tǒng)中生成的仿真數(shù)據(jù)與真實(shí)環(huán)境中產(chǎn)生的觸發(fā)數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)，從準(zhǔn)確性、實(shí)時(shí)性、穩(wěn)定性三個(gè)方面驗(yàn)證了分布式異�；顒�(dòng)檢測(cè)方法DetectingAct相比于傳統(tǒng)的基于軌跡的檢測(cè)算法的優(yōu)勢(shì)。
[Abstract]:At present, our country is in the stage of aging society, because most of the children go out to work, the empty nest rate of the elderly family is also increasing, so the need for the intelligent nursing system is more urgent. The key problem of intelligent nursing system is to identify and understand the activities of the elderly in their daily life. At present, the research on activity recognition can be divided into two main categories: visual monitoring devices and sensor devices. Although the method based on visual monitoring device is mature in technology, it is not suitable for home environment because it infringes the privacy of observation object in the process of collecting data. With the development of wireless sensor network (WSN), it has attracted more and more researchers' attention to collect activity data and identify activities by wireless sensor devices. In order to solve the problem of intelligent monitoring of daily life in home environment, a distributed detection method for abnormal activities is proposed. Since the distinction between normal and abnormal activities is a more subjective problem, the normal activities in this paper are defined as recurring activities in the activity data. The abnormal activity is defined as the activity which deviates greatly from the normal activity in time and space data. The main contributions of this paper are as follows: 1 according to the requirement of real time in intelligence monitoring of daily life, In this paper, a distributed anomaly detection method, detect activity, is designed. This method makes full use of the limited computing and storage resources of sensor nodes in detection, avoids the shortcomings of large bandwidth and long reaction time of centralized detection methods, and ensures the accuracy of detection. The detection speed is improved. 2 aiming at the defect of the definition of activity in traditional activity recognition, this paper introduces the duration information of trigger data on the basis of the original trajectory information of the activity model. The improved model is more accurate in the definition of activity and improves the accuracy of detection. 3 aiming at the lack of intelligent environment simulation system for motion sensor in current research, The daily life simulation and statistics system is developed as the experimental platform. The system can simulate the intelligent environment in real environment. The credibility of the obtained simulation data is high. 4 the simulation data generated from the daily life simulation and statistical system and the trigger data generated in the real environment are used for experiments. Three aspects of stability verify the advantages of the distributed anomaly detection method (detection Act) over the traditional locus based detection algorithm.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP277;TP212.9;TN929.5

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