發(fā)布時間：2018-06-09 06:35

  本文選題：智能家居 + 無線傳感器網絡��； 參考：《重慶大學》2014年碩士論文

【摘要】：目前我國正處于老齡化社會階段，由于子女大多出外工作，老人家庭空巢率也在不斷增加，對智能化的看護系統的需求更加緊迫。而智能化看護系統的關鍵問題就在于對老人在日常生活中的活動進行識別和理解。目前，針對活動識別的研究主要可分為兩大類：基于視覺監(jiān)控設備的方式和基于傳感器設備的方式�；谝曈X監(jiān)控設備的方式雖然在實現技術上已經比較成熟，但由于其在采集數據的過程中侵犯了觀察對象的隱私，因此并不適合在家居環(huán)境中采用。而隨著無線傳感網絡的發(fā)展，，利用無線傳感設備收集活動數據，進行活動識別，已經吸引了越來越多的研究者的注意力。 本文將活動識別和無線傳感器網絡技術結合起來，針對家居環(huán)境中日常生活的智能監(jiān)護問題，提出了異常活動分布式檢測方法DetectingAct。由于正�；顒优c異�；顒拥膮^(qū)分是一個比較主觀的問題，本文中的正�；顒颖欢x為在活動數據中反復出現的活動，而異�；顒颖欢x為在時空數據上與正�；顒哟嬖谥^大偏差的活動。 本文的主要貢獻體現在以下幾個方面： ①針對日常生活智能監(jiān)護中對實時性的要求，本文設計了分布式異�；顒訖z測方法DetectingAct。該方法在檢測時充分利用了傳感器節(jié)點自身有限的計算資源和存儲資源，避免了集中式檢測方法帶寬需求大和反應時間長的缺點，同時保證了檢測精度，提高了檢測速度。 ②針對傳統活動識別中對活動定義的缺陷，本文在活動模型的原有軌跡信息的基礎上，引入了觸發(fā)數據中的持續(xù)時間信息。改進后的模型對活動的定義更準確，提高了檢測精度。 ③針對當前研究中缺乏面向運動傳感器的智能環(huán)境仿真系統的問題，開發(fā)設計了日常生活仿真及統計系統作為實驗基礎平臺。該系統可對真實環(huán)境下的智能環(huán)境進行仿真，所獲取到的仿真數據的可信度較高。 ④利用從日常生活仿真及統計系統中生成的仿真數據與真實環(huán)境中產生的觸發(fā)數據進行了實驗，從準確性、實時性、穩(wěn)定性三個方面驗證了分布式異�；顒訖z測方法DetectingAct相比于傳統的基于軌跡的檢測算法的優(yōu)勢。
[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.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP277;TP212.9;TN929.5

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本文編號：1999401