【摘要】：隨著科學(xué)技術(shù)與計(jì)算機(jī)技術(shù)的發(fā)展，無(wú)線網(wǎng)絡(luò)的普及面越來(lái)越廣，并且新興科技下的產(chǎn)物可穿戴式傳感器正在得到科研人員的認(rèn)可與普及，故通過無(wú)線傳感器發(fā)出信號(hào)，進(jìn)行人體行為識(shí)別系統(tǒng)的開發(fā)已經(jīng)成為具有重要研究意義與價(jià)值的工作，越來(lái)越多的科研機(jī)構(gòu)開始利用行為識(shí)別系統(tǒng)進(jìn)行廣泛的科學(xué)研究。功能日益完善的智能手機(jī)也給人們的日常生活帶來(lái)了極大的便利。隨著在健康保健領(lǐng)域?qū)π袨樽R(shí)別系統(tǒng)的需求的增加，尤其是在老年護(hù)理，長(zhǎng)期健康監(jiān)控，以及協(xié)助有認(rèn)知障礙患者，越來(lái)越多的注意集中在識(shí)別攜帶有傳感器的人的行為上。智能手機(jī)上有很多軟件能記錄智能手機(jī)用戶的日常行為。于是本研究在獲取智能手機(jī)傳感器信號(hào)的基礎(chǔ)上，提出一種基于譜聚類和隱馬爾可夫模型（Spectral clustering and Hidden Markov Models，SC-HMM）的日常行為識(shí)別算法。SC-HMM方法利用智能手機(jī)獲取GPS地理位置、加速度、接收信號(hào)強(qiáng)度等傳感器數(shù)據(jù)，結(jié)合譜聚類技術(shù)和隱馬爾可夫模型學(xué)習(xí)，能有效地對(duì)用戶日常活動(dòng)行為進(jìn)行自動(dòng)識(shí)別。本研究通過在真實(shí)數(shù)據(jù)上進(jìn)行實(shí)驗(yàn)驗(yàn)證本研究提出的SC-HMM識(shí)別方法。實(shí)驗(yàn)結(jié)果表明，在真實(shí)的智能手機(jī)數(shù)據(jù)集中，該方法具有較高的識(shí)別準(zhǔn)確度，，并且優(yōu)于以前傳統(tǒng)的識(shí)別方法。本研究提出的識(shí)別方法在用戶行為學(xué)習(xí)、情景感知等領(lǐng)域具有良好的實(shí)用性。 本文的主要內(nèi)容如下： （1）本論文研究無(wú)線傳感器網(wǎng)絡(luò)的架構(gòu)，介紹了無(wú)線傳感器的網(wǎng)絡(luò)特征等方面的內(nèi)容。 （2）研究智能手機(jī)傳感器技術(shù)、加速度傳感器、RSSI技術(shù)，深入介紹了RSSI的原理， RSSI異常判斷以及產(chǎn)生的原因等。研究如何收集智能手機(jī)傳感器的傳感器數(shù)據(jù)并分析智能手機(jī)用戶的行為。 （3）研究基于傳感器的行為識(shí)別的分類，分為基于傳感器的單人行為識(shí)別、基于傳感器的多人行為識(shí)別；基于傳感器的行為識(shí)別的識(shí)別階段分為：在最底層，收集傳感器數(shù)據(jù)，在中間階段，采用統(tǒng)計(jì)推論，在最高層，識(shí)別出行為的目標(biāo)或者子目標(biāo)；基于傳感器的行為識(shí)別的識(shí)別方法，主要有四種方法：概率推理方法、邏輯推理方法、基于WiFi的行為識(shí)別方法、基于數(shù)據(jù)挖掘的方法。本研究主要研究基于智能手機(jī)傳感器的行為識(shí)別方法。 （4）研究機(jī)器學(xué)習(xí)的聚類方法，譜聚類，并將譜聚類方法用于將從智能手機(jī)傳感器中收集的傳感器數(shù)據(jù)聚成K個(gè)相似的類。 （5）研究無(wú)監(jiān)督學(xué)習(xí)方法，隱馬爾科夫模型的特性以及要解決的三個(gè)問題：估計(jì)問題、解碼問題以及學(xué)習(xí)問題。通過隱馬爾科夫模型訓(xùn)練活動(dòng)時(shí)間序列得到智能手機(jī)用戶的行為，并識(shí)別未訓(xùn)練的活動(dòng)時(shí)間序列的行為。 （6）總結(jié)本研究的工作，并展望未來(lái)的研究工作�？梢詫⒈狙芯刻岢龅幕谥悄苁謾C(jī)傳感器的SC-HMM行為識(shí)別方法擴(kuò)展到識(shí)別兩個(gè)或者多個(gè)智能手機(jī)用戶的交互行為識(shí)別。
[Abstract]:With the development of science and technology and computer technology, wireless network is becoming more and more popular, and wearable sensors, which are the product of new technology, are being recognized and popularized by researchers, so they send out signals through wireless sensors. The development of human behavior recognition system has become a work of great significance and value. More and more scientific research institutions begin to use behavior recognition system to carry out extensive scientific research. The increasingly sophisticated smartphone also brings great convenience to people's daily life. With the increasing demand for behavioral recognition systems in the field of health care, especially in geriatric care, long-term health monitoring, and assistance to patients with cognitive impairment, more and more attention has been focused on identifying the behaviour of people with sensors. There is a lot of software on smartphones that records the daily behavior of smartphone users. In this paper, based on the acquisition of smart phone sensor signals, a spectral clustering and hidden Markov model (Spectral clustering and Hidden Markov Models,) is proposed. SC-HMM). The SC-HMM method uses smart phone to acquire GPS data, such as location, acceleration and signal intensity, and combines spectral clustering technology with hidden Markov model to learn. Can effectively identify the daily activities of users automatically. The SC-HMM recognition method proposed in this study is verified by experiments on real data. The experimental results show that this method has high recognition accuracy in real smart phone data sets and is superior to the traditional recognition methods. The method proposed in this study has good practicability in user behavior learning and situational perception. The main contents of this paper are as follows: (1) this paper studies the architecture of wireless sensor networks and introduces the characteristics of wireless sensor networks. (2) the technology of smart phone sensor, acceleration sensor and RSSI technology are studied. The principle of RSSI, the abnormal judgment of RSSI and the cause of its occurrence are introduced in detail. This paper studies how to collect sensor data of smart phone sensor and analyze the behavior of smart phone user. (3) the classification of behavior recognition based on sensor is studied, which is divided into single person behavior recognition based on sensor and multi-person behavior recognition based on sensor. The recognition stage of behavior recognition based on sensor is divided into: at the lowest level, collecting sensor data, in the middle stage, using statistical inference, at the highest level, recognizing the target or sub-target of travel behavior; There are four main methods for behavior recognition based on sensor: probabilistic reasoning, logical reasoning, behavior recognition based on WiFi and data mining. This research mainly studies the behavior recognition method based on smart phone sensor. (4) the clustering method of machine learning, spectral clustering is studied, and the spectral clustering method is used to cluster the sensor data collected from smart phone sensors into K similar clusters. (5) study the unsupervised learning method, the characteristics of hidden Markov model and three problems to be solved: estimation problem, decoding problem and learning problem. The hidden Markov model is used to train the activity time series to obtain the behavior of the smartphone user and to recognize the behavior of the untrained activity time series. (6) summing up the work of this study and looking forward to the future research work. The proposed SC-HMM behavior recognition method based on smart phone sensor can be extended to identify the interaction behavior of two or more smartphone users.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP212.9;TN929.5

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本文編號(hào)：2390881