無線體域網(wǎng)中人體動(dòng)作監(jiān)測與識(shí)別若干方法研究
發(fā)布時(shí)間：2018-12-13 03:09
【摘要】：無線體域網(wǎng)是由可感知人體多種生理參數(shù)的輕便、可穿戴或可植入的傳感器節(jié)點(diǎn)構(gòu)建的無線網(wǎng)絡(luò)。無線體域網(wǎng)為人體健康監(jiān)測提供了新的手段,在疾病監(jiān)控、健康恢復(fù)、特殊人群監(jiān)護(hù)等領(lǐng)域有著巨大的應(yīng)用意義和需求。通過佩戴在身體上的微慣性傳感器,體域網(wǎng)可以采集人體的運(yùn)動(dòng)信號(hào),在人體動(dòng)作監(jiān)測方面得到廣泛應(yīng)用,可實(shí)現(xiàn)人體動(dòng)作識(shí)別、異常動(dòng)作檢測、步態(tài)識(shí)別與分析、運(yùn)動(dòng)能耗分析等目的。 在利用無線體域網(wǎng)進(jìn)行人體運(yùn)動(dòng)監(jiān)測過程中,如何在滿足身體活動(dòng)監(jiān)測指標(biāo)要求的同時(shí)提高傳感器節(jié)點(diǎn)的能量有效性,以便能在實(shí)際應(yīng)用中長時(shí)間不間斷地進(jìn)行人體動(dòng)作監(jiān)測,是一個(gè)具有挑戰(zhàn)性的問題。本文以由多個(gè)可穿戴的微慣性傳感器構(gòu)成的無線體感網(wǎng)為研究對(duì)象,圍繞能量有效性,以稀疏表示和壓縮感知理論為主線,從信號(hào)識(shí)別、信號(hào)壓縮、數(shù)據(jù)融合、功率控制這四個(gè)方面展開研究。主要工作和創(chuàng)新點(diǎn)如下： (1)提出了一種基于自學(xué)習(xí)稀疏表示的動(dòng)態(tài)手勢(shì)識(shí)別方法L-SRC.針對(duì)手勢(shì)識(shí)別中手勢(shì)長短不一的問題,將手勢(shì)樣本向量進(jìn)行歸一化線性插值,從而將手勢(shì)識(shí)別問題轉(zhuǎn)化為求解待識(shí)別樣本在訓(xùn)練樣本中的稀疏表示問題；針對(duì)如何提高手勢(shì)識(shí)別精度和速度的問題,采用基于類別的字典學(xué)習(xí)方法尋求一個(gè)較小的并經(jīng)過優(yōu)化的超完備字典來計(jì)算待識(shí)別樣本的稀疏表示,從而在手勢(shì)識(shí)別階段大幅度縮減識(shí)別算法的計(jì)算復(fù)雜度,滿足快速識(shí)別要求。在包含18種手勢(shì)的數(shù)據(jù)集上驗(yàn)證了提出的L-SRC手勢(shì)識(shí)別方法在保證識(shí)別精度的同時(shí)提升了識(shí)別速度。 (2)提出了兩種壓縮分類的動(dòng)作識(shí)別方法RP-CCall和RP-CCeach.針對(duì)運(yùn)動(dòng)信號(hào)的時(shí)間冗余性和稀疏性,結(jié)合壓縮感知和稀疏表示理論,將傳感信號(hào)壓縮與動(dòng)作識(shí)別相結(jié)合,以滿足一定動(dòng)作識(shí)別率的同時(shí)降低傳感器節(jié)點(diǎn)的能耗。兩種RP-CC方法是在傳感器節(jié)點(diǎn)上利用隨機(jī)投影對(duì)運(yùn)動(dòng)信號(hào)進(jìn)行數(shù)字化的壓縮采樣,通過減少無線體域網(wǎng)的數(shù)據(jù)傳輸量來節(jié)省能耗；在基站上直接對(duì)壓縮的數(shù)據(jù)建立稀疏表示的人體運(yùn)動(dòng)模式識(shí)別模型,利用稀疏系數(shù)的分布來實(shí)現(xiàn)動(dòng)作識(shí)別。理論分析了壓縮分類動(dòng)作識(shí)別方法能正確識(shí)別的基本條件。找到了能在存儲(chǔ)和計(jì)算資源有限的傳感器節(jié)點(diǎn)上實(shí)現(xiàn)的隨機(jī)投影矩陣。在包含13種動(dòng)作的數(shù)據(jù)集上進(jìn)行了驗(yàn)證,結(jié)果顯示RP-CCall方法和RP-CCeach方法在對(duì)壓縮的數(shù)據(jù)識(shí)別時(shí)也能達(dá)到無壓縮時(shí)相近似的識(shí)別準(zhǔn)確率,并高于最近鄰、支持向量機(jī)等分類方法。 (3)提出了基于分布式壓縮感知和聯(lián)合稀疏表示的動(dòng)作識(shí)別方法DCS-JSRC.針對(duì)無線體域網(wǎng)中多傳感器采集的運(yùn)動(dòng)數(shù)據(jù)之間的時(shí)空相關(guān)性,采用分布式壓縮感知在傳感器節(jié)點(diǎn)進(jìn)行分布式壓縮,充分利用這種相關(guān)性來進(jìn)一步壓縮數(shù)據(jù)以降低傳輸能耗。在基站通過探索多傳感器節(jié)點(diǎn)感知運(yùn)動(dòng)信號(hào)的時(shí)空相關(guān)性,構(gòu)建適用于動(dòng)作識(shí)別的聯(lián)合稀疏表示模型,將多傳感器的動(dòng)作識(shí)別問題轉(zhuǎn)化為多變量稀疏線性回歸問題來求解。采用層次貝葉斯模型來求解稀疏表示系數(shù),利用不同傳感器節(jié)點(diǎn)的相互關(guān)聯(lián)來進(jìn)一步提高動(dòng)作識(shí)別的準(zhǔn)確率。在動(dòng)作數(shù)據(jù)集上進(jìn)行驗(yàn)證,實(shí)驗(yàn)結(jié)果顯示DCS-JSRC方法在相同壓縮比的情況下獲得了比RP-CCall方法和RP-CCeach方法更高的識(shí)別準(zhǔn)確率。 (4)設(shè)計(jì)了輕量級(jí)的基于動(dòng)作行為的自適應(yīng)功率反饋控制機(jī)制PID-A。針對(duì)無線體域網(wǎng)中鏈路通信質(zhì)量受人的運(yùn)動(dòng)、姿態(tài)變化影響具有動(dòng)態(tài)時(shí)變特性,通過實(shí)測人體不同動(dòng)作以及發(fā)射功率變化對(duì)無線鏈路的影響,分析和總結(jié)了在人體不同運(yùn)動(dòng)狀態(tài)下節(jié)點(diǎn)的發(fā)射功率與鏈路通信質(zhì)量的變化特性和規(guī)律,在此基礎(chǔ)上建立基于反饋的功率控制系統(tǒng)模型,結(jié)合人體動(dòng)作識(shí)別的結(jié)果,來動(dòng)態(tài)調(diào)整無線體域網(wǎng)中節(jié)點(diǎn)的發(fā)射功率。實(shí)驗(yàn)結(jié)果顯示PID-A功率控制機(jī)制可保證在數(shù)據(jù)包成功接收的條件下降低了傳感器節(jié)點(diǎn)發(fā)送數(shù)據(jù)包的平均能耗。 (5)為了驗(yàn)證算法在實(shí)際系統(tǒng)中的性能,設(shè)計(jì)并實(shí)現(xiàn)了用于人體運(yùn)動(dòng)監(jiān)測的無線體域網(wǎng)原型系統(tǒng)。利用所構(gòu)建的基于微慣性傳感器的無線體域網(wǎng),采集人體在日�；顒�(dòng)中的動(dòng)作信號(hào),實(shí)際驗(yàn)證了所提出的動(dòng)作識(shí)別算法的識(shí)別準(zhǔn)確率,并對(duì)傳感器節(jié)點(diǎn)的能耗進(jìn)行了分析,驗(yàn)證了算法的能量有效性。
[Abstract]:the wireless body domain network is a wireless network constructed from a light, wearable or implantable sensor node that can sense a variety of physiological parameters of the human body. The wireless body area network provides new means for human health monitoring, and has great application meaning and requirement in the fields of disease monitoring, health recovery, special crowd monitoring and the like. By wearing the micro-inertial sensor on the body, the body-domain network can collect the motion signal of the human body, and has wide application in human motion monitoring, and can realize the purposes of human body motion identification, abnormal motion detection, gait recognition and analysis, motion energy consumption analysis, and the like. In the process of human motion monitoring by using the wireless body domain network, how to improve the energy efficiency of the sensor nodes while meeting the requirements of the physical activity monitoring indexes, so as to be able to carry out human motion monitoring for a long time in the practical application, is a challenging question, In this paper, a wireless body-sensing network composed of a plurality of wearable micro-inertial sensors is used as the research object, and the energy efficiency is focused on the basis of the sparse representation and the compression-sensing theory, and the four aspects of signal identification, signal compression, data fusion and power control are developed. Research. Key work and innovative points such as (1) A dynamic gesture recognition method based on self-learning sparse representation is proposed. SRC. The gesture recognition problem is transformed into a sparse representation problem for solving the sample to be identified in the training sample, and the gesture recognition problem is converted into a sparse representation problem in the training sample for the sample to be identified; and the gesture recognition precision and the speed are improved. According to the problem, the sparse representation of the sample to be identified is calculated by adopting a category-based dictionary learning method, so that the calculation complexity of the identification algorithm is greatly reduced in the gesture recognition stage, and the rapid recognition is met. The proposed L-SRC gesture recognition method is verified to improve the recognition precision while the recognition accuracy is guaranteed. (2) Two types of motion recognition methods, RP-CCall, and RP-C, are proposed. Cach. Combining the time redundancy and sparsity of the motion signal, combining the compression-aware and sparse representation theory, the sensing signal compression is combined with the action recognition to meet the recognition rate of a certain action while reducing the sensor. The method comprises the following steps of: carrying out digital compression sampling on a motion signal on a sensor node by using a random projection on a sensor node, saving energy consumption by reducing the data transmission amount of the wireless body domain network, a pattern recognition model that uses the distribution of the sparse coefficients to The recognition of the present action is carried out. The theoretical analysis of the identification of the motion recognition method of the compression classification can be correctly identified. The basic condition of a sensor node that can be realized on a sensor node with limited storage and computational resources The results show that the RP-CCall method and the RP-CCeach method can achieve similar recognition accuracy when the compressed data is not compressed, and it is higher than the nearest neighbor and support vector machine. (3) An action identification method based on distributed compression-aware and combined sparse representation is presented. CS-JSRC. The spatial and temporal correlation between the motion data collected by multiple sensors in the wireless body domain network is distributed in the sensor node by the distributed compression sensing, and the correlation is fully utilized to further compress the data. in that base station, the time-space correlation of the motion signal is sensed by the base station, a joint sparse representation model suitable for action identification is constructed, and the action identification problem of the multi-sensor is converted into a multi-variable sparse linear model, The problem of regression is solved. A hierarchical Bayesian model is used to solve the sparse representation coefficient, and the correlation of different sensor nodes is used to further improve the motion. The results show that the method of the DCS-JSRC is more effective than the RP-CCall method and the RP-CCeach method in the case of the same compression ratio. High recognition accuracy. (4) A lightweight self-adaptive power feedback based on action behavior is designed The control mechanism PID-A. Aiming at the movement of the link communication quality in the wireless body domain network, the influence of the attitude change has the dynamic time-varying characteristic, and by actually measuring the different actions of the human body and the transmission power change, The influence of the wireless link on the wireless link is analyzed and summarized, the change characteristics and the law of the transmission power and the link communication quality of the nodes in different motion states of the human body are analyzed and summarized, a power control system model based on the feedback is established, the results of human motion recognition are used to dynamically adjust the wireless volume domain, The experimental results show that the PID-A power control mechanism can ensure that the sensor node is reduced under the condition that the data packet is successfully received. The average energy consumption of the data packet is sent. (5) In order to verify the performance of the algorithm in the real system, it is designed and implemented for human motion monitoring. based on the built wireless body domain network of the micro-inertial sensor, the motion signal of the human body in the day-to-day activity is collected, the identification accuracy of the proposed action identification algorithm is actually verified, and the energy consumption of the sensor node is analyzed,
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN92;TP391.41
本文編號(hào)：2375766
[Abstract]:the wireless body domain network is a wireless network constructed from a light, wearable or implantable sensor node that can sense a variety of physiological parameters of the human body. The wireless body area network provides new means for human health monitoring, and has great application meaning and requirement in the fields of disease monitoring, health recovery, special crowd monitoring and the like. By wearing the micro-inertial sensor on the body, the body-domain network can collect the motion signal of the human body, and has wide application in human motion monitoring, and can realize the purposes of human body motion identification, abnormal motion detection, gait recognition and analysis, motion energy consumption analysis, and the like. In the process of human motion monitoring by using the wireless body domain network, how to improve the energy efficiency of the sensor nodes while meeting the requirements of the physical activity monitoring indexes, so as to be able to carry out human motion monitoring for a long time in the practical application, is a challenging question, In this paper, a wireless body-sensing network composed of a plurality of wearable micro-inertial sensors is used as the research object, and the energy efficiency is focused on the basis of the sparse representation and the compression-sensing theory, and the four aspects of signal identification, signal compression, data fusion and power control are developed. Research. Key work and innovative points such as (1) A dynamic gesture recognition method based on self-learning sparse representation is proposed. SRC. The gesture recognition problem is transformed into a sparse representation problem for solving the sample to be identified in the training sample, and the gesture recognition problem is converted into a sparse representation problem in the training sample for the sample to be identified; and the gesture recognition precision and the speed are improved. According to the problem, the sparse representation of the sample to be identified is calculated by adopting a category-based dictionary learning method, so that the calculation complexity of the identification algorithm is greatly reduced in the gesture recognition stage, and the rapid recognition is met. The proposed L-SRC gesture recognition method is verified to improve the recognition precision while the recognition accuracy is guaranteed. (2) Two types of motion recognition methods, RP-CCall, and RP-C, are proposed. Cach. Combining the time redundancy and sparsity of the motion signal, combining the compression-aware and sparse representation theory, the sensing signal compression is combined with the action recognition to meet the recognition rate of a certain action while reducing the sensor. The method comprises the following steps of: carrying out digital compression sampling on a motion signal on a sensor node by using a random projection on a sensor node, saving energy consumption by reducing the data transmission amount of the wireless body domain network, a pattern recognition model that uses the distribution of the sparse coefficients to The recognition of the present action is carried out. The theoretical analysis of the identification of the motion recognition method of the compression classification can be correctly identified. The basic condition of a sensor node that can be realized on a sensor node with limited storage and computational resources The results show that the RP-CCall method and the RP-CCeach method can achieve similar recognition accuracy when the compressed data is not compressed, and it is higher than the nearest neighbor and support vector machine. (3) An action identification method based on distributed compression-aware and combined sparse representation is presented. CS-JSRC. The spatial and temporal correlation between the motion data collected by multiple sensors in the wireless body domain network is distributed in the sensor node by the distributed compression sensing, and the correlation is fully utilized to further compress the data. in that base station, the time-space correlation of the motion signal is sensed by the base station, a joint sparse representation model suitable for action identification is constructed, and the action identification problem of the multi-sensor is converted into a multi-variable sparse linear model, The problem of regression is solved. A hierarchical Bayesian model is used to solve the sparse representation coefficient, and the correlation of different sensor nodes is used to further improve the motion. The results show that the method of the DCS-JSRC is more effective than the RP-CCall method and the RP-CCeach method in the case of the same compression ratio. High recognition accuracy. (4) A lightweight self-adaptive power feedback based on action behavior is designed The control mechanism PID-A. Aiming at the movement of the link communication quality in the wireless body domain network, the influence of the attitude change has the dynamic time-varying characteristic, and by actually measuring the different actions of the human body and the transmission power change, The influence of the wireless link on the wireless link is analyzed and summarized, the change characteristics and the law of the transmission power and the link communication quality of the nodes in different motion states of the human body are analyzed and summarized, a power control system model based on the feedback is established, the results of human motion recognition are used to dynamically adjust the wireless volume domain, The experimental results show that the PID-A power control mechanism can ensure that the sensor node is reduced under the condition that the data packet is successfully received. The average energy consumption of the data packet is sent. (5) In order to verify the performance of the algorithm in the real system, it is designed and implemented for human motion monitoring. based on the built wireless body domain network of the micro-inertial sensor, the motion signal of the human body in the day-to-day activity is collected, the identification accuracy of the proposed action identification algorithm is actually verified, and the energy consumption of the sensor node is analyzed,
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN92;TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前9條
1 王萬良;楊經(jīng)緯;蔣一波;;基于運(yùn)動(dòng)傳感器的手勢(shì)識(shí)別[J];傳感技術(shù)學(xué)報(bào);2011年12期
2 張志強(qiáng);黃志蓓;吳健康;;髖關(guān)節(jié)角多模型貝葉斯動(dòng)態(tài)估計(jì)[J];電子與信息學(xué)報(bào);2011年04期
3 方紅;章權(quán)兵;韋穗;;基于亞高斯隨機(jī)投影的圖像重建方法[J];計(jì)算機(jī)研究與發(fā)展;2008年08期
4 宮繼兵;王睿;崔莉;;體域網(wǎng)BSN的研究進(jìn)展及面臨的挑戰(zhàn)[J];計(jì)算機(jī)研究與發(fā)展;2010年05期
5 梁秀波;張順;李啟雷;張翔;耿衛(wèi)東;;運(yùn)動(dòng)傳感驅(qū)動(dòng)的3D直觀手勢(shì)交互[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2010年03期
6 胡正平;宋淑芬;;基于類別相關(guān)近鄰子空間的最大似然稀疏表示魯棒圖像識(shí)別算法[J];自動(dòng)化學(xué)報(bào);2012年09期
7 李方敏;徐文君;劉新華;;無線傳感器網(wǎng)絡(luò)功率控制技術(shù)[J];軟件學(xué)報(bào);2008年03期
8 陸希玉;陳鑫磊;孫光;金德鵬;蘇厲;曾烈光;;超寬帶體域網(wǎng)信道測量及傳輸特性分析[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年11期
9 孫洪;張智林;余磊;;從稀疏到結(jié)構(gòu)化稀疏:貝葉斯方法[J];信號(hào)處理;2012年06期
相關(guān)博士學(xué)位論文 前3條
1 薛洋;基于單個(gè)加速度傳感器的人體運(yùn)動(dòng)模式識(shí)別[D];華南理工大學(xué);2011年
2 劉蓉;人體運(yùn)動(dòng)信息獲取及物理活動(dòng)識(shí)別研究[D];華中科技大學(xué);2009年
3 姜鳴;基于體感網(wǎng)的人體動(dòng)作監(jiān)測識(shí)別的研究[D];大連理工大學(xué);2012年
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