無線體域網(wǎng)中人體動作監(jiān)測與識別若干方法研究
發(fā)布時間：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)點構(gòu)建的無線網(wǎng)絡。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、特殊人群監(jiān)護等領(lǐng)域有著巨大的應用意義和需求。通過佩戴在身體上的微慣性傳感器,體域網(wǎng)可以采集人體的運動信號,在人體動作監(jiān)測方面得到廣泛應用,可實現(xiàn)人體動作識別、異常動作檢測、步態(tài)識別與分析、運動能耗分析等目的。 在利用無線體域網(wǎng)進行人體運動監(jiān)測過程中,如何在滿足身體活動監(jiān)測指標要求的同時提高傳感器節(jié)點的能量有效性,以便能在實際應用中長時間不間斷地進行人體動作監(jiān)測,是一個具有挑戰(zhàn)性的問題。本文以由多個可穿戴的微慣性傳感器構(gòu)成的無線體感網(wǎng)為研究對象,圍繞能量有效性,以稀疏表示和壓縮感知理論為主線,從信號識別、信號壓縮、數(shù)據(jù)融合、功率控制這四個方面展開研究。主要工作和創(chuàng)新點如下： (1)提出了一種基于自學習稀疏表示的動態(tài)手勢識別方法L-SRC.針對手勢識別中手勢長短不一的問題,將手勢樣本向量進行歸一化線性插值,從而將手勢識別問題轉(zhuǎn)化為求解待識別樣本在訓練樣本中的稀疏表示問題；針對如何提高手勢識別精度和速度的問題,采用基于類別的字典學習方法尋求一個較小的并經(jīng)過優(yōu)化的超完備字典來計算待識別樣本的稀疏表示,從而在手勢識別階段大幅度縮減識別算法的計算復雜度,滿足快速識別要求。在包含18種手勢的數(shù)據(jù)集上驗證了提出的L-SRC手勢識別方法在保證識別精度的同時提升了識別速度。 (2)提出了兩種壓縮分類的動作識別方法RP-CCall和RP-CCeach.針對運動信號的時間冗余性和稀疏性,結(jié)合壓縮感知和稀疏表示理論,將傳感信號壓縮與動作識別相結(jié)合,以滿足一定動作識別率的同時降低傳感器節(jié)點的能耗。兩種RP-CC方法是在傳感器節(jié)點上利用隨機投影對運動信號進行數(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；在基站上直接對壓縮的數(shù)據(jù)建立稀疏表示的人體運動模式識別模型,利用稀疏系數(shù)的分布來實現(xiàn)動作識別。理論分析了壓縮分類動作識別方法能正確識別的基本條件。找到了能在存儲和計算資源有限的傳感器節(jié)點上實現(xiàn)的隨機投影矩陣。在包含13種動作的數(shù)據(jù)集上進行了驗證,結(jié)果顯示RP-CCall方法和RP-CCeach方法在對壓縮的數(shù)據(jù)識別時也能達到無壓縮時相近似的識別準確率,并高于最近鄰、支持向量機等分類方法。 (3)提出了基于分布式壓縮感知和聯(lián)合稀疏表示的動作識別方法DCS-JSRC.針對無線體域網(wǎng)中多傳感器采集的運動數(shù)據(jù)之間的時空相關(guān)性,采用分布式壓縮感知在傳感器節(jié)點進行分布式壓縮,充分利用這種相關(guān)性來進一步壓縮數(shù)據(jù)以降低傳輸能耗。在基站通過探索多傳感器節(jié)點感知運動信號的時空相關(guān)性,構(gòu)建適用于動作識別的聯(lián)合稀疏表示模型,將多傳感器的動作識別問題轉(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)點的相互關(guān)聯(lián)來進一步提高動作識別的準確率。在動作數(shù)據(jù)集上進行驗證,實驗結(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方法更高的識別準確率。 (4)設計了輕量級的基于動作行為的自適應功率反饋控制機制PID-A。針對無線體域網(wǎng)中鏈路通信質(zhì)量受人的運動、姿態(tài)變化影響具有動態(tài)時變特性,通過實測人體不同動作以及發(fā)射功率變化對無線鏈路的影響,分析和總結(jié)了在人體不同運動狀態(tài)下節(jié)點的發(fā)射功率與鏈路通信質(zhì)量的變化特性和規(guī)律,在此基礎上建立基于反饋的功率控制系統(tǒng)模型,結(jié)合人體動作識別的結(jié)果,來動態(tài)調(diào)整無線體域網(wǎng)中節(jié)點的發(fā)射功率。實驗結(jié)果顯示PID-A功率控制機制可保證在數(shù)據(jù)包成功接收的條件下降低了傳感器節(jié)點發(fā)送數(shù)據(jù)包的平均能耗。 (5)為了驗證算法在實際系統(tǒng)中的性能,設計并實現(xiàn)了用于人體運動監(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�；顒又械膭幼餍盘�,實際驗證了所提出的動作識別算法的識別準確率,并對傳感器節(jié)點的能耗進行了分析,驗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,
【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN92;TP391.41
本文編號：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,
【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN92;TP391.41
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