本文選題：可穿戴計算 + 慣性測量單元��； 參考：《南京郵電大學》2017年碩士論文

【摘要】：近年來,隨著微控制單元(Micro Control Unit,MCU)、微電子機械系統(tǒng)(Microelectromechanical Systems,MEMS)和無線傳感網(wǎng)(Wireless Sensor Network,WSN)的快速發(fā)展,基于無線可穿戴設備的動作捕捉系統(tǒng)具有良好的應用前景和巨大的商業(yè)價值。慣性測量單元(Inertial Measurement Unit,IMU)具有體積小、便于攜帶以及成本低等優(yōu)點,目前在醫(yī)療康復、老人監(jiān)護等重要領域已得到廣泛應用。然而由于需要長時間運行傳感節(jié)點和高頻高速率實時傳輸數(shù)據(jù)流等,基于慣性測量單元的動作捕捉系統(tǒng)存在能效低、續(xù)航能力弱等問題。針對上述問題,本文研究在低功耗約束環(huán)境下,結(jié)合情景感知技術(shù),探索可穿戴計算中基于慣性測量單元的動作捕捉系統(tǒng)的能效提高策略,并在無線可穿戴平臺Shimmer2R上對準確率優(yōu)化和能耗降低方面進行了驗證。主要研究成果如下:(1)實現(xiàn)了腕戴式單傳感器動作捕捉系統(tǒng)。目前較為流行的動作捕捉系統(tǒng)采用的均是三軸加速度傳感器,現(xiàn)有的系統(tǒng)存在有線牽制、佩戴節(jié)點多、佩戴設備重等不足。針對當前存在的問題,本文提出一種腕戴式運動識別系統(tǒng),采用佩戴在實驗者右手手腕上的單個慣性傳感器進行數(shù)據(jù)采集。在不影響使用者日�；顒拥那疤嵯聺M足佩戴的舒適度,并且能夠達到較好的行為識別準確率,同時降低系統(tǒng)的計算復雜度。(2)實現(xiàn)了人體運動識別優(yōu)化算法,用于提高動作捕捉系統(tǒng)的能效。本文設計了適用于人體運動識別分類模型的優(yōu)化算法,用于提高運動識別系統(tǒng)的整體能效性。以往的算法中由于分類模型存在參數(shù)選定問題,默認的參數(shù)值不能適應不同個體的行動特點。本文提出對于不同的分類模型參數(shù)分別進行優(yōu)化,針對不同個體的運動習慣求得最佳參數(shù)組合,達到提升人體運動識別的分類準確率。實驗結(jié)果表明,系統(tǒng)優(yōu)化算法能夠有效提高人體分類準確率,分類準確率最高可以達到98.59%。相比傳統(tǒng)分類器,分類準確率最高可以提高26.05%。(3)提出了自適應無線傳輸控制算法和多分類器融合的自適應控制算法。動作捕捉系統(tǒng)所采用的無線可穿戴節(jié)點的能耗主要集中在無線傳輸通信模塊。高速實時的無線傳輸消耗了傳感節(jié)點大部分的能量。本文提出自適應無線傳輸控制算法,結(jié)合情景感知技術(shù),對人體運動數(shù)據(jù)進行處理判斷,并能自適應控制無線傳輸?shù)臄?shù)據(jù)量,可以有效的降低無線傳輸?shù)哪芎�。并在此基礎上,提出了多分類器融合的自適應數(shù)據(jù)傳輸控制。實驗結(jié)果表明,提出的自適應數(shù)據(jù)傳輸控制方法能夠在不降低分類準確率的前提下,減少無線通信的數(shù)據(jù)傳輸量,數(shù)據(jù)傳輸削減量最高可達76.7%。通過此方法提高設備能效性和節(jié)點的續(xù)航能力,續(xù)航時間最高可延長73.38%。
[Abstract]:In recent years, with the rapid development of Micro Control Unit (MCU), Microelectromechanical Systems (MEMSs) and Wireless Sensor Network (WSNs), the motion capture system based on wireless wearable devices has a good application prospect and great commercial value. Inertial Measurement Unit (IMU) has many advantages such as small size, easy to carry and low cost. At present, it has been widely used in many important fields, such as medical rehabilitation, care for the elderly and so on. However, due to the need of long time operation of sensor nodes and real-time transmission of high frequency and high speed data streams, the motion capture system based on inertial measurement unit has some problems, such as low energy efficiency and weak endurance. In order to solve the above problems, this paper studies the strategy of improving the energy efficiency of the motion capture system based on inertial measurement unit in wearable computing under low power constraints and scenario sensing technology. The accuracy optimization and energy consumption reduction are verified on Shimmer2R, a wireless wearable platform. The main research results are as follows: 1) the single sensor motion capture system is realized. At present, the popular motion capture system uses three-axis acceleration sensor, the existing system has some shortcomings, such as cable traction, wearing more nodes, wearing equipment weight, and so on. Aiming at the existing problems, this paper presents a motion recognition system based on wrist wearing, which uses a single inertial sensor which is worn on the right hand wrist of the experimenter to collect data. On the premise of not affecting the user's daily activities, it can satisfy the comfortable degree of wearing, and can achieve better accuracy of behavior recognition, and reduce the computational complexity of the system. At the same time, it realizes the optimization algorithm of human motion recognition. Used to improve the energy efficiency of motion capture systems. In this paper, an optimization algorithm suitable for classification model of human motion recognition is designed to improve the overall energy efficiency of motion recognition system. In the previous algorithms, because of the problem of parameter selection in the classification model, the default parameter values can not adapt to the behavior characteristics of different individuals. In this paper, the parameters of different classification models are optimized, and the best combination of parameters is obtained according to different individuals' movement habits, so as to improve the classification accuracy of human motion recognition. Experimental results show that the system optimization algorithm can effectively improve the accuracy of human classification, the highest classification accuracy can reach 98.5959. Compared with the traditional classifier, the classification accuracy can be improved by 26.05. (3) the adaptive wireless transmission control algorithm and the multi-classifier fusion adaptive control algorithm are proposed. The energy consumption of the wireless wearable nodes used in the motion capture system is mainly concentrated in the wireless transmission communication module. High-speed and real-time wireless transmission consumes most of the energy of sensor nodes. In this paper, an adaptive wireless transmission control algorithm is proposed, which can process and judge human motion data with scene sensing technology, and can adaptively control the amount of wireless transmission data, which can effectively reduce the energy consumption of wireless transmission. On this basis, a multi-classifier fusion adaptive data transmission control is proposed. The experimental results show that the proposed adaptive data transmission control method can reduce the amount of wireless communication data transmission without reducing the classification accuracy, and the maximum reduction amount of data transmission can be up to 76.7. By using this method, the energy efficiency of the equipment and the capacity of the node can be improved, and the maximum life span can be extended by 73.38.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP368.33

【參考文獻】

相關(guān)期刊論文 前5條

1 楊璐璐;陳建新;周亮;魏昕;;基于無線體域網(wǎng)的囚犯異常行為實時分析[J];計算機科學;2015年03期

2 蘇鍵;陳軍;何潔;;主成分分析法及其應用[J];輕工科技;2012年09期

3 李曉丹;肖明;曾莉;;人體動作捕捉技術(shù)綜述以及一種新的動作捕捉方案陳述[J];中國西部科技;2011年15期

4 童恩棟;沈強;雷君;劉宇;唐暉;;物聯(lián)網(wǎng)情景感知技術(shù)研究[J];計算機科學;2011年04期

5 韓樹人;溫如春;王軍;;無線傳感器網(wǎng)絡中的nesC嵌入式編程語言[J];單片機與嵌入式系統(tǒng)應用;2010年05期

相關(guān)碩士學位論文 前2條

1 周暄承;可穿戴計算中能效提高策略研究[D];南京郵電大學;2016年

2 周生強;腕帶式跌倒檢測系統(tǒng)研究[D];南京郵電大學;2016年

，

本文編號：2007046