不同場景下基于手機加速度傳感器的人體活動端點檢測研究
發(fā)布時間：2019-01-09 06:45
【摘要】：隨著可穿戴設備的普及和普適計算的不斷發(fā)展,越來越多的研究將關注點聚焦到加速度傳感器數據上來。用戶與終端便攜設備逐步呈現緊耦合的態(tài)勢,與此同時,智能手機的運算能力在近年來有飛躍性的提高,如何利用好智能移動設備高度的攜帶時間占比以及計算能力是重要的突破口。而加速度傳感器數據作為描述人體活動信息的重要組成部分,其攜帶的步態(tài)特征、行為模式等信息對于人體活動語義理解具有至關重要的意義。由智能手機收集到的用戶活動數據不僅時間跨度長而且規(guī)模龐大,對數據的處理與存儲提出了挑戰(zhàn)。因此,本文以人體活動端點檢測為出發(fā)點,將長時間復雜人體活動加速度數據當中活動起始點與終止點的提取做為目標,提出了兩種不同場景下的端點檢測算法。具體地,所做工作如下:一、針對作為信號采集設備的智能手機計算能力和內存資源的限制,提出了一種改進的雙門限人體活動端點檢測算法。針對三維空間矢量數據定義了三種不同的短時過零率。該算法可以進行粗粒度的行為活動檢測,避免上傳全部數據,節(jié)省大量的網絡傳輸帶寬以及服務器端的存儲資源。二、以改進的雙門限判別人體活動端點檢測算法為核心技術,提出了一種適用于客戶端資源受限條件下人體活動數據(加速度)傳輸策略。該策略包括相應的動態(tài)采樣策略、上傳窗口判定、數據存儲隊列以及上傳隊列的建立等內容。通過本文提出的傳輸策略,可以有效降低傳輸成本以及數據存儲成本。三、針對服務器端具體人體行為識別過程中需要更精確的活動段提取的需求,提出了基于加速度數據信息熵的人體活動端點檢測算法。并且為避免在計算三軸數據均方根時對加速度矢量方向信息的丟失構建了三維加速度信源聯合信息熵模型。該算法相較于雙門限算法雖然復雜度更高,但檢測結果更加精確,適用于在人體行為識別前期做為數據預處理步驟用以提取實際活動段數據。通過驗證實驗,證明了本文提出的雙門限方法可以有效降低數據的產生量,傳輸策略可以節(jié)省傳輸成本,信息熵檢測算法可以有效提高復雜情況下行為識別的準確率。
[Abstract]:With the popularization of wearable devices and the development of pervasive computing, more and more researches focus on acceleration sensor data. At the same time, the computing ability of smart phone has been improved by leaps and bounds in recent years. It is an important breakthrough how to make good use of the high carrying time ratio and computing power of intelligent mobile devices. As an important part of describing human activity information, acceleration sensor data, such as gait characteristics, behavior patterns and so on, are of great significance to the understanding of human activity semantics. The user activity data collected by smart phone not only has a long time span but also has a large scale, which poses a challenge to data processing and storage. Therefore, this paper takes the detection of human moving endpoint as the starting point, taking the extraction of the starting point and the ending point of the moving point from the long-time complex acceleration data of human body as the target, and proposes two kinds of endpoint detection algorithms under different scenarios. Specifically, the work is as follows: firstly, an improved dual-threshold human mobile endpoint detection algorithm is proposed for the limitation of the computing power and memory resources of the smart phone as a signal acquisition device. Three different short time zero crossing rates are defined for three dimensional space vector data. The algorithm can detect coarse-grained behavior, avoid uploading all data and save a lot of network bandwidth and storage resources on the server side. Secondly, based on the improved dual threshold discriminant human activity endpoint detection algorithm, a new method is proposed to transmit human activity data (acceleration) under the condition of limited client resource. The strategy includes the corresponding dynamic sampling strategy, upload window decision, data storage queue and the establishment of upload queue. The transmission cost and data storage cost can be effectively reduced by the proposed transmission strategy. Thirdly, aiming at the need for more accurate extraction of human activity segment in the process of human behavior recognition on the server side, an algorithm based on acceleration information entropy is proposed to detect human activity endpoint. In order to avoid the loss of direction information of acceleration vector when calculating the root mean square of triaxial data, a three-dimensional information entropy model of acceleration source is constructed. Compared with the two-threshold algorithm, the proposed algorithm is more complex, but the detection result is more accurate. It is suitable for extracting the actual active segment data as a data preprocessing step in the early stage of human behavior recognition. Through the verification experiment, it is proved that the double threshold method proposed in this paper can effectively reduce the amount of data generated, the transmission strategy can save the transmission cost, and the information entropy detection algorithm can effectively improve the accuracy of behavior recognition in complex cases.
【學位授予單位】：遼寧大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212
本文編號：2405263
[Abstract]:With the popularization of wearable devices and the development of pervasive computing, more and more researches focus on acceleration sensor data. At the same time, the computing ability of smart phone has been improved by leaps and bounds in recent years. It is an important breakthrough how to make good use of the high carrying time ratio and computing power of intelligent mobile devices. As an important part of describing human activity information, acceleration sensor data, such as gait characteristics, behavior patterns and so on, are of great significance to the understanding of human activity semantics. The user activity data collected by smart phone not only has a long time span but also has a large scale, which poses a challenge to data processing and storage. Therefore, this paper takes the detection of human moving endpoint as the starting point, taking the extraction of the starting point and the ending point of the moving point from the long-time complex acceleration data of human body as the target, and proposes two kinds of endpoint detection algorithms under different scenarios. Specifically, the work is as follows: firstly, an improved dual-threshold human mobile endpoint detection algorithm is proposed for the limitation of the computing power and memory resources of the smart phone as a signal acquisition device. Three different short time zero crossing rates are defined for three dimensional space vector data. The algorithm can detect coarse-grained behavior, avoid uploading all data and save a lot of network bandwidth and storage resources on the server side. Secondly, based on the improved dual threshold discriminant human activity endpoint detection algorithm, a new method is proposed to transmit human activity data (acceleration) under the condition of limited client resource. The strategy includes the corresponding dynamic sampling strategy, upload window decision, data storage queue and the establishment of upload queue. The transmission cost and data storage cost can be effectively reduced by the proposed transmission strategy. Thirdly, aiming at the need for more accurate extraction of human activity segment in the process of human behavior recognition on the server side, an algorithm based on acceleration information entropy is proposed to detect human activity endpoint. In order to avoid the loss of direction information of acceleration vector when calculating the root mean square of triaxial data, a three-dimensional information entropy model of acceleration source is constructed. Compared with the two-threshold algorithm, the proposed algorithm is more complex, but the detection result is more accurate. It is suitable for extracting the actual active segment data as a data preprocessing step in the early stage of human behavior recognition. Through the verification experiment, it is proved that the double threshold method proposed in this paper can effectively reduce the amount of data generated, the transmission strategy can save the transmission cost, and the information entropy detection algorithm can effectively improve the accuracy of behavior recognition in complex cases.
【學位授予單位】：遼寧大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212
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