【摘要】：如今智能手機由于配備了大量的傳感器、計算和存儲資源,使得基于各種傳感器的應用出現(xiàn)在各種領域,如個人健康、環(huán)境監(jiān)測、社交網(wǎng)絡。目前,智能手機集成了豐富的傳感器,如加速度計,陀螺儀,全球定位系統(tǒng),麥克風,相機,亮度傳感器,Wi-Fi和藍牙接口,為人體活動識別提供一個合適的平臺系統(tǒng),也為研究人員提供了更為簡單、方便的傳感器數(shù)據(jù)獲取方案。本文針對集成在智能手機中的GPS模塊和三軸加速度傳感器,提出了利用GPS速度數(shù)據(jù)結合人體在活動狀態(tài)下產(chǎn)生的三軸加速度數(shù)據(jù)對老年人的室內和室外的行為進行實時識別以達到對老年人的行為監(jiān)護的目的。本文的重點工作如下:(1)總結了基于加速度傳感器的行為識別的工作流程及研究方法,對人體行為識別的各個功能模塊分別作了詳細的介紹。針對如今已經(jīng)普及的智能手機提出一種基于智能手機的面向老年人的行為識別方法,該方法基于智能手機中的加速度傳感器和GPS模塊提出了一種新的行為數(shù)據(jù)模型,并采用該數(shù)據(jù)模型采集人體的行為數(shù)據(jù)。(2)對數(shù)據(jù)的分割進行研究,并為本系統(tǒng)選取了合適大小的滑動窗口對人體行為數(shù)據(jù)進行分割,隨后列出本系統(tǒng)所使用的特征值。最后本章設計相應的對比實驗證明當分類模型采用包含GPS速度特征值的特征向量進行訓練和識別時相比未包含GPS特征值的特征向量的準確率要高,特別在人體乘車行為的識別準確率上有大幅度的提升。(3)在本文提出的基于智能手機加速度傳感器和GPS模塊的數(shù)據(jù)模型和行為識別方法的基礎上,設計并實現(xiàn)了人體運動模式實時識別系統(tǒng)。系統(tǒng)實現(xiàn)了實時從移動終端接收人體行為數(shù)據(jù)并在后臺處理、分類得出當前用戶所處的位置是室內還是室外,并且把用戶活動模式歸類為靜坐、站立、行走、跑步、騎車、乘車這6種狀態(tài)。
[Abstract]:Today smart phones are equipped with a large number of sensors, computing and storage resources, making sensor-based applications in a variety of areas, such as personal health, environmental monitoring, social networks. Today, smart phones integrate a wealth of sensors such as accelerometers, gyroscopes, global positioning systems, microphones, cameras, luminance sensors, Wi-Fi and Bluetooth interfaces to provide an appropriate platform for human activity recognition. It also provides a simple and convenient sensor data acquisition scheme for researchers. In this paper, the GPS module and the three-axis acceleration sensor integrated into the smart phone, Using the GPS velocity data and the triaxial acceleration data produced by the human body in the active state, the real-time recognition of the behavior of the elderly in indoor and outdoor is proposed to achieve the purpose of monitoring the behavior of the elderly in order to achieve the purpose of monitoring the behavior of the elderly. The main work of this paper is as follows: (1) the workflow and research methods of behavior recognition based on acceleration sensor are summarized, and the functional modules of human behavior recognition are introduced in detail. Based on the acceleration sensor and GPS module in smart phones, a new behavior data model is proposed, which is based on the smart phone-oriented behavior recognition method for the elderly. The data model is used to collect the human behavior data. (2) the segmentation of the data is studied, and the sliding window of appropriate size is selected to segment the human behavior data, and then the characteristic values used in the system are listed. Finally, a comparative experiment is designed to prove that when the classification model is trained and recognized by the Eigenvectors containing the GPS velocity eigenvalues, the accuracy of the Eigenvectors without the GPS eigenvalues is higher than that of the classification models. Especially, the recognition accuracy of human ride behavior has been greatly improved. (3) on the basis of the data model and behavior recognition method based on smart phone acceleration sensor and GPS module proposed in this paper, A real-time human motion pattern recognition system is designed and implemented. The system realizes real-time receiving the human behavior data from the mobile terminal and processing it in the background, classifies whether the current user's position is indoor or outdoor, and classifies the user's activity mode as sit-in, stand, walk, run, and bike, and classify the user's activity mode as sitting, standing, walking, running, biking. Take a ride in these six states.
【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】：TP212.9

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