【摘要】：移動終端技術、可穿戴式技術、移動互聯(lián)網(wǎng)技術、無線傳感器技術、嵌入式技術等領域的快速進步及相互結(jié)合,推動了智能手機傳感器的飛躍發(fā)展。智能手傳感器將虛擬世界與現(xiàn)實世界緊密地連接起來,改變了人類與環(huán)境的交互方式,使虛擬世界信息可以便捷地、有效地表達為現(xiàn)實世界信息。目前,由于可穿戴式設備的快速興起,智能手機傳感器已經(jīng)廣泛應用于用戶行為識別、用戶行為分析、導航、大型競速游戲等領域。用戶行為識別泛指通過傳感器設備收集與用戶行為密切相關的大量信息,經(jīng)過處理后對用戶行為進行分析和推理,為用戶提供更好的智能服務。本文所研究的用戶行為識別主要有兩個:駕駛行為檢測和行人安全行走檢測。對于駕駛行為檢測,本文提出了基于加速度傳感器的駕駛行為檢測方法。由駕駛事件和路面凹凸狀況引起的交通事故對司機的駕駛行為來說是非常關鍵的問題,相關研究人員已經(jīng)廣泛地研究了交通事故的原因和對策。盡管已經(jīng)提出幾種方法來解決這些問題,但是大部分方法都需要很高的計算成本或者是固定額外的硬件設備。為了應對這些挑戰(zhàn),我們設計了一個基于智能手機加速度傳感器的系統(tǒng),HealthDriving,用來檢測司機的駕駛事件和路面狀況。更具體地說,首先從智能手機的加速度傳感器收集加速度數(shù)據(jù),然后采用所設計的加速度重定向校準算法將所獲得的加速度傳感器數(shù)據(jù)轉(zhuǎn)換為汽車的加速度數(shù)據(jù),最后利用HealthDriving來檢測司機的駕駛事件和路面凹凸狀況。同時,為了評估由司機駕駛行為而引起進攻性程度,采用ISO 2631人體暴露的振動程度標準設計了一個有效的評分機制,對司機的駕駛安全體驗進行評分,得分越高,說明司機的駕駛行為越安全,攻擊性越低。反之,司機存在嚴重的危險駕駛行為。大量的評估表明,HealthDriving可以成功運作在普通的智能手機上,并且與其他方法相比,具有較低的計算成本,驗證了本方案的可行性和有效性。對于行人的安全行走行為,本文提出了基于加速度傳感器和攝像頭的行人安全行走檢測方法。最近幾年,行人在走路時使用智能手機進行閱讀或者娛樂活動已經(jīng)越來越受歡迎,低頭看手機行走使行人的注意力集中在手機屏幕上,而忽略了周圍環(huán)境的不安全。為了避免行人絆倒、跌倒,甚至與其他行人碰撞,我們設計了WalkWell,一個基于智能手機的安全行走檢測系統(tǒng),確保行人在使用手機時的安全。首先使用重力傳感器和加速度傳感器估計行人的移動速度,然后激活前置攝像頭,基于OpenCV4Android檢測人臉和眼睛,并通過眼睛灰度圖分析瞳孔的運動姿態(tài),說明行人是否正在看手機屏幕。如果看手機屏幕的時間達到了所設的閾值,WalkWell會通過手機振動的方式提醒行人注意安全。我們將Walk Well實現(xiàn)在了Android手機上,而且評估了實驗的精確率,實驗結(jié)果表明,WalkWell可以預防行人走路時長時間看手機屏幕的潛在危險。
[Abstract]:Mobile terminal technology, wearable technology, mobile Internet technology, wireless sensor technology, embedded technology and other fields of rapid progress and integration, promote the rapid development of smart phone sensors. Intelligent hand sensors connect the virtual world to the real world, change the interaction between human and the environment, and make the virtual. The proposed world information can be easily and effectively expressed as the real world information. At present, because of the rapid rise of wearable devices, smart phone sensors have been widely used in the fields of user behavior recognition, user behavior analysis, navigation, large race speed games and so on. In this paper, there are two main types of user behavior identification: driving behavior detection and pedestrian safety walking detection. In this paper, the driving behavior detection based on acceleration sensor is proposed, and the driving behavior detection based on acceleration sensor is proposed. Methods. Traffic accidents caused by driving events and road bump conditions are a key problem for drivers' driving behavior. The researchers have extensively studied the causes and Countermeasures of traffic accidents. Although several methods have been proposed to solve these problems, most of the methods require high computational cost or high cost. To cope with these challenges, we designed a system based on smart phone acceleration sensors, HealthDriving, to detect drivers' driving events and road conditions. More specifically, we first collect acceleration data from the acceleration sensors of the smartphone, and then use the designed acceleration. The degree redirection calibration algorithm converts the obtained acceleration sensor data to the acceleration data of the car. Finally, HealthDriving is used to detect driver's driving events and road bump conditions. At the same time, in order to evaluate the offensive degree caused by driver's driving behavior, a standard of vibration degree of ISO 2631 human exposure is designed. The higher the driver's driving safety experience, the higher the score, the higher the score, the safer driving, the lower the aggressiveness. On the other hand, the driver has serious dangerous driving behavior. A large number of evaluations show that HealthDriving can be successfully operated on a normal smartphone and compared with other methods. Low computing cost proves the feasibility and effectiveness of this scheme. For pedestrian safe walking, a pedestrian safety walking detection method based on acceleration sensor and camera is proposed. In the last few years, pedestrians have been getting more and more popular with their smartphones for reading or entertainment during walking. In order to avoid pedestrians tripping, falling, and even colliding with other pedestrians, we designed WalkWell, a smart mobile detection system based on smart phones, to ensure the safety of pedestrians in the use of a mobile phone. First, the use of gravity sensors and the use of a gravity sensor. The acceleration sensor estimates the moving speed of the pedestrian, then activates the front camera, detects the face and eyes based on the OpenCV4Android, and analyzes the movement of the pupil through the eye grayscale, indicating whether the pedestrians are looking at the mobile screen. If the time of watching the phone screen is reached, the WalkWell will pass through the vibration of the cell phone. We remind pedestrians to pay attention to safety. We implemented Walk Well on a Android phone and evaluated the accuracy of the experiment. The experimental results showed that WalkWell could prevent the potential danger of a long time looking at a mobile phone screen when pedestrians walk.
【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP212

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