利用惯性传感器的人体运动空间轨迹追踪

人体运动的空间轨迹追踪是一种利用传感器技术和计算机技术来分析记录人体的运动过程的方法.为了实现人体运动轨迹的空间追踪,本文设计了一种人体可穿戴式的人体运动捕捉系统,通过佩戴在人体关节点的惯性传感器单元来获取肢体的实时姿态信息.惯性传感器由加速度传感器、角速度传感器和磁力计构成.通过微控制单元获取传感器数据,利用低通滤波和卡尔曼滤波来更新四元数,再将预处理后的数据由蓝牙模块实时发送到电脑端.本文通过对肢体运动的不同角度的实验,证明了利用惯性传感器可以追踪人体肢体、运动的空间轨迹.     

基于惯性传感器的动作捕捉系统设计

针对传统的如光学式、机械式等动作捕捉系统在成本、使用环境及精度等方面各有优劣的特点,设计了一套基于惯性传感器的动作捕捉系统,通过在人体各部位佩戴惯性传感器节点,实时采集各部位的运动惯性数据,通过WIFI通信发送至数据处理终端,对数据进行存储和显示。经实验验证所设计动作捕捉系统能够正确采集人体惯性运动数据,系统的使用不受时间和地点的限制,具有成本低廉、穿戴方便和操作简单等特点,可以深入应用于医学、体育和军事等多个领域。     

基于视频的三维人体运动跟踪系统的设计与实现

在优化粒子滤波跟踪框架下,设计并实现了一个结合多种图像特征、在多摄像机环境下跟踪人体运动的三维人体运动跟踪系统.通过定义三维人体模型、摄像机模型以及观测似然模型,得到跟踪所需目标函数,并使用优化粒子滤波算法进行求解.实验结果表明,该系统能够对人体运动进行准确的跟踪和三维重建,可应用于体育运动分析和动画制作等领域.     

基于序列图像的人体跟踪算法研究综述

为了综述目前基于序列图像的人体跟踪算法的研究现状,将人体跟踪问题分为人体运动目标检测与跟踪两部分分别加以讨论,系统地总结和介绍了目前人体检测与跟踪算法的研究技术和最新进展,比较了各种跟踪方法的优劣并总结了跟踪方法常用的搜索策略.针时人体跟踪算法中的难点问题进行了专门讨论,通过总结近几年相关文献对人体跟踪算法研究方向进行了展望.     

一种基于传感器的人体上肢运动实时跟踪方法

实时跟踪人体运动是人机交互的重要研究课题,可以广泛应用于虚拟现实、虚拟人运动合成、聋人手语自动生成、计算机3D动画、机器人运动控制、远程人机交互等领域。文中介绍了一种基于传感器的人体上肢运动实时跟踪方法,给出了该方法的虚拟人模型、计算原理与校正方法,最后介绍了整个方法的实现以及在中国聋人手语自动合成中的应用。该方法具有使用传感器少,运动跟踪精度高、计算过程简单而且速度快等特点。     

基于多传感器融合的人体运动分析系统设计

由于光学式、机械式等传统运动测量系统在成本、使用环境及精度等方面的缺陷,设计了一款主要基于惯性传感器的手臂关节人体运动测量系统,通过在人体特定部位佩戴所设计的惯性传感器节点,可实时采集人体各部位的运动数据,并以数据帧格式,通过WIFI无线传感器网络将各节点所采集的运动信息发送至所设计的STM32主机运算单元,通过使用STM32内置DSP-CMSIS库进行卡尔曼滤波和姿态融合,加快运算速度,降低节点的运算量,使得节点的体积更小;再通过串口将数据传输至上位机进行存储和显示;经实验验证,设计的运动测量系统能够准确采集人体运动数据,并经转台实验所测姿态角相对误差小于2.5%;同时通过三维仿真,可以较好复现人体运动轨迹,该系统具有成本低廉,穿戴方便和操作简单等特点,可广泛应用于医学,体育等多个领域。     

适用于户外增强现实系统的混合跟踪定位算法

单一传感器无法解决户外增强现实系统中的跟踪定位问题.为了提高视觉跟踪定位算法的精度和鲁棒性,提出一种基于惯性跟踪器与视觉测量相结合的混合跟踪定位算法.该算法在扩展卡尔曼滤波框架下,通过融合来自视觉与惯性传感器的信息进行摄像机运动轨迹估计,并利用视觉测量信息对惯性传感器的零点偏差进行实时校正;同时采用SCAAT方法解决惯性传感器与视觉测量间的时间采样不同步问题.实验结果表明,该算法能够有效地提高运动估计的精度和稳定性.     

基于惯性传感器的步态身份识别

基于步态的身份识别作为一种新的生物特征识别技术，以其非接触、无打扰、远距离、不易伪装等优点成为了生物特征识别技术领域的研究热点。此外，近年来，由于MEMS惯性传感器技术发展成熟及其在便携式设备中的广泛应用，基于惯性传感器的步态身份识别越来越受到科研人员的关注。文中收集整理了国内外有关惯性传感器步态身份识别的研究方法和现状，并对该领域的相关技术进行了回顾；根据识别过程处理的先后顺序，依次回顾了数据采集、数据预处理、数据分割、特征选择与组合、智能识别各个阶段的相关技术以及研究现状，并给出了目前主要的公共步态数据库，以方便感兴趣的读者进行实验分析。最后，在此基础上讨论了基于惯性传感器的步态身份识别的技术难点，并对未来发展方向进行了展望。     

基于视觉的人体运动捕捉技术研究

运动捕捉技术是计算机视觉和人体运动分析领域的研究热点,在计算机动画等领域拥有广泛的应用前景。在总结基于视觉的人体运动捕捉技术进展的基础上,分析运动跟踪、捕捉方法及技术难点,提出一种新的从视频提取人体运动信息,重现人体运动轨迹的方法、流程及系统设计框架。     

面向人体健康防护的光纤生化传感技术

光纤生化传感器因具有不受电磁干扰、绝缘性好、安全防爆、损耗低和耐腐蚀等优点,在人体健康防护领 域表现出了良好的应用价值:及时且有效地监测污染物可为环境污染物的有效控制提供数据支撑,从源头上减少疾病的发生;准确且灵敏地检测疾病标志物可诊断人体疾病的情况,从而使得疾病的预防和早期治疗成为可能.对应用于人体健康防护中的光纤生化传感器进行全方位地概述:首先,介绍其使用的光纤传感原理;然后,总结并分析应用于气体和水质污染物监测的光纤生化传感技术;接着,总结并分析应用于人体呼出气体和生物体液检测的光纤生化传感技术;最后,讨论光纤生化传感器在人体健康防护领域实际应用的一些限制因素以及未来的发展方向.     

一种基于蒙特卡罗法的无线传感器网络移动节点定位算法研究

定位技术是无线传感器网络中关键的基础支撑技术。文章研究无线传感网中信标节点静止、定位节点随机运动情况下的节点定位方法。在传统MCL算法基础上,提出一个改进定位算法,通过构建节点运动模型,进行运动预测、位置滤波,提高定位精度。仿真结果表明,改进方案在不同的信标节点密度、不同的连接度、不同的节点运动速度等情况下,均表现出了良好的性能。     

Direction finding of moving ferromagnetic objects inside water by magnetic anomaly

The remote sensing methods by the use of magnetic anomaly are gaining importance in applications of defense technologies and industrial purposes recently. In this study, it is aimed to determine the remote detection, the variation of characteristic of the voltage in the sensor relative to the motion, the effects of material length, magnetic permeability and direction of motion of the object on this characteristic and to convert them to a useful mathematical expression by using magnetic anomaly of ferromagnetic objects such as submarines moving inside water. For this purpose, first of all, a water tank of 1 m³ is prepared and approximately a homogeneous magnetic field of 10⁻³ T is created within this water tank. Ferromagnetic materials with six different lengths and permeabilities are moved in three different directions relative to the position of the sensor by means of a computer controlled x–y scanner designed for this experiment inside this magnetic field. The magnetic change caused by this motion at the point where the sensor is positioned is detected as the output voltage of the sensor. A mathematical expression is formulated taken into account the variations of the sensor output voltage with respect to the length, magnetic permeability and the direction of motion of the material and it is validated by the experimental results. This study clearly shows that the existence and the direction of motions of ferromagnetic objects with different lengths and magnetic permeabilities inside water can be detected with high accuracy.     

Hierarchical querying scheme of human motions for smart home environment

With the recent development of ubiquitous technologies, many new applications have been emerging for smart home implementation. Usually, such applications are based on diverse sensors. One fundamental operation in the applications is to find out semantically meaningful events or activities from huge sensor data stream. Usually, such event or activity is represented by a salient sequence pattern. Among the diverse research issues, detecting salient sequence patterns of human motions from image sensor data stream has received much attention for security and surveillance purposes. In the case of detecting human motions from image sensor data, finding and matching their salient sequence patterns could become more complicated since semantically same motions could show diverse variations such as different motion time. Based on this observation, in this paper, we propose a new querying and answering scheme for continuous sensor data stream to detect abnormal human motions. More specifically, we first present a new hierarchical querying scheme to consider variable length of semantically same human motions. Secondly, we present an indexing scheme to efficiently find semantically meaningful motion sequences in the sensor data stream. Thirdly, we present Dynamic Group Warping algorithm to effectively filter out unnecessary human motions. Through extensive experiments, we show that our proposed method achieves outstanding performance.     

基于无线传感网的海洋监测节点定位算法

针对应用于海洋监测的无线传感器网络,提出了一种基于蒙特卡罗算法的节点定位算法。该算法根据海洋中洋流在某一时间段内线性运动的特性,通过引入符合海水运动规律的角度来提高节点在位置预测阶段的精度;并根据节点感知压力的大小来确定预测坐标的置信度,以对预测坐标进行修正,进而得到最终的预测结果。仿真结果表明,该算法比传统的定位方法在不同的锚节点密度、节点密度、节点运动速度和时间等条件下都表现出更好的性能。     

A Scalable Agent Architecture for a Dynamic Sensor Network

Information systems for networks of small and inexpensive sensors attached to computational nodes in distributed networks will in the future be required for applications where the focus is to assess information about different ground based targets in motion. Operations of concern to these applications are, for instance, to determine the type and track of these targets, processes that generally require methods for sensor data fusion. Of special importance is also that such networks must be able to expand and shrink with respect to the number of sensors and nodes, that is, a property called scalability. Clearly, traditional and centralized architectures are not suitable since they will lead to very complex systems that eventually will be very difficult to maintain and accommodate to changing requirements. For this reason novel systems architectures are required. In this work, an architecture based on systems of intelligent multi-agents is proposed. The architecture has been implemented and tested in a simulated environment.     

利用惯性传感器和AdaBoost算法的步态识别方法

针对现有方法在步态识别方面存在的动作信号分割、传感器方向不一致和相似动作识别精度不高等缺点,提出一种基于惯性传感器和AdaBoost算法的步态动作识别方法。首先,基于尺度空间技术,提出了一种鲁棒的步态检测方法将信号分为动作样本,以此来应对动作速度或强度的剧烈变化;然后,应用定位补偿匹配算法纠正传感器的倾斜,从而解决了传感器方向不一致的问题;最后,为了提高识别精度,基于AdaBoost算法自适应选取动作特征,再进行判别分析完成识别。对5个相似步态动作类进行识别实验,结果表明提出的算法具有较高的准确性。     

A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system

Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches(86% reducing for a 0.5 min visual tracking). Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme(edge detection), and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration.     

Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition

The proper orthogonal decomposition (POD) has been widely used in fluid dynamic applications for extracting dominant flow features. The “gappy” POD is an extension to this method that allows the consideration of incomplete data sets. In this paper, the gappy POD is extended to handle unsteady flow reconstruction problems, such as those encountered when limited flow measurement data is available. In addition, a systematic approach for effective sensor placement is formulated within the gappy framework using a condition number criterion. This criterion allows for accurate flow reconstruction results and yields sensor configurations that are robust to sensor noise. Two applications are considered. The first aims to reconstruct the unsteady flow field using a small number of surface pressure measurements for a subsonic airfoil undergoing plunging motion. The second considers estimation of POD modal content of a cylinder wake flow for active control purposes. In both cases, using the dominant POD basis vectors and a small number of sensor signals, the gappy approach is found to yield accurate flow reconstruction results.