Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking

Real-time tracking of human body motion is an important technology in synthetic environments, robotics, and other human-computer interaction applications. This paper presents an extended Kalman filter designed for real-time estimation of the orientation of human limb segments. The filter processes data from small inertial/magnetic sensor modules containing triaxial angular rate sensors, accelerometers, and magnetometers. The filter represents rotation using quaternions rather than Euler angles or axis/angle pairs. Preprocessing of the acceleration and magnetometer measurements using the Quest algorithm produces a computed quaternion input for the filter. This preprocessing reduces the dimension of the state vector and makes the measurement equations linear. Real-time implementation and testing results of the quaternion-based Kalman filter are presented. Experimental results validate the filter design, and show the feasibility of using inertial/magnetic sensor modules for real-time human body motion tracking     

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.     

Minimum-Order Kalman Filter With Vector Selector for Accurate Estimation of Human Body Orientation

This paper describes a new quaternion-based Kalman filter (KF) for estimating human body orientation using an inertial/magnetic sensor. The proposed algorithm is comprised of a quaternion measurement step and a KF step that are connected in feedback relationship. This allows the algorithm to have a minimum-order structure (i.e., fourth order) that is computationally very efficient. Furthermore, to offer more reliable information to the quaternion measurement step, a vector selector scheme is adopted, which effectively adds the gyro measurement to the so-called Wahba's problem that conventionally uses only the accelerometer and magnetometer measurements. This protects the algorithm against undesirable conditions such as fast movements and temporary magnetic disturbances, enabling it to compute an accurate orientation estimate. Due to the computational efficiency of the algorithm, it is suitable for real-time ambulatory human motion tracking applications that require multiple and untethered inertial/magnetic sensors with low-cost onboard processing.     

Wearable sensors for 3D upper limb motion modeling and ubiquitous estimation

Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health care, and navigation. Because of the agility, upper limb motion estimation is the most difficult problem in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and then estimate their movements separately; therefore, the estimated motion are always with serious distortion. In this paper, we propose a novel ubiquitous upper...     

Motion trajectory of human arms based on the dual quaternion with motion tracker

In this paper, we present an algorithm for human motion capture of the real-time motion trajectory of human arms based on wireless inertial 3D motion trackers. It aims to improve the accuracy of inertial motion captures and quickly reconstruct some human movements. To evaluate the performance of the proposed dual quaternion algorithm, we present the prototype design. The wireless inertial measurement system and Kinect device are introduced simultaneously in capturing human motion. The dual quaternion algorithm incorporates features of the quaternion rotation and translation. So the singular points of Euler angles can be avoided. Dual quaternion algorithm and DCM(direction cosine matrix) are used to reconstruct human arm movements respectively. Compared with the computing speed in Matlab, the speed of the dual quaternion is faster than it of DCM. To the end, we propose a 3D ADAMS human robotic model for simulating the motion trajectory using dual quaternion algorithm. The results show that the dual quaternion can achieve capabilities of a positive DCM solving, which completed between body segments rotating and translating the coordinate system transformation. Also it can effectively drive in real-time a human model to animate movement, and provide a good algorithm.     

A human–robot interface using particle filter,Kalman filter,and over-damping method

This paper presents a novel human–manipulator interface which copies the hand motion to control a manipulator. In the proposed interface, an inertial measurement unit is used to measure the orientation of the human hand, and a 3D camera is employed to locate the human hand using the Camshift algorithm. Although the position and the orientation of the human can be obtained from two sensors, the measurement errors increase over time due to the noise of the devices and the tracking errors. Therefore, particle filter and Kalman filter are used to estimate the position and the orientation of the human hand. Due to the limitations of the perceptive and the motor, human operator cannot accomplish the high-precision manipulation without any assistance. An over-damping method is employed to assist the operator to improve the accuracy and reliability in determining the postures of the manipulator. The human–manipulator interface system was experimentally tested in a lab environment, and the results indicate that such an interface can successfully control a robot manipulator even when the operator is not an expert.     

网络化人体运动跟踪系统研究

针对目前基于惯性传感的动作捕捉系统存在的姿态漂移、实时性不强和价格较高的问题,设计了一种低功耗、低成本,能够有效克服姿态数据漂移的人体实时动作捕捉系统。首先通过人体运动学原理,构建分布式关节运动捕捉节点,各捕捉节点采用低功耗模式,当节点采集数据低于预定阈值时,自动进入休眠模式,降低系统功耗;结合惯性导航和Kalman滤波算法对人体运动姿态进行实时的解算,以降低传统的算法存在的数据漂移问题;基于Wi-Fi模块,采用TCP-IP协议对姿态数据进行转发,实现对模型的实时驱动。选取多轴电机测试平台对算法的精度进行了评估,并对比了系统对真实人体的跟踪效果。实验结果表明,改进算法与传统的互补滤波算法相比具有更高的精度,基本能将角度漂移控制在1°以内;且算法的时延相对于互补滤波没有明显的滞后,基本能够实现对人体运动的准确跟踪。     

Recognizing multiple human activities and tracking full-body pose in unconstrained environments

Visual observations, such as camera images, are hard to obtain for long-term human motion analysis in unconstrained environments. In this paper, we present a method for human full-body pose tracking and activity recognition from measurements of few body-worn inertial orientation sensors. The sensors make our approach insensitive to illumination and occlusions and permit a person to move freely. Since the data provided by inertial sensors is sparse, noisy and often ambiguous, we use a generative prior model of feasible human poses and movements to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific manifold embeddings that significantly restrict the search space for pose tracking. Using a particle filter, our method continuously explores multiple pose hypotheses in the embedding space. An efficient activity switching mechanism governs the distribution of particles across the activity-specific manifold embeddings. Selecting a pose hypothesis that best explains incoming sensor observations simultaneously allows us to classify the activity a person is performing and to estimate the full-body pose. We also derive an effective measure of predictive confidence that enables detecting anomalous movements. Experiments on a multi-person data set containing several activities show that our method can seamlessly detect activity switches and accurately reconstruct full-body poses from the data of only six wearable inertial sensors.     

基于惯性传感器的上肢位置跟踪

为辅助中风病人的康复训练,设计了一种手腕固定惯性测量单元的上肢位置跟踪方案.导航定位算法采用传统的捷联惯性导航解算算法,并在此基础上根据标准康复训练中手臂做屈伸运动时速度周期性为零的特征,引入零速修正技术(ZUPT).采用姿态检测最优算法检测零速区间,将此时惯性导航解算的速度作为量测值进行Kalman滤波,对系统的速度、姿态、位置、加速度计和陀螺仪常值零偏误差进行估计,将估计结果反馈以修正捷联惯性导航的累积误差.同时在Kalman滤波的基础上设计了固定区间RTS平滑算法,解决了零速修正引起的运动轨迹的突变问题.实验结果证明,该方案可以有效地实现上肢位置跟踪,在运动时间为108 s的情况下,定位误差为运动路程的0.089％.     

结合部件分割和粒子滤波的3D人体运动跟踪

尽管随机采样降低了陷入局部极值的风险,但不能保证收敛到全局最优.为此提出了一个将人体部件分割算法嵌入到粒子滤波框架的人体运动跟踪系统.首先使用Condensation算法传播并评估粒子,然后利用基于期望最大化的部件分割算法迭代更新粒子.在迭代过程中,从采样粒子推导的姿态用于部件分割,分割结果用于确定粒子分布,使粒子逐渐接近高似然区域,从而提高找到全局最优的概率并降低采样粒子数.在HumanEva-Ⅱ数据库上的测试结果表明了文中系统的有效性,且对比实验结果也优于Condensation算法和退火粒子滤波.     

自适应粒子滤波在人体跟踪过程中的应用

人体运动跟踪技术近年来在图像处理与计算机视觉领域引起很多关注,在当前一些重要研究和应用领域有着广泛的需求。在以往跟踪方法的基础上提出了基于决策规则的自适应粒子滤波的无标记运动目标跟踪方法。利用一个带外观模板的人体关节模型,通过学习得到运动模型及基于关节模型的相似性计算,巧妙地利用自适应粒子滤波对运动目标进行实时跟踪,使得在粒子滤波过程中,可以根据实际滤波情况在线调节粒子数。实验表明,提出的算法鲁棒性好,跟踪速度比基于传统粒子滤波的快。     

基于sift特征匹配的人体上半身三维运动跟踪

研究人体姿态与视频优化跟踪问题,单目视频缺少深度信息,使得单目视频的人体运动跟踪难以实现三维姿态恢复问题。为解决上述问题,提出了一种利用sift特征尺度不变性的优点进行人体上半身三维运动跟踪的算法。在跟踪过程中先计算初始匹配sift特征点对,然后反复迭代出除误匹配点,消除误差,最后求解由两个匹配sift特征组成的方程组得到胸部关节的位姿,根据人体骨骼模型采用深度遍历依次恢复其它关节的姿态。实验结果表明,系统能够对人体上半身运动进行比较准确的三维运动跟踪。     

Multi-Sensory Motion Estimation and Control of an Autonomous Quadrotor

In this paper, a fully autonomous quadrotor in a heterogeneous air–ground multi-robot system is established only using minimal on-board sensors: a monocular camera and inertial measurement units (IMUs). Efficient pose and motion estimation is proposed and optimized. A continuous-discrete extended Kalman filter is applied, in which the high-frequency IMU data drive the prediction, while the estimates are corrected by the accurate and steady vision data. A high-frequency fusion at 100 Hz is achieved. Moreover, time delay analysis and data synchronizations are conducted to further improve the pose/motion estimation of the quadrotor. The complete on-board implementation of sensor data processing and control algorithms reduces the influence of data transfer time delay, enables autonomous task accomplishment and extends the work space. Higher pose estimation accuracy and smaller control errors compared to the standard works are achieved in real-time hovering and tracking experiments.     

基于多传感器的运动姿态测量算法

在基于MEMS传感技术的运动姿态测量中, 陀螺仪信号的漂移和载体线性加速度与重力加速度的叠加是影响测量结果准确性的主要原因, 实践中一般采用静态补偿和滤波技术减小测量误差. 基于自主研发的惯性测量单元, 设计了一种新型两级扩展卡尔曼滤波器: 基于四元数的运动姿态测量模型, 首先构造自适应加速度误差协方差矩阵, 消除载体线性加速度, 再采用多传感器融合技术进行数据融合, 修正陀螺仪信号漂移产生的误差. 实验表明, 本文算法结果与业界认可的动作捕捉系统Xsens的测量结果一致, 可有效满足应用需求.     

基于预估测量值的EKF在手臂测姿中的应用

针对载体线性加速度以及周围局部磁干扰对姿态测量精度的影响,基于已有的惯性测量单元,设计了一个基于四元数的实时估计手臂姿态的扩展卡尔曼滤波器(EKF)。提出利用四元数引入加速计和磁强计的预估测量值构造自适应测量噪声协方差阵的方法,结合QUEST算法,来判定姿态角解算对陀螺仪、加速计和磁强计输出信息的依赖程度,以此来提高测量精度。文末通过实验仿真对该方法进行了验证,并对实验结果和电磁跟踪系统采集到的数据进行了比较,结果表明,本文提出的方法能显著提高手臂姿态测量精度,可有效满足应用要求。     

基于运动传感器的远程健康监护系统研究

摘要设计了一种基于惯性测量单元((IMU Inertial Measurement Unit)的远程健康监护系统。首先采集固定在人体上的4个惯性测量单元的加速度、地磁和角速度数据,然后对数据采用卡尔曼滤波器进行融合,最后将融合后的姿态数据写入I3VH文件后传输到远程医护人员的计算机,以便医护人员对病人进行监护。实验结果表明,系统能实时准确地采集到人体的姿态数据,并能实时地再现人体的动作。     

Sensor fusion algorithms for orientation tracking via magnetic and inertial measurement units: An experimental comparison survey

Lightweight and low-cost wearable magnetic and inertial measurement units (MIMUs) have found numerous applications, such as aerial vehicle navigation or human motion analysis, where the 3D orientation tracking of a rigid body is of interest. However, due to the errors in measurements of gyroscope, accelerometer, and/or magnetometer inside a MIMU, numerous studies have proposed sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by performing an experimental comparison among a variety of SFAs. Notably, we compared the estimated orientation of 36 SFAs from the complementary filter and linear/extended/complementary/unscented/cubature Kalman filter families with the reference orientation obtained from a camera motion-capture system. The experimental study included data collection with a foot-worn MIMU where nine participants performed various short- and long-duration tasks. We shared the codes and sample of data in https://www.ncbl.ualberta.ca/codes to enable other researchers to compare their works with the literature toward creating a comprehensive online repository for SFAs. To perform a fair comparison, we used the Particle Swarm Optimization routine to find the optimal adaptive gain tuning scheme for each SFAs, as recommended in the literature. Our experimental results showed that gyroscope static bias removal, in general, showed to be effective in reducing the estimation error of SFAs, specifically during long-duration trials. Moreover, our experimental results identified the SFAs with the highest accuracy from each family. We also reported the execution times for the selected SFAs from each family. This paper is among the first experimental comparison studies which provide such breadth of coverage across various SFAs for tracking orientation with MIMUs.     

一种移动人体轮廓的两步提取法

移动人体轮廓提取算法作为移动人体跟踪的基础,在交通监控、客流量统计、运动分析、虚拟现实等领域都有很高的实用价值.本文根据灰度图像的背景分布与Sohel算子,提出一种结合自适应灰度阈值分割与人体梯度信息判定的两步移动人体轮廓提取算法.实验表明,该算法具有很强的实用性与鲁棒性.     

40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods,lessons learned,and future challenges

Technological developments over the past two decades have resulted in the development of more accurate and lightweight low-cost magnetic and inertial measurement units (MIMUs). These developments have allowed the extensive application of MIMUs in various fields, specifically tracking the 3D orientation of a rigid body. Despite recent technological improvements, measurements from a tri-axial gyroscope, accelerometer, and/or magnetometer inside the MIMU are characterized by uncertainties. Numerous studies have been conducted to address these uncertainties and develop sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by providing a survey of the state-of-the-art SFAs for orientation estimation. We surveyed +250 publications, categorized the SFAs with various structures, identified the modifications proposed to improve their performance, and discussed the strengths and weaknesses of these approaches. We found that, while early SFAs were mostly a vector observation algorithm or an extended Kalman filter, to improve the computational efficiency, more recent works have developed SFAs with a complementary filter or complementary Kalman filter structure. At the same time, to improve the performance of the SFAs, several research teams have proposed various modifications to the basic structure of these filters, such as adaptive gain tuning or imperfect measurement rejection. We also provided an outlook on the lessons learned as well as the main challenges related to SFAs and discussed the practical steps toward developing an effective SFA. We have identified the need for benchmarking studies as the main challenge at the moment. This paper is among the first surveys which provide such breadth of coverage across different SFAs for tracking orientation with MIMUs.