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MEMS的可穿戴无线体域网设计与实现

针对高精度低成本可穿戴式人体动作捕捉,采用MEMS微惯性传感单元(IMU)设计实现无线体域网系统.系统由六轴微惯性单元MPU-6050、三轴地磁仪AK8975、微处理器及射频模块CC2530构成.首先,详细讨论系统硬件构架和基于Z-Stack的软件构架,然后通过上位机Matlab用户程序进行数据采集与实验验证.实验结果表明,与其他微惯性传感器系统相比,本设计采用高集成度的芯片和无线通信方式,在减小硬件体积的同时能够获得较高精度的数据(角度传感器零漂＜1.5°),对实现可穿戴无线惯性测量有着积极的作用.     

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.     

手机内置加速度传感器数据的空间坐标转换算法

手机内置加速度传感器坐标系固定于设备自身,其采集的数据因手机姿态的改变而不断发生漂移,受此影响即使同一运动过程,加速度数据也难以同前一个时刻保持一致。为解决该问题,提出利用空间坐标转换算法将加速度数据从手机坐标系映射至惯性坐标系,从而确保数据在手机任意姿态下均能准确反映实际的运动状态。为验证该方法的有效性,设计一种手机传感器数据在线采集与实时处理新方法,实现Matlab中数据动态特征的实时观测及算法性能的在线评估。利用此方法,在旋转实验中分别测试方向余弦与四元数两种算法的可行性,并在计步器实验中进一步测试四元数算法性能。实验结果表明,基于方向传感器数据的方向余弦算法因测量范围限制,不能实现全方位空间坐标转换;而基于旋转矢量传感器数据的四元数算法则能够实现全方位转换,且转换后的加速度对步态识别率达到95%以上,较准确地反映了实际运动状态。     

遥操作护理机器人系统的操作者姿态解算方法研究

设计了一种遥操作护理机器人系统,为实现从端同构式机器人的随动运动控制,对主端操作者人体姿态解算方法进行了研究.首先,构建由惯性传感单元构成的动作捕捉系统,对用作从端机器人动作指令的操作者人体姿态信息进行采集,采用四元数法对人体运动原始数据进行初步求解.其次,将四元数法得到的姿态数据解算成依据仿人结构设计的护理机器人各关节运动的目标姿态角,实现人体姿态到机器人动作的同构性映射.最后,为验证本文所提姿态解算方法的性能,设计了操作者控制护理机器人完成递送和拿取药瓶动作的实验.结果表明,本文姿态解算方法的解算性能与参考系统基本相同;在操作者动作姿态快速变化的时间段,系统仍可获得较高精度的目标姿态数据,其误差在动态条件下依旧能保持在2%以下;护理机器人可较好地实时复现操作者的人体动作.本文方法能满足机器人进行一般护理作业时对人体姿态数据处理的快速性和准确性要求.     

Sensor data fusion for body state estimation in a hexapod robot with dynamical gaits

We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot's body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences.     

基于MEMS 传感器和Unity3D 的人体运动捕获系统

随着人机交互技术的发展,人与计算机之间自然、多模态交互将成为人与计算机 之间交互的主要方式,而这首先需要计算机可以正确地理解和捕捉人的行为特征,运动捕获技 术正是在这种背景下提出来。通过运动捕获技术,计算机可以理解人体动作,用户就可以通过 体态、方位、手势和表情等模态向计算机发出指令、传达信息等,因此运动捕获是新一代人机 交互中的关键技术之一。目前基于MEMS 惯性传感器的动作捕获研究主要针对动漫和电影制 作,价格昂贵;随着传感器的集成度变高,价格逐渐降低带来了新的系统设计需求。在原有动 作捕获技术基础上,设计并实现一种普适性更好的人体动作捕获系统原型,原型系统实现了基 于惯性传感节点的人体运动信息的采集与融合、节点与汇聚节点的数据传输、虚拟人体模型实 时动作呈现程序;实现了从数据测量到采集,再到模拟呈现的全过程。     

What is the effective key length for a block cipher: an attack on every practical block cipher

Recently,several important block ciphers are considered to be broken by the brute-force-like cryptanalysis,with a time complexity faster than the exhaustive key search by going over the entire key space but performing less than a full encryption for each possible key.Motivated by this observation,we describe a meetin-the-middle attack that can always be successfully mounted against any practical block ciphers with success probability one.The data complexity of this attack is the smallest according to the unicity distance.The time complexity can be written as 2k(1-),where>0 for all practical block ciphers.Previously,the security bound that is commonly accepted is the length k of the given master key.From our result we point out that actually this k-bit security is always overestimated and can never be reached because of the inevitable loss of the key bits.No amount of clever design can prevent it,but increments of the number of rounds can reduce this key loss as much as possible.We give more insight into the problem of the upper bound of effective key bits in block ciphers,and show a more accurate bound.A suggestion about the relationship between the key size and block size is given.That is,when the number of rounds is fixed,it is better to take a key size equal to the block size.Also,effective key bits of many well-known block ciphers are calculated and analyzed,which also confirms their lower security margins than thought before.The results in this article motivate us to reconsider the real complexity that a valid attack should compare to.     

Motion Synthesis for Sports Using Unobtrusive Lightweight Body‐Worn and Environment Sensing

The ability to accurately achieve performance capture of athlete motion during competitive play in near real‐time promises to revolutionize not only broadcast sports graphics visualization and commentary, but also potentially performance analysis, sports medicine, fantasy sports and wagering. In this paper, we present a highly portable, non‐intrusive approach for synthesizing human athlete motion in competitive game‐play with lightweight instrumentation of both the athlete and field of play. Our data‐driven puppetry technique relies on a pre‐captured database of short segments of motion capture data to construct a motion graph augmented with interpolated motions and speed variations. An athlete's performed motion is synthesized by finding a related action sequence through the motion graph using a sparse set of measurements from the performance, acquired from both worn inertial and global location sensors. We demonstrate the efficacy of our approach in a challenging application scenario, with a high‐performance tennis athlete wearing one or more lightweight body‐worn accelerometers and a single overhead camera providing the athlete's global position and orientation data. However, the approach is flexible in both the number and variety of input sensor data used. The technique can also be adopted for searching a motion graph efficiently in linear time in alternative applications.     

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。     

On‐line real‐time physics‐based predictive motion control with balance recovery

In this paper, we present an on‐line real‐time physics‐based approach to motion control with contact repositioning based on a low‐dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on‐line user request by transforming an example motion extracted from a motion library. Guided by the reference motion, it repeatedly generates an optimal control policy for a small time window one at a time for a sequence of partially overlapping windows, each covering a couple of footsteps of the reference motion, which supports an on‐line performance. On top of this, our system dynamics and problem formulation allow to derive closed‐form derivative functions by exploiting the low‐dimensional dynamics model together with example motion data. These derivative functions and their sparse structures facilitate a real‐time performance. Our approach also allows contact foot repositioning so as to robustly respond to an external perturbation or an environmental change as well as to perform locomotion tasks such as stepping on stones effectively.     

Action recognition from only somatosensory information using spectral learning in a hidden Markov model

Human action classification is fundamental technology for robots that have to interpret a human’s intended actions and make appropriate responses, as they will have to do if they are to be integrated into our daily lives. Improved measurement of human motion, using an optical motion capture system or a depth sensor, allows robots to recognize human actions from superficial motion data, such as camera images containing human actions or positions of human bodies. But existing technology for motion recognition does not handle the contact force that always exists between the human and the environment that the human is acting upon. More specifically, humans perform feasible actions by controlling not only their posture but also the contact forces. Furthermore these contact forces require appropriate muscle tensions in the full body. These muscle tensions or activities are expected to be useful for robots observing human actions to estimate the human’s somatosensory states and consequently understand the intended action. This paper proposes a novel approach to classifying human actions using only the activities of all the muscles in the human body. Continuous spatio-temporal data of the activity of an individual muscle is encoded into a discrete hidden Markov model (HMM), and the set of HMMs for all the muscles forms a classifier for the specific action. Our classifiers were tested on muscle activities estimated from captured human motions, electromyography data, and reaction forces. The results demonstrate their superiority over commonly used HMM-based classifiers.     

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under‐constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.     

Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

In this work, we present a novel approach for calibrating material model parameters for soft body simulations using real data. We use a fully differentiable pipeline, combining a differentiable soft body simulator and differentiable depth rendering, which permits fast gradient-based optimizations. Our method requires no data pre-processing, and minimal experimental set-up, as we directly minimize the L2-norm between raw LIDAR scans and rendered simulation states. In essence, we provide the first marker-free approach for calibrating a soft-body simulator to match observed real-world deformations. Our approach is inexpensive as it solely requires a consumer-level LIDAR sensor compared to acquiring a professional marker-based motion capture system. We investigate the effects of different material parameterizations and evaluate convergence for parameter optimization in both single and multi-material scenarios of varying complexity. Finally, we show that our set-up can be extended to optimize for dynamic behaviour as well.     

基于移动感知的智能手机防盗软件的研究

得益于硬件技术的发展,智能手机感知信息的方式日益丰富。因而以智能手机自带的传感器件为出发点,以移动感知为基础,设计并开发了一款智能手机防盗软件。该软件针对智能手机在生活中的不同使用场景,利用手机自带的加速度传感器、环境光传感器、接近传感器以及手机内部的广播机制,通过分析从周围环境获取的实时数据来判断手机状态是否变化,从而实现不同的防盗模式。其中在通过加速度传感器输出值判断手机状态时,设计了三轴加速度从手机坐标系到参考坐标系的四元数转换算法,以利于客观统一地判断手机的运动状态。此外,通过手机内部短信广播的截获技术来判断设备是否收到短信以及短信内容是否为预设指令,实现对手机的远程控制。目前Android智能手机的市场占有率高达80%,因而以Android手机为例,实现了上述智能手机防盗软件。各项功能经过真机测试,均已达到预期效果。     

基于足迹采样的运动编辑

针对人体两足动画提出一种基于足迹采样的运动编辑算法．足迹很好地描述了两足动画中必须满足的时空约束，通过调整足迹的位置与朝向来编辑两足动画是一种较为自然、直观的交互方式．为有效、快速地生成编辑后的动画，采用一种实时的逆向运动学算法求解两足动画中的支撑脚约束，然后使用层次B样条技术构造偏移映射完成编辑．为了便于逆向运动学算法的求解，提出基于采样的方法来计算质心轨迹．     

人体6 自由度运动参数无线测试系统BTViewer 的设计与开发

为解决测试人体在三维空间中的关节运动参数,自主设计了人体六自由度运动参 数测试系统BTViewer。基于惯性传感器跟踪技术,设计了整体的系统平台工作流程,同时完成 了相关算法。为降低误差、减少漂移现象,本实验采用集成的三轴加速度计、三轴磁力计的模 块LSM303DLH 以及三轴陀螺仪动态采集样本数据,通过无线装置传输到电脑,有效合理地对 采集到的数据进行预处理,将原始数据互补滤波之后解析成实际数据,准确地得到人体六自由 度运动参数。最后,运用Qt 集成的OpenGL 等模块进行曲线、三维等可视化显示。实验表明, 该系统的设计达到了预期的要求,能够较好地跟踪人体6 自由度运动参数。     

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.     

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     

网络化手势运动跟踪系统设计

设计了一种基于微机电惯性传感器的数据手套.根据惯性导航和刚体动力学原理,构建了基于微型传感器的体感网络,并通过多传感器数据的融合解算以获取运动姿态信息,实现对手指各关节运动数据的捕获.结合计算机图形技术构建三维虚拟手捕获系统进行性能对比评估,实验结果表明:所设计的系统具有良好的稳定性和适应性,能够对手运动信息进行实时有效地捕获.