Quantitative analysis of the morphing wing mechanism of raptors:IMMU-based motion capture system and its application on gestures of a Falco peregrinus

This paper presented a novel tinny motion capture system for measuring bird posture based on inertial and magnetic measurement units that are made up of micromachined gyroscopes, accelerometers, and magnetometers. Multiple quaternion-based extended Kalman filters were implemented to estimate the absolute orientations to achieve high accuracy.Under the guidance of ornithology experts, the extending/contracting motions and flapping cycles were recorded using the developed motion capture system, and the orientation of each bone was also analyzed. The captured flapping gesture of the Falco peregrinus is crucial to the motion database of raptors as well as the bionic design.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Real-Time Human Body Motion Capturing System

This paper proposes a human body motion capturing system using the depth images. It consists of three processes to estimate the human pose parameters. First, we develop a pixel-based body part classifier to segment the human silhouette into different body part sub-regions and extract the primary joints. Second, we convert the distribution of the joints to the feature vector and apply the regression forest to estimate human pose parameters. Third, we apply the temporal constraints mechanism to find the best human pose parameter with the minimum estimation error. In experiments, we show that our system can operate in real-time with sufficient accuracy.

基于虚拟演播室的Motion Capture研究

在介绍虚拟演播室的基础上,分析了Motion Capture的技术和应用,提出了一种基于虚拟演播室的系统实现,并探讨了其在节目制作中的应用。

Differences between lower extremity joint running kinetics captured by marker-based and markerless systems were speed dependent

Background:The development of computer vision technology has enabled the use of markerless movement tracking for biomechanical analysis.Recent research has reported the feasibility of markerless systems in motion analysis but has yet to fully explore their utility for capturing faster movements,such as running.Applied studies using markerless systems in clinical and sports settings are still lacking.Thus,the present study compared running biomechanics estimated by marker-based and markerless systems.Given running speed not only affects sports performance but is also associated with clinical injury prevention,diagnosis,and rehabilitation,we aimed to investigate the effects of speed on the comparison of estimated lower extremity joint moments and powers between markerless and marker-based technologies during treadmill running as a concurrent validating study.Methods:Kinematic data from marker-based/markerless technologies were collected,along with ground reaction force data,from 16 young adults running on an instrumented treadmill at 3 speeds:2.24 m/s,2.91 m/s,and 3.58 m/s(5.0 miles/h,6.5 miles/h,and 8.0 miles/h).Sagittal plane moments and powers of the hip,knee,and ankle were calculated by inverse dynamic methods.Time series analysis and statistical parametric mapping were used to determine system differences.Results:Compared to the marker-based system,the markerless system estimated increased lower extremity joint kinetics with faster speed during the swing phase in most cases.Conclusion:Despite the promising application of markerless technology in clinical settings,systematic markerless overestimation requires focused attention.Based on segment pose estimations,the centers of mass estimated by markerless technologies were farther away from the relevant distal joint centers,which led to greater joint moments and powers estimates by markerless vs.marker-based systems.The differences were amplified by running speed.

The Marking Technology in Motion Capture for the Complex Locomotor Behavior of Flexible Small Animals(Gekko gecko)

Animals have evolved a variety of behavior patterns to adapt to the environment. Motion-capture technology is utilized to quantify and characterize locomotor behaviors to reveal the mechanisms of animal motion. In the capture of flexible, small animals with complex locomotor behaviors, the markers interfere with each other easily, and the motion forms(bending, twisting) of the moving parts are obviously different;thus, it is a great challenge to realize accurate quantitative characterization of complex locomotor behaviors. The correlation between the marker properties, including the size and space length, and the precision of the system are revealed in this paper, and the effects of diverse marker shapes on the capturing accuracy of the captured objects in different motion forms were tested. Results showed that the precision of system is significantly improved when the ratio of the space length to the diameter of the markers is larger than four;for the capture of the spatial twisting motion of the flexible object, the hexagon markers had the lowest spatial lost-marker rate relative to the circle, triangle, and square. Customized markers were used to capture the locomotor behavior of the gecko-inspired robot(rigid connection) and the gecko(flexible connection). The results showed that this marking technology can achieve high accuracy of motion capture for geckos(the average deviation was approximately 0.32 mm, and the average deviation’s variation rate was approximately 0.96%). In this paper, the marking technology for the motion capture of flexible, small animals with complex motion is proposed;it can effectively improve the system precision as well as the capture accuracy, and realize the quantitative characterization of the complex motion of flexible, small objects. It provides a reliable technical means to deeply study the evolution of the motion function of small animals and advance systematic research of motion-capture technology.

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.

Review of techniques for motion capture data processing

In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can＇t ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.

Fast Individual Facial Animation Framework Based on Motion Capture Data

Based upon motion capture,a semi-automatic technique for fast facial animation was implemented. While capturing the facial expressions from a performer,a camera was used to record her /his front face as a texture map. The radial basis function( RBF) technique was utilized to deform a generic facial model and the texture was remapped to generate a personalized face.Partitioning the personalized face into three regions and using the captured facial expression data,the RBF and Laplacian operator,and mean-value coordinates were implemented to deform each region respectively. With shape blending,the three regions were combined together to construct the final face model. Our results show that the technique is efficient in generating realistic facial animation.

Motion Capture for Puppet Control Using Integrated Sensor System

The paper aims to execute puppet without restrictions by controling puppet using robot. We controling puppet in the same way as the present puppet, but we perform this by robot. It offers more advantages and lessen the weak points. It needs various actions and expressions because of the nature of a puppet. The biggest problem which executes this is the ways to create a system. This thesis proposes motion capture of developed method with solution of this problem. So, we create various contents needed by puppet. In this part, developed method means a mixed method on the basis of optical system and magnetic system used mainly for the present method of motion capture. We lessen the weak points of each method and propoe solution of create motion for pupct by offering more advantages. So we solve difficulties of executing puppet and probable probkans when we execute puppet by using robot. The solution of this thesis is proven by applying control of puppet.

Automatic Key-Frame Extraction from Optical Motion Capture Data

Optical motion capture is an increasingly popular animation technique. In the last few years, plenty of methods have been proposed for key-frame extraction of motion capture data, and it is a common method to extract key-frame using quaternion. Here, one main difficulty is due to the fact that previous algorithms often need to manually set various parameters. In addition, it is problematic to predefine the appropriate threshold without knowing the data content. In this paper, we present a novel adaptive threshold-based extraction method. Key-frame can be found according to quaternion distance. We propose a simple and efficient algorithm to extract key-frame from a motion sequence based on adaptive threshold. It is convenient with no need to predefine parameters to meet certain compression ratio. Experimental results of many motion captures with different traits demonstrate good performance of the proposed algorithm. Our experiments show that one can typically cut down the process of extraction from several minutes to a couple of seconds.

Silk road dance exhibition system based on motion capture

Silk Road dance is an excellent traditional Chinese dance drama, which has high artistic values. This article introduces the virtual performance techniques of Silk Road based on motion capture. First, obtain the dance data using motion capture technique according to the features of dance itself, then establish figure model, finally combines these data to make 3-D animation system to perform Silk Road, meanwhile designs some interfaces to introduce it. This system excavates details of the dance, and can be reference of man-machine interaction and animation production.

Application of Optical Motion Capture Technology in Power Safety Entitative Simulation Training System

The safety production is critical to stable development of Chinese electric power industry. With the development of electric power enterprises, the requirements of its employees are also becoming higher and higher. In this paper, an optical motion capture system based on the virtual reality technology is proposed to meet the requirements of the power enterprise for the qualified business ability. Electric power equipment, power equipment model entitative operating environment and the human model are established by electric power simulation unit, ZigBee technology and OpenGL graphics library. The problem of missing feature points is solved by applying the human model driven algorithm and the Kalman filtering algorithm. The experimental results show that it is more accurate to use Kalman filtering algorithm to extract the feature point in tracking process of actual motion capture and real-time animation display. The average absolute error of 3D coordinates is 1.61 mm and the average relative error is 2.23%. The system can improve trainees’ sense of experience and immersion.

Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

Biomechanics is the study of physiological properties of data and the measurement of human behavior.In normal conditions,behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style,body movements in walking patterns,writing style and voice tunes.One cannot perform any change in these inputs that make results reliable and increase the accuracy.The aim of our study is to perform a comparative analysis between the marker-based motion capturing system(MBMCS)and the marker-less motion capturing system(MLMCS)using the lower body joint angles of human gait patterns.In both the MLMCS and MBMCS,we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity(walk and running).Using five state of the art machine learning algorithms,we obtained 44.6%and 64.3%accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm(two angles as features).In the second set of experiments,we used six machine learning algorithms to obtain 65.9%accuracy with the k-nearest neighbor(KNN)algorithm(two angles as features)and 74.6%accuracy with an ensemble algorithm.Also,by increasing features(6 angles),we obtained higher accuracy of 99.3%in MBMCS for person recognition and 98.1%accuracy in MBMCS for activity recognition using the KNN algorithm.MBMCS is computationally expensive and if we redesign the model of OpenPose with more body joint points and employ more features,MLMCS(low-cost system)can be an effective approach for video data analysis in a person identification and activity recognition process.

Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy.A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method.Through a series of novel design concepts,including the integration of a detecting circuit and an analog-to-digital converter,a miniaturized functional electrical stimulation circuit technique,a low-power super-regeneration chip for wireless receiving,and two wearable armbands,a prototype system has been established with reduced size,power,and overall cost.Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects,the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy.Test results showed that wrist flexion/extension,hand grasp,and finger extension could be reproduced with high accuracy and low latency.This system can build a bridge of information transmission between healthy limbs and paralyzed limbs,effectively improve voluntary participation of hemiplegic patients,and elevate efficiency of rehabilitation training.

Reconfiguration of face expressions based on the discrete capture data of radial basis function interpolation

Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.

Heterogeneity induced strain localization in block-in-matrix-soils subjected to uniaxial loading using real-time CT scanning

Block-in-matrix-soils(bimsoils)are geological mixtures that have distinct structures consisting of relatively strong rock blocks and weak matrix soils.It is still a challenge to evaluate the mechanical behaviors of bimsoils because of the heterogeneity,chaotic structure,and lithological variability.As a result,only very limited laboratory studies have been reported on the evolution of their internal deformation.In this study,the deformation evolution of bimsoils under uniaxial loading is investigated using real-time X-ray computed tomography(CT)and image correlation algorithm(with a rock block percentage(RBP)of 40%).Three parameters,i.e.heterogeneity coefficient(K),correlation coefficient(CC),and standard deviation(STD)of displacement fields,are proposed to quantify the heterogeneity of the motion of the rock blocks and the progressive deformation of the bimsoils.Experimental results show that the rock blocks in bimsoils are prone to forming clusters with increasing loading,and the sliding surface goes around only one side of a cluster.Based on the movement of the rock blocks recorded by STD and CC,the progressive deformation of the bimsoils is quantitatively divided into three stages:initialization of the rotation of rock blocks,formation of rock block clusters,and formation of a shear band by rock blocks with significant rotation.Moreover,the experimental results demonstrate that the meso-motion of rock blocks controls the macroscopic mechanical properties of the samples.

Methodical Approach to Integrate Human Movement Diversity in Real-Time into a Virtual Test Field for Highly Automated Vehicle Systems

Recently, virtual realities and simulations play important roles in the development of automated driving functionalities. By an appropriate abstraction, they help to design, investigate and communicate real traffic scenario complexity. Especially, for edge cases investigations of interactions between vulnerable road users (VRU) and highly automated driving functions, valid virtual models are essential for the quality of results. The aim of this study is to measure, process and integrate real human movement behaviour into a virtual test environment for highly automated vehicle functionalities. The overall system consists of a georeferenced virtual city model and a vehicle dynamics model, including probabilistic sensor descriptions. By motion capture hardware, real humanoid behaviour is applied to a virtual human avatar in the test environment. Through retargeting methods, which enable the independency of avatar and person under test (PuT) dimensions, the virtual avatar diversity is increased. To verify the biomechanical behaviour of the virtual avatars, a qualitative study is performed, which funds on a representative movement sequence. The results confirm the functionality of the used methodology and enable PuT independence control of the virtual avatars in real-time.

A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.

基于三维运动捕捉技术构建蒙医震脑术三维虚拟仿真平台的视觉呈现

背景:基于三维运动捕捉技术可以构建精准的、客观量化的医学虚拟仿真模型,有利于临床学习者精准、深入理解和掌握各种传统疗术。目前中医虚拟仿真模型已有报道,但蒙医传统疗术的虚拟仿真模型尚未见报道。目的:构建一种基于三维运动捕捉技术的交互式三维可视化的蒙医震脑术虚拟仿真模型。方法:使用Motion Analysis三维光学运动捕捉系统和足底测力台采集蒙医专家的运动捕捉数据,在Motion Builder软件中构建震脑术三维动作模型,使用Maya软件构建角色模型并与动作模型相匹配,运动Unity3D软件构建蒙医震脑术虚拟仿真系统,系统整合蒙医震脑术操作3D动画、运动学和动力学参数信息。结果与结论:通过三维运动捕捉技术和计算机仿真重现蒙医震脑术操作,能够显示施术者和受试者的运动姿态,记录关节运动的关键空间位置参数与变化情况,得出运动过程中的运动学、动力学参数。运用交互式三维虚拟仿真技术,实现蒙医震脑术的三维虚拟仿真的视觉呈现,为蒙医震脑术手法的标准化、数字化、可视化研究奠定基础。