A basic study for a robotic transfer aid system based on human motion analysis

A metal hydride (MH) actuator uses the reversible reaction between heat energy and mechanical energy of a hydrogen-absorbing alloy and moves smoothly without any noise. Moreover, the actuator is compact, light and has adequate compliance. In this paper, some experiments concerning human motion patterns were carried out in order to adopt the MH actuator to the transfer aid system. The results were evaluated from a biomedical engineering viewpoint in order to obtain the optimal parameters for transferring the disabled from a bed to a wheelchair and vice versa (transfer aid). Sagittal plane kinematics, surface electromyographic signals (EMG) obtained from lower limbs, ground reaction forces and foot pressure distribution were measured using a three-dimensional motion analyzer. From the experimental results, it was found that the motion of the transfer aid was smooth when the initial angle of the ankle was 70°. The best initial angle of the trunk was also found to be about 45° while standing from a bed. However, the initial angle of the trunk was best at 60° when a person was in the process of sitting down on a wheelchair. Furthermore, a knee pad which supports the lower limbs requires flexibility in the range of 0.1-0.2 kgf/mm.     

Matching and mismatching between the pedagogical design principles of a math game and the actual practices of play

The article reports and discusses a long‐term qualitative study of forty 8–10‐year‐old students who regularly played a math game during math lessons for 9 weeks. The goal was to explore the relations between (i) some of the pedagogical principles that underlie the game and (ii) the playing practice in terms of what actually takes place when students play the math game during regular math lessons. The article discusses indications of matches and mismatches between pedagogical principles and playing practice as they appear in analyses of observations and video recordings. The result highlights the difficulty of predicting areas in which possible mismatches appear between the intentions of the pedagogues and designers of educational technology and the actual use of the technology by the students. This also applies to educational materials that have already been pilot tested and used on a smaller scale. We emphasize the need to observe actual use for extensive periods of time, i.e. to go beyond short‐time user testing.     

基于Kinect的体感康复游戏系统设计

传统的康复训练因理疗师和设备紧缺、训练场地有限、娱乐性差等问题,存在着很大的局限性。构建了基于Kinect与Unity3D的体感康复游戏系统,主要特色包括:虚拟人物与玩家运动同步;通过引导轨迹实现运行引导反馈、卡通目标点设定算法实现游戏的不同难度等。测试结果表明,系统具有很好的实时性、交互性和娱乐性,对类似体感游戏开发有较好的参考价值。     

A hypermedia system to aid teacher understanding of pupil preconceptions

The paper reports on the initial development of a hypermedia environment aimed at teachers. The system uses constructivist learning principles to discover the preconceptions held by teachers about fundamental electrical principles. The interface is designed to be as transparent and non‐threatening as possible, thus allowing the users to concentrate on content. The system which was prototyped in SuperCard has generated positive feedback from the eventual end users.     

Biographies of scientists and public understanding of science

In referring to biographies of Edison as examples, the following are shown: the image of a scientist or an engineer in biographies has dramatically changed over time; the images produced anew in each period fitted well to the social milieu of the day; biographies therefore acquired a large readership and contributed to informing to the public of the value of science and technology and the necessity of promoting them. It is also pointed out that a new image of scientist or engineer is to be created which is surely attractive to people today if we expect biographies to remain effective in promoting public understanding of science as before.A preliminary version of this paper was read in July 1997 at the International Congress of the History of Science (Liège, Belgium), and in March 1998 at the International Conference on Science, Technology and Society (Tokyo, Japan). I am grateful to those who provided helpful comments at the sessions, especially to V. V. Krishna and J. Fudano.     

Learning and motivational impacts of a multimedia science game

The power of a web-based forensic science game to teach content and motivate STEM careers was tested among secondary students. More than 700 secondary school students were exposed to one of the three web-based forensic cases for approximately 60 min. Gain scores from pre-test to a delayed post-test indicated significant gains in content knowledge. In addition, the game’s usability ratings were a strong predictor of learning. A positive relationship between role-play experience and science career motivation was observed, which suggests a role for authentic virtual experiences in inspiring students to consider STEM careers.     

Fast human activity recognition based on structure and motion

We present a method for the recognition of human activities. The proposed approach is based on the construction of a set of templates for each activity as well as on the measurement of the motion in each activity. Templates are designed so that they capture the structural and motion information that is most discriminative among activities. The direct motion measurements capture the amount of translational motion in each activity. The two features are fused at the recognition stage. Recognition is achieved in two steps by calculating the similarity between the templates and motion features of the test and reference activities. The proposed methodology is experimentally assessed and is shown to yield excellent performance.     

Graph interpretation practices of science and education majors

Abstract

Studies of scientists show that graphs and graphing are central to the processes and products of science. It does not surprise us that recent reform documents propose the development of competency in using and reading graphs as an important aspect of learning science and about science. Reform documents propose that (a) public school science classrooms have students engage in hands‐on inquiry practices, in which they summarize investigative activities using graphs, and (b) education students take science courses instructed by science department faculty members. Past research has demonstrated that pre‐service secondary teachers do not, on their own, default to the graphical summarizing and interpretive practices of scientists. We examined the graphical analysis practices of pre‐service elementary teachers and science graduates on two graphing tasks and concluded that there were few differences between those two groups in their interpretations. We propose a model to understand the differences between these groups and more experienced groups of graph interpreters. We conclude that taking the lecture‐oriented science courses common to science programs does not necessarily improve education students’ competencies at summarizing data or interpreting graphs and that they need to engage in scaffolded, research‐oriented activities themselves.     

Recognition of human gestures and behaviour based on motion trajectories

Human activities are characterised by the spatio-temporal structure of their motion patterns. Such structures can be represented as temporal trajectories in a high-dimensional feature space of closely correlated measurements of visual observations. Models of such temporal structures need to account for the probabilistic and uncertain nature of motion patterns, their non-linear temporal scaling and ambiguities in temporal segmentation. In this paper, we address such problems by introducing a statistical dynamic framework to model and recognise human activities based on learning prior and continuous propagation of density models of behaviour patterns. Prior is learned from example sequences using hidden Markov models and density models are augmented by current visual observations.     

Spatio-temporal structure of human motion primitives and its application to motion prediction

This paper proposes a novel approach to structuring behavioral knowledge based on symbolization of human whole body motions, hierarchical classification of the motions, and extraction of the causality among the motions. The motion patterns are encoded into parameters of corresponding Hidden Markov Models (HMMs), where each HMM abstracts the dynamics of motion pattern, and hereafter is referred to as “motion symbol”. The motion symbols allow motion recognition and synthesis. The motion symbols are organized into a hierarchical tree structure representing the property of spatial similarity among the motion patterns, and this tree is referred to as “motion symbol tree”. Seamless motion is segmented into a sequence of motion primitives, each of which is classified as a motion symbol based on the motion symbol tree. The seamless motion results in a sequence of the motion symbols, which is stochastically represented as transitions between the motion symbols by an N-gram model. The motion symbol N-gram model is referred to as “motion symbol graph”. The motion symbol graph extracts the temporal causality among the human behaviors. The integration of the motion symbol tree and the motion symbol graph makes it possible to recognize motion patterns fast and predict human behavior during observation. The experiments on a motion dataset of radio calisthenics and on a large motion dataset provided by CMU motion database validate the proposed framework.     

基于人体运动规律的运动融合技术

介绍了一种不需要人工干预的运动融合方法,提出了基于关节运动规律的运动周期判别方法。通过分析运动捕获数据,计算双膝与臀部节点连线夹角的变化来确定运动周期,然后再进行时空变形、插值与约束重建,从而生成高质量的运动融合动画。实验结果表明本算法能够准确的计算出运动周期,并且使约束后的融合动作更加真实。     

A hybrid dynamic motion prediction method for multibody digital human models based on a motion database and motion knowledge

In this paper, we present a novel method to predict human motion, seeking to combine the advantages of both data-based and knowledge-based motion prediction methods. Our method relies on a database of captured motions for reference and introduces knowledge in the prediction in the form of a motion control law, which is followed while resembling the actually performed reference motion. The prediction is carried out by solving an optimization problem in which the following conditions are imposed to the motion: must fulfill the goals of the task; resemble the reference motion selected from the database; follow a knowledge-based dynamic motion control law; and ensure the dynamic equilibrium of the human model, considering its interactions with the environment. In this work, we apply the proposed method to a database of clutch pedal depression motions, and we present the results for three predictions. The method is validated by comparing the results of the prediction to motions actually performed in similar conditions. The predicted motions closely resemble the motions in the validation database and no significant differences have been noted either in the motion’s kinematics or in the motion’s dynamics.     

Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking

We present a method to reconstruct human motion pose from uncalibrated monocular video sequences based on the morphing appearance model matching. The human pose estimation is made by integrated human joint tracking with pose reconstruction in depth-first order. Firstly, the Euler angles of joint are estimated by inverse kinematics based on human skeleton constrain. Then, the coordinates of pixels in the body segments in the scene are determined by forward kinematics, by projecting these pixels in the scene onto the image plane under the assumption of perspective projection to obtain the region of morphing appearance model in the image. Finally, the human motion pose can be reconstructed by histogram matching. The experimental results show that this method can obtain favorable reconstruction results on a number of complex human motion sequences.     

Real-time humanoid whole-body remote control framework for imitating human motion based on kinematic mapping and motion constraints

In this paper, we propose a whole-body remote control framework that enables a robot to imitate human motion efficiently. The framework is divided into kinematic mapping and quadratic programming based whole-body inverse kinematics. In the kinematic mapping, the human motion obtained through a data acquisition device is transformed into a reference motion that is suitable for the robot to follow. To address differences in the kinematic configuration and dynamic properties of the robot and human, quadratic programming is used to calculate the joint angles of the robot considering self-collision, joint limits, and dynamic stability. To address dynamic stability, we use constraints based on the divergent component of motion and zero moment point in the linear inverted pendulum model. Simulation using Choreonoid and a locomotion experiment using the HUBO2+ demonstrate the performance of the proposed framework. The proposed framework has the potential to reduce the preview time or offline task computation time found in previous approaches and hence improve the similarity of human and robot motion while maintaining stability.     

The human motion database: A cognitive and parametric sampling of human motion

Motion databases have a strong potential to guide progress in the field of machine recognition and motion-based animation. Existing databases either have a very loose structure that does not sample the domain according to any controlled methodology or too few action samples which limit their potential to quantitatively evaluate the performance of motion-based techniques. The controlled sampling of the motor domain in the database may lead investigators to identify the fundamental difficulties of motion cognition problems and allow the addressing of these issues in a more objective way. In this paper, we describe the construction of our Human Motion Database using controlled sampling methods (parametric and cognitive sampling) to obtain the structure necessary for the quantitative evaluation of several motion-based research problems. The Human Motion Database is organized into several components: the praxicon dataset, the cross-validation dataset, the generalization dataset, the compositionality dataset, and the interaction dataset. The main contributions of this paper include (1) a survey of human motion databases describing data sources related to motion synthesis and analysis problems, (2) a sampling methodology that takes advantage of a systematic controlled capture, denoted as cognitive sampling and parametric sampling, and (3) a novel structured motion database organized into several datasets addressing a number of aspects in the motion domain.     

基于CPG的上肢周期性运动控制的研究

依据Ken等人所提供的曲柄转动机构中人体手臂运动的实验及其力学模型,根据互抑制神经振荡器原理和EMG信号的特点,构造了上肢屈肌和伸肌的中枢模式发生器（CPG）模型,由CPG的输出建立上肢运动的六肌模型,并得到所需要的驱动转矩.根据上肢运动的最小转矩原理,通过遗传算法优化CPG的参数.仿真结果表明：基于CPG模型基础上的上肢动力学模型所得到的计算结果与实验数据有很好的一致性.     

基于加速度传感器的前臂运动姿态检测

人体前臂在运动过程中始终和竖直方向（或水平方向）成一定的角度关系。通过固定于前臂上的三轴加速度传感器ADXL330的各个轴与重力方向（竖直向下）之间的夹角就可以检测出人体前臂的运动姿态。为了验证该方法的可行性,采集传感器在不同角度时的输出,运用Matlab处理采集到的数据,处理结果和实际角度的相对误差小于l％,绝对误差小于4°;并将此方法用于检测人体前臂运动,其结果与高速摄像系统采集到的图像分析结果进行比较,其绝对误差小于5％。实验结果表明：在一定的误差允许范围内,ADXL330可以用于前臂运动姿态的检测。