一个人体运动的数学模型

本文采用L-E法和铰分解法研究了人体运动的数学模型问题,给出了一种人体运动的规范建模方法。该方法适用于多种拓扑结构形式的人体运动（如空翻、步行、滑行等）,所得方程具有适合于计算机程式求解的特点。     

汽车碰撞中的人体动力学仿真

将多则体系统动力学中的Ｌ－Ｅ法与经典碰撞理论相结合,推导了树形系统下的Ｌ－Ｅ碰撞动力学方程,并运用面向对象的编程方法开发出汽车碰撞中人体动力学仿真软件,成功地仿真出汽车碰撞后人体运动响应．     

基于AnyBodyTM技术的人体运动建模方法

人体运动的建模与仿真是当今运动生物力学研究的一个热点.利用数值模型研究人体的运动规律,是人体运动研究的一个重要手段和有效工具.其关键技术在于应用逆向运动学方法求解人体运动,并获取人体运动中各个肌肉力学上技术参数.文中主要探讨基于AnyBodyTM System软件人体运动仿真的建模方法来研究人体运动力学规律,结合The AnyBodyTM system对人体运动具体应用,说明The AnyBodyTM system技术在人体运动仿真领域的优势.     

肠道菌群:运动健康促进的新靶点

肠道菌群与人体通过广泛信息交流形成互惠共生关系。肠道菌群结构稳定性、菌种多样性和微生态平衡性使其成为人体更易接触和调控的"生理中心",深刻影响人体健康。适宜运动可通过优化肠道菌群,促进宿主肠道微生态健康。以肠道菌群为靶点的运动健康促进研究,为运动与人体健康和疾病研究呈现出新领域。基于宏基因组学、宏转录组学等微生态研究技术,揭示与人体疾病相关的肠道菌群失调,鉴定出疾病相关的特定菌群种类及功能,使得以肠道菌群为靶点的运动精准干预人体健康成为可能。     

幼时饮食关系成年后肥胖倾向

近日,加拿大卡尔加里大学人体运动机能学学院的瑞利．拉瑞蒙博士的研究报告指出：幼儿时期的食谱将直接关系到成年后是否会出现肥胖倾向。     

便携式上肢运动协调性检测系统设计与实现

目的:研究人体上肢协调性运动的相关指标,以此来检测儿童先天性发育不良以及预防老年人的协调性相关疾病等问题。方法:本文设计了一种上肢协调性运动检测系统,检测被试的上肢运动状态,研究其协调性运动指标。本系统由运动感知单元、PC机端的控制台以及协调性运动指标数据分析组成。结果:利用该系统检测被试的运动协调性,给出了被试的准确性运动指标0.23以及协同性运动指标0.18。结论:经试验该系统对检测上肢的运动协调性有帮助,且方便携带与操作。     

运动生物力学的计算机仿真研究

本文叙述运动生物力学计算仿真研究的原理和方法。介绍已经专家鉴定的人体腾空运动仿真软件系统的总体设计，对该系统已实现数学模型。三维影片解析技术，人体三维消隐藏线的动静态显示以及运动动作的定量分析和设计技术作了较详细的说明，文章最后给出了利用该仿真软件系统研究背跃式跳高过杆动作的实例，具体说明仿真研究的过程和对运动科学训练的意义。     

高温、颗粒物污染环境与运动促进健康

特殊环境对人体健康形成了挑战,如何在特殊环境保证人体健康具有较为重要的意义。本文针对高温和颗粒物污染两种特殊环境,对其与运动促进健康的国内外研究动态进行综述。运动和高温是胰岛素抵抗非药物干预治疗的主流,PGC-1α是机体通过运动、高温获得诸多益处的关键调控因子,新型激素Irisin可能是运动和高温改善胰岛素抵抗内在机理的重要环节。颗粒物暴露对运动机体运动能力和呼吸系统有一定的负面影响,但系统运动可以抵抗颗粒物染污负面作用,自由基和炎症途径关系系统运动阻碍颗粒物染污的可能机制。     

人体心功能状态综合评价的实验研究

本文介绍了系统地检查和评阶人体心功能的方法。本方法是以多个无创性心功能指标、动态的功能性负荷刺激和数学分析判断方法,对心功能作综合性评价。前先进行安静状态下的检查与评定,排除明显的心脏疾病,并初步评价心功能。其次进行运动负荷的检查与评定,发现早期心脏疾病,挑选优良的泵血功能者。笫三步进行下身负压或低氧运动双负荷的检查与评定,达到对心脏疾病挖潜和选拔调节功能优良者。最后,为了人体心功能的择优,采用运动(或者低氧运动)和下身负压二种检查的联合评价。本方法能显露人体心功能状态的储备和调节能力的优劣,达到发现早期疾病、提高诊断率,择优选拔人体心功能的目的。     

初级纤毛的结构与功能及其在运动系统疾病中的研究进展

纤毛是由微管组成,存在于大部分细胞表面呈头发状结构的细胞器。根据纤毛是否运动,可以将其分为初级纤毛和动纤毛(多纤毛)。动纤毛常分布于大脑中央水管上皮、气道上皮、生殖系统的输卵管上皮组织等处。初级纤毛则分布于其余的大部分组织器官的细胞内,例如肾小管上皮细胞、各骨细胞或者软骨细胞、椎间盘细胞等。初级纤毛被认为是细胞把外界信号转导到细胞内的机械信号或者化学感受器和多种信号通路转导的中心。运动系统是由骨、软骨、关节、肌腱等组织组成,具有运动、支持、保护功能的人体主要承受力学的系统。因此,作为人体机械信号感受器的初级纤毛被认为与人体运动系统正常生理功能的维持密切相关。参与纤毛形成的基因突变可导致纤毛缺失,从而引起运动系统的多组织器官异常。同时,人们也发现在骨性关节炎、椎间盘退变、脊柱侧弯等许多常见的运动系统疾病中存在初级纤毛异常。因此,深入研究初级纤毛在运动系统组织器官生理功能维持及与疾病的关系有助于运动系统疾病的治疗。该文对初级纤毛与运动系统疾病的研究进展进行综述,指出了纤毛与运动系统疾病的最新进展及重点与难点,为运动系统疾病的发病机制研究提供理论参考。     

A learning-based markerless approach for full-body kinematics estimation in-natura from a single image

We present a supervised machine learning approach for markerless estimation of human full-body kinematics for a cyclist from an unconstrained colour image. This approach is motivated by the limitations of existing marker-based approaches restricted by infrastructure, environmental conditions, and obtrusive markers. By using a discriminatively learned mixture-of-parts model, we construct a probabilistic tree representation to model the configuration and appearance of human body joints. During the learning stage, a Structured Support Vector Machine (SSVM) learns body parts appearance and spatial relations. In the testing stage, the learned models are employed to recover body pose via searching in a test image over a pyramid structure. We focus on the movement modality of cycling to demonstrate the efficacy of our approach. In natura estimation of cycling kinematics using images is challenging because of human interaction with a bicycle causing frequent occlusions. We make no assumptions in relation to the kinematic constraints of the model, nor the appearance of the scene. Our technique finds multiple quality hypotheses for the pose. We evaluate the precision of our method on two new datasets using loss functions. Our method achieves a score of 91.1 and 69.3 on mean Probability of Correct Keypoint (PCK) measure and 88.7 and 66.1 on the Average Precision of Keypoints (APK) measure for the frontal and sagittal datasets respectively. We conclude that our method opens new vistas to robust user-interaction free estimation of full body kinematics, a prerequisite to motion analysis.     

Isomap transform for segmenting human body shapes

Segmentation of the 3D human body is a very challenging problem in applications exploiting volume capture data. Direct clustering in the Euclidean space is usually complex or even unsolvable. This paper presents an original method based on the Isomap (isometric feature mapping) transform of the volume data-set. The 3D articulated posture is mapped by Isomap in the pose of Da Vinci's Vitruvian man. The limbs are unrolled from each other and separated from the trunk and pelvis, and the topology of the human body shape is recovered. In such a configuration, Hoshen–Kopelman clustering applied to concentric spherical shells is used to automatically group points into the labelled principal curves. Shepard interpolation is utilised to back-map points of the principal curves into the original volume space. The experimental results performed on many different postures have proved the validity of the proposed method. Reliability of less than 2 cm and 3° in the location of the joint centres and direction axes of rotations has been obtained, respectively, which qualifies this procedure as a potential tool for markerless motion analysis.     

人──板系统最佳蹬伸动作的控制模型及数值分析

本文基于Hanavan模型和人体测量学参数,以人体各环节间的相对运动作为控制量,建立了人-板系统中起跳蹬伸动作的数学模型,给出了模型的数值计算方法,在此基础上给出了实现最佳蹬伸用力过程的计算实验途径与运动技术诊断方法。     

Analysis of Maneuvering Flight of an Insect

Wing motion of a dragonfly in the maneuvering flight, which was measured by Wang et al. was investigated. Equations of motion for a maneuvering flight of an insect were derived. These equations were applied for analyzing the maneuvering flight. Inertial forces and moments acting on a body and wings were estimated by using these equations and the measured motions of the body and the wings. The results indicated the following characteristics of this flight: ( 1 ) The phase difference in flapping motion between the two fore wings and two hind wings, and the phase difference between the flapping motion and the feathering motion of the four wings are equal to those in a steady forward flight with the maximum efficiency. (2)The camber change and the feathering motion were mainly controlled by muscles at the wing bases.     

Development of a computational model for astronaut reorientation

The ability to model astronaut reorientations computationally provides a simple way to develop and study human motion control strategies. Since the cost of experimenting in microgravity is high, and underwater training can lead to motions inappropriate for microgravity, these techniques allow for motions to be developed and well-understood prior to any microgravity exposure. By including a model of the current space suit, we have the ability to study both intravehicular and extravehicular activities. We present several techniques for rotating about the axes of the body and show that motions performed by the legs create a greater net rotation than those performed by the arms. Adding a space suit to the motions was seen to increase the resistance torque and limit the available range of motion. While rotations about the body axes can be performed in the current space suit, the resulting motions generated a reduced rotation when compared to the unsuited configuration.     

Self versus Environment Motion in Postural Control

To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results.     

A novel approach to predicting human ingress motion using an artificial neural network

Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal parameters. For example, the optimal design of the door panel of an automobile can be obtained for a human operator to conduct the easiest ingress and egress motion. However, acquiring motion data from existing methods provides only unrealistic motion or requires a great amount of time. This not only leads to an increased time consumption for a product development, but also causes inefficiency of the overall design process. To solve such problems, this research proposes an algorithm to rapidly and accurately predict full-body human motion using an artificial neural network (ANN) and a motion database, as the design parameters are varied. To achieve this goal, this study refers to the processes behind human motor learning procedures. According to the previous research, human generate new motion based on past motion experience when they encounter new environments. Based on this principle, we constructed a motion capture database. To construct the database, motion capture experiments were performed in various environments using an optical motion capture system. To generate full-body human motion using this data, a generalized regression neural network (GRNN) was used. The proposed algorithm not only guarantees rapid and accurate results but also overcomes the ambiguity of the human motion objective function, which has been pointed out as a limitation of optimization-based research. Statistical criteria were utilized to confirm the similarity between the generated motion and actual human motion. Our research provides the basis for a rapid motion prediction algorithm that can include a variety of environmental variables. This research contributes to an increase in the usability of digital human models, and it can be applied to various research fields.     

Direct evidence for encoding of motion streaks in human visual cortex

Temporal integration in the visual system causes fast-moving objects to generate static, oriented traces (‘motion streaks’), which could be used to help judge direction of motion. While human psychophysics and single-unit studies in non-human primates are consistent with this hypothesis, direct neural evidence from the human cortex is still lacking. First, we provide psychophysical evidence that faster and slower motions are processed by distinct neural mechanisms: faster motion raised human perceptual thresholds for static orientations parallel to the direction of motion, whereas slower motion raised thresholds for orthogonal orientations. We then used functional magnetic resonance imaging to measure brain activity while human observers viewed either fast (‘streaky’) or slow random dot stimuli moving in different directions, or corresponding static-oriented stimuli. We found that local spatial patterns of brain activity in early retinotopic visual cortex reliably distinguished between static orientations. Critically, a multivariate pattern classifier trained on brain activity evoked by these static stimuli could then successfully distinguish the direction of fast (‘streaky’) but not slow motion. Thus, signals encoding static-oriented streak information are present in human early visual cortex when viewing fast motion. These experiments show that motion streaks are present in the human visual system for faster motion.