Detection of human respiration based on measurement of current generated by electrostatic induction

In this study, we developed an effective technique for measuring human respiration using a noncontact and nonattached electrode. The technique requires the measurement of the current generated due to the difference in capacitance between a given electrode and the human body. The subpicoampere electrostatic induction current flowing through the electrode when placed a few centimeters from the subject is detected. We propose an occurrence model for the electrostatic induction current generated by the change in capacitance caused by the movement of the body surface while taking a breath. This model effectively describes the behavior of the current flowing through the measurement electrode.     

基于静电信号的人体动作识别

提出一种通过检测人体行为动作产生的静电信号进行人体动作识别的方法.在分析人体荷电特性的基础上,设计静电信号检测系统采集被测人员的5种典型动作（行走、踏步、坐下、拿取物品、挥手）的静电感应信号.对采集的5种动作的静电信号进行特征参量提取和显著性差异分析,优化用于分类的特征参数.基于Weka平台使用3种分类算法（支持向量机、决策树C4.5和随机森林）分别对采集到的250组样本数据通过10折交叉验证进行了分类识别,结果显示随机森林算法的识别效果最好,正确率可达99.6%.研究表明本文提出的单人环境下基于人体静电信号的动作分类识别方法能够有效地对典型人体动作进行识别.     

An efficient control over human running animation with extension of planar hopper model

The most important goal of character animation is to efficiently control the motions of a character. Until now, many techniques have been proposed for human gait animation. Some techniques have been created to control the emotions in gaits such as ‘tired walking’ and ‘brisk walking’ by using parameter interpolation or motion data mapping. Since it is very difficult to automate the control over the emotion of a motion, the emotions of a character model have been generated by creative animators. This paper proposes a human running model based on a one‐legged planar hopper with a self‐balancing mechanism. The proposed technique exploits genetic programming to optimize movement and can be easily adapted to various character models. We extend the energy minimization technique to generate various motions in accordance with emotional specifications. Copyright © 1999 John Wiley & Sons, Ltd.     

基于主动式静电探测技术的目标定位方法

针对被动式静电探测技术无法对不带电目标进行探测的问题,提出了一种基于主动式静电探测技术的目标定位方法。在对带电和不带电目标分析的基础上,得到了相应的通过检测电流实现目标定位的探测方程,并在Maxwell 3D中进行了仿真。仿真结果表明:基于该方法的探测距离与目标的带电量、主动静电场的强弱以及电流检测电路的灵敏度有关。     

A statistical wisp model and pseudophysical approaches for interactive hairstyle generation

This work presents an interactive technique that produces static hairstyles by generating individual hair strands of the desired shape and color, subject to the presence of gravity and collisions. A variety of hairstyles can be generated by adjusting the wisp parameters, while the deformation is solved efficiently, accounting for the effects of gravity and collisions. Wisps are generated employing statistical approaches. As for hair deformation, we propose a method which is based on physical simulation concepts, but is simplified to efficiently solve the static shape of hair. On top of the statistical wisp model and the deformation solver, a constraint-based styler is proposed to model artificial features that oppose the natural flow of hair under gravity and hair elasticity, such as a hairpin. Our technique spans a wider range of human hairstyles than previously proposed methods and the styles generated by this technique are fairly realistic.     

A mathematical framework to study fast walking of human

The body of a walking human is an elaborated dynamic system that operates adaptively in various conditions such as fast walking. Due to dynamic redundancies, the individual motor control strategies for speeding up the walking can be different among normal subjects. However, in reality, we see that the pattern of motion is quite similar among people and it is only the profile of hip joint motion along its path which determines the speed. The objective of the current paper is to develop a mathematical framework to investigate time optimal motion of a human during walking. To this end, a nine-link planar biped model is used. The motion is considered to take place in sagittal plane and to follow a normal pattern of motion. The solution is obtained using a phase plane method to solve minimum time problem which is subjected to inequality constraints of variable maximum joint torques and stability conditions. The solution method can be used to find the maximum possible speed of a human with specific body characteristics and to obtain a hip joint trajectory which could produce that speed. The proposed method can be utilized to study quantitative effect of different parameters such as joint strength in fast walking.     

Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.     

Human walking animation based on foot reaction force in the three‐dimensional virtual world

This paper introduces a method that can generate continuous human walking motion automatically on an arbitrary path in a three‐dimensional (3D) modelled scene. The method is based on a physical approach that solves the boundary value problem. In the motion generation stage, natural‐looking walking motion, which includes plane walking, walking upstairs and downstairs and walking on a curved path, is created by applying dynamics and kinematics. The human body is approximated as a simple rigid skeleton model, and dynamic motion is created based on the ground reaction force of the human foot. To adapt to the 3D environment, the 3D walking path is divided into steps which are tagged with the parameters needed for motion generation, and step‐by‐step motion is connected end‐to‐end. Additional features include fast calculation and a reduced need for user control. The proposed method can produce interesting human motion and can create realistic computer animation scenes. Copyright © 2000 John Wiley & Sons, Ltd.     

面向离散地形的欠驱动双足机器人平衡控制方法

欠驱动双足机器人在行走中为保持自身的平衡,双脚需要不间断运动.但在仅有特定立足点的离散地形上很难实现调整后的落脚点,从而导致欠驱动双足机器人在复杂环境中的适应能力下降.提出了基于虚拟约束(Virtual constraint,VC)的变步长调节与控制方法,根据欠驱动双足机器人当前状态与参考落脚点设计了非时变尺度缩放因子,能够实时重构适应当前环境的步态轨迹;同时构建了全身动力学模型,采用反馈线性化的模型预测控制(Model predictive control,MPC)滚动优化产生力矩控制量,实现准确的轨迹跟踪控制.最终进行了欠驱动双足机器人的随机离散地形稳定行走的仿真实验,验证了所提方法的有效性与鲁棒性.     

Generating 1-DOF limit cycle walking at target walking speed by feed-forward and feedback limit cycle control

We propose the feed-forward and feedback (FaF) control systems for generating the limit cycle walking at a target walking speed by the combined rimless wheel (CRW) model. The proposed FaF control systems can calculate a control input constantly based on the mathematical analysis of the current and target walking states. As a result, first, the limit cycle walking at the target walking speed is generated by numerical simulations when the walker is driven by the constant FaF control system. Second, for controlling the convergence speed, we extend the FaF control to the two-period stepwise control systems. The limit cycle walking at the target walking speed is still generated, and the convergence speed is controlled by the settling time parameter. Finally, the real-time walking state updating is considered in the FaF control systems to handle the disturbances. In this case, we find that for generating a target walking speed, a precise mathematical model is not necessary for generating the target state, but can make the control input stable and efficient.     

面向反馈运动控制器的多目标求解

目的 基于物理模拟的人体运动生成方法由于能够合成符合自然规律的运动片段,可实时响应环境的变化,且生成的物理运动不是机械性的重复,因此是近年来计算机动画和虚拟现实领域中最活跃的研究方向之一。然而人体物理模型具有高维、非线性及关节间强耦合性等特点,求解人体物理运动十分困难。反馈控制器常用于人体物理运动控制,求解时通常需要对多个目标函数加权求和,然而权重的设置需多次试验,烦杂耗时。针对运动控制器求解困难的问题,本文提出了一种面向反馈运动控制器的多目标求解方法。方法 首先,对运动数据进行预处理并提取关键帧求解初始控制器,并设计一种改进的反馈控制机制;在此基础上,种群父代个体变异产生子代,采用禁选区域预筛选策略去除不满足约束的个体,并通过重采样获取新解;然后,通过物理仿真获得多目标适应度值,采用区域密度多层取优选取分布均匀的优秀个体作为下一代父代,并通过基于剪枝的多阶段物理求解算法决定是否进入下一阶段优化;经过多次迭代后获得物理控制器,从而生成具有反馈的人体物理运动。结果 针对提出的方法,本文针对多个测试函数和物理运动分别进行实验：在测试函数实验中,本文分别采用经典的测试函数进行实验对比,在相同的迭代次数下,相比之前算法,本文算法中满足约束的优秀个体命中率更高,反转世距离更小,且最优解集的分布更加均匀;物理运动生成实验中,分别针对走路、跑步和翻滚等运动进行物理运动生成,与之前算法进行对比,本文算法可以更早地完成收敛,同时目标函数值更小,表明生成的运动效果更好。结论 本文提出的进化求解方法可以生成不同运动的控制器,该控制器不仅可以生成物理运动,而且还具备外力干扰下保持平衡的能力,解决了运动控制器求解中多目标权重设置困难、优化时间长的问题;除此之外,本文算法还对具有约束的多目标问题具有较好的求解效果。     

人体步行规律与仿人机器人步态规划

从仿生学角度分析了人体的步行运动规律,提出了一种基于人体运动规律的仿人机器人步态参数设定方法.首先对人体步行运动数据进行捕捉并分析,得出人体各步态参数间的函数关系,以人体步行相似性作为评价指标,提出仿人机器人步态参数的设定方法.其次,通过分析人体在步行过程中的补偿支撑脚偏航力矩的基本原理,提出了基于双臂及腰关节协调运动的仿人机器人偏航力矩补偿算法,以提高仿人机器人行走的稳定性.最后通过仿真及实验验证了所提出的步态规划方法的正确性及有效性.     

Quantifying the physical intensity of construction workers,a mechanical energy approach

Construction workers typically undertake highly demanding physical tasks involving various types of stresses from awkward postures, using excessive force, highly repetitive actions, and excessive energy expenditure, which increases the likelihood of unsafe actions, productivity loss, and human errors. Biomechanical models have been developed to estimate joint loadings, which can help avoid strenuous physical exertion, potentially enhancing construction workforce productivity, safety, and well-being. However, the models used are mainly in 2D, or to predict static strength ignored their velocity and acceleration or using marker-based method for dynamic motion data collection. To address this issue, this paper proposes a novel framework for investigating the mechanical energy expenditure (MEE) of workers using a 3D biomechanical model based on computer vision-based techniques. Human 3D Pose Estimation algorithm based on 2D videos is applied to approximate the coordinates of human joints for working postures, and smart insoles are used to collect foot pressures and plantar accelerations, as input data for the biomechanical analyses. The results show a detailed MEE rate for the whole body, at which joints the maximum and minimum values were obtained to avoid excessive physical exertion. The proposed method can approximate the total daily MEE of construction tasks by summing the assumed cost of individual tasks (such as walking, lifting, and stooping), providing suggestions for the design of a daily workload that workers can sustain without developing cumulative fatigue.     

Real‐time Locomotion Controller using an Inverted‐Pendulum‐based Abstract Model

In this paper, we propose a novel motion controller for the online generation of natural character locomotion that adapts to new situations such as changing user control or applying external forces. This controller continuously estimates the next footstep while walking and running, and automatically switches the stepping strategy based on situational changes. To develop the controller, we devise a new physical model called an inverted‐pendulum‐based abstract model (IPAM). The proposed abstract model represents high‐dimensional character motions, inheriting the naturalness of captured motions by estimating the appropriate footstep location, speed and switching time at every frame. The estimation is achieved by a deep learning based regressor that extracts important features in captured motions. To validate the proposed controller, we train the model using captured motions of a human stopping, walking, and running in a limited space. Then, the motion controller generates human‐like locomotion with continuously varying speeds, transitions between walking and running, and collision response strategies in a cluttered space in real time.     

Harnessing social dynamics through persuasive technology to promote healthier lifestyle

In light of current calls by medical professionals to confront the global issue of obesity and related illnesses, we developed a mobile application called STEP UP that monitors physical activity and provides data that can be easily shared within a social network. We then conducted an exploratory, theoretical study based on the theory of reasoned action (TRA) followed by an experimental trial and user study. The purpose of the studies was to explore the effect of persuasive technology on physical activity behavior and to investigate its effectiveness in motivating users to use the technology to be more physically active. The application was found to have a positive effect on the participants and their level of physical activity. They enjoyed using the application and were motivated to walk more, especially when enabled to share their step counts with their friends. The social component of the application clearly enhanced users’ walking experience, as the atmosphere of friendly competition motivated them to walk more. Based on user responses, we conclude that a further enhanced application that includes chat functionality may be even more successful in supporting increased physical activity and thus healthier lifestyle.     

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette–Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.     

A measurement-based approach for designing reduced-order controllers with guaranteed bounded error

The objective of this paper is to present a measurement-based control-design approach for single-input single-output linear systems with guaranteed bounded error. A wide range of control-design approaches available in the literature are based on parametric models. These models can be obtained analytically using physical laws or via system identification using a set of measured data. However, due to the complex properties of real systems, an identified model is only an approximation of the plant based on simplifying assumptions. Thus, the controller designed based on a simplified model can seriously degrade the closed-loop performance of the system. In this paper, an alternative approach is proposed to develop fixed-order controllers based on measured data without the need for model identification. The proposed control technique is based on computing a suitable set of fixed-order controller parameters for which the closed-loop frequency response fits a desired frequency response that meets the desired closed-loop performance specifications. The control-design problem is formulated as a nonlinear programming problem using the concept of bounded error. The main advantages of our proposed approach are: (1) it guarantees that the error between the computed and the desired frequency responses is less than a small value; (2) the difficulty of finding the globally optimal solution in the error minimisation problem is avoided; (3) the controller can be designed without the use of any analytical model to avoid errors associated with the identification process; and (4) low-order controllers can be designed by selecting a fixed low-order controller structure. To experimentally validate and illustrate the efficacy of the proposed approach, proportional-integral measurement-based controllers are designed for a DC (direct current) servomotor.     

一种基于多分类器融合的人体运动行为识别模型

为了提高基于智能设备的人体日常行为识别的准确率,针对不同智能设备内置加速度传感器获取的三轴加速度信息,提出了一种基于多分类器融合的行为识别MCF(Multiple Classifier Fusion)模型。针对5种日常行为(静止、散步、跑步、上楼及下楼),优选出与每种行为相关度高的特征集,用于训练对每种行为识别效果最佳的5个基分类器,并采用一个融合器对5个基分类器的输出进行融合处理,得到最终行为识别结果。该模型对这5种行为的平均识别准确率和可信度分别达到96.84%和97.41%,能有效进行用户行为识别。     

基于相对形变模型及正则化技术的人体运动估计

为了使根据人体行走的单目动态图象序列，对人体手臂及腿部的运动及结构参数进行估计的结果更为可信、更具鲁棒性，提出了一种基于相对形变模型及正则化技术的人体运动估计方法，该方法首先在物体中心坐标的运动表示方式下，通过在刚体运动模型中加入形变系数的方法给出了基于相对形变概念的非刚体运动模型；然后，根据这一非刚体运动模型进行正则化运动及结构参数的估计，再以正则化的形式融入人体运动的先验知识，使运动估计的结果更具鲁棒性，实验结果证明，该方法有效地反映了人体的非刚体运动模式，运动模型中所加入的相对形变系数也一定程度反映了人体的运动规律。