一种基于多传感器的步态相位实时检测系统

对人体步态相位进行准确实时的判断是智能假肢等康复机器人控制的基础,提出并建立了一种实时状态相位检测系统.该系统包含了4只压力传感器和2只姿态传感器,能够准确地区分出平地行走时足跟着地、足放平、足跟离地、足尖离地、摆动弯曲以及摆动伸展等五种状态相位,实验表明:该系统能够适应不同测试者的不同步幅和步速,并能够应用在假肢的实时控制中.     

基于MPSO-BP神经网络方法的人体步态识别

为提高人体下肢步态相位识别准确率以实现外骨骼机器人控制,采用一种改进的粒子群优化MPSO-BP神经网络方法识别不同运动模式下的人体步态相位。通过自适应调整学习因子构造MPSO-BP神经网络分类器,以多种传感信息组成的特征向量样本集训练神经网络分类器,用于识别人体下肢在平地行走、上楼梯和起坐三种典型运动模式下的步态相位。实验结果表明,MPSO-BP神经网络分类器能有效识别三种不同运动模式的步态相位,识别准确率均达到96%以上,识别性能优于传统的BP神经网络模型和粒子群优化神经网络模型。     

传感器通道中的运动模式的步态识别算法

提出一种基于运动模式的步态识别算法,应用于轨道交通自动售检票系统的由闸机构成的传感器通道中,对行走者和物体进行识别.算法是在检测人体步态的XYT模型的基础上进行改进,用红外传感器替代摄像头采集个体的通行信息,将连续状态的XYT模型改进成离散状态的步态识别算法,通过定义一些符合人体步态特征的特殊的复合模式,即步态模式来区分人与物体.实验证明,该算法用于闸机的智能识别系统是有效的.     

面向外骨骼机器人人机交互的步态数据获取系统及识别模型

外骨骼机器人人机交互是当前的研究热点,通常需要获取人体相关运动信息作为控制信号源。为了采集人体步态数据,研究了生理信号与关节运动之间的关联机制,设计了一种步态数据获取系统,其利用鞋内薄膜压力传感器和关节角度传感器组成测试设备,成功采集了15组健康男子在3km/h、4km/h和5km/h 3种速率下自然行走的步态数据。提出采用基因表达式编程建立膝关节运动识别模型,并使用所采集的步态数据进行训练和验证。结果显示,利用此模型可有效进行关节运动的识别和预测,验证了本系统作为外骨骼人机接口的可行性。     

基于Tsfresh-RF特征提取的人体步态识别算法

惯性传感器(IMU)由于尺寸小、价格低、精度高以及信息实时性强等优点, 在人体运动信息的获取与控制等方面得到广泛应用, 但在步态识别的时间序列特征提取和步态环境数据等方面还存在着明显的局限. 本文针对人体下肢步态识别特征提取的复杂性及适用性差等问题, 提出基于Tsfresh-RF特征提取的人体步态识别新方法. 首先, 利用IMU获取的人体步态数据集, 构建基于Tsfresh时间序列特征提取和随机森林(RF)的人体步态识别算法模型. 其次, 采用该算法对人体不同传感器位置进行实验, 完成爬梯、行走、转弯等9种人体运动步态的识别. 最后, 实验结果表明所提方法平均分类准确率达到91.0％, 显著高于传统的支持向量机(SVM)与朴素贝叶斯(NB)等方法的识别结果. 此外, 本文所提基于Tsfresh-RF特征提取的人体步态识别算法具有很好的鲁棒性, 将为后续下肢外骨骼机器人的控制提供有利依据.     

基于足底压力与支持向量机的步态相位识别研究

随着两足机器人、人工假肢技术以及为行走困难病人康复设计的康复训练机器人的发展,在线的步态相位识别方法越来越重要。本文提出的基于足底压力与支持向量机(SVM)的步态相位识别算法主要由五部分组成,即数据采集、数据预处理、特征提取、训练分类器和分类识别。实验表明：该方法能够对运动中的步态相位进行准确的判断。     

步态相位识别技术在人工腿设计中的应用研究

对国内首个智能仿生人工腿原型机CIP-I Leg的基本结构及工作原理进行简要的介绍,接着介绍了智能人工腿摆动相模糊控制系统的整体设计方案;文中对健康腿的步态从单足和双足两个角度进行分析,提出基于人工腿的步态识别方案,并详细介绍了人工腿步态识别的具体实现方法;步态信息通过膝关节处的霍尔传感器和脚底的压力传感器获取,并给出了霍尔传感器和压力传感器的信号的硬件处理电路及软件流程图;实验结果表明,系统能够可靠地检测出步态相位信息,并取得了预期的效果.     

仿生双足机器人步态轨迹自适应控制方法研究

仿生双足机器人的步态控制具有高阶、高耦合,以及不完整约束等复杂特征,为了使其行走性能更接近人体,基于模型分析提出了一种步态轨迹自适应控制方法.首先在人体步态变化对质心投影位置与质心角动量影响的基础上,引入倒立摆建立步态模型来模拟人体行走状态,同时推导出质心投影点位置与速度公式.然后为了实现步态轨迹自适应控制,对影响步态性能的稳定性、速度调整、能量消耗三个因素设计了相应的目标约束.在目标约束条件下,利用参考轨迹对机器人的步态控制参数进行自适应整定.并根据周期间的相位差异进行偏差调整,从而抑制步态控制的误差.最后通过仿真模拟仿生双足机器人的行走过程,证明提出的步态轨迹自适应控制方法能够合理有效的控制能量消耗和稳定调节,具有良好的步态平衡性能与抗扰动性能.     

三肢体机器人足部力传感器研究

针对一种三肢体机器人的足部三维力/力矩传感器,设计了其信号采集调理电路.对传感器进行静态标定,以提高其测量精度.最后通过结合机器人在各种步态下的移动实验,检测传感器系统的工作性能.实验结果表明:传感器具有较高的测量精度,对分析并控制机器人的运动步态起到了重要作用.     

兼具安全性与柔顺性的步行康复训练机器人交互方法

提高弱机能群体使用助行机器人的柔顺性和安全性是人机交互领域重要的研究内容。本文提出一种能够兼顾柔顺性、安全性与便捷性的步行康复训练机器人非接触式柔顺控制方法。首先，分析自主研制的步行康复训练机器人结构、功能及工作模式。为准确识别使用者步态意图，研发了一种多通道近距离传感器，并结合机器人的机械结构，搭建了机载内嵌式双腿动态意图识别系统，实现了对双腿步态的实时检测。然后，为提高交互柔顺性，提出一种改进粒子滤波算法，解决了传统粒子滤波算法多次迭代过程中粒子退化问题。最后，通过引入步态速度补偿，设计了具有紧密跟踪人体步态功能的柔顺化控制方法并进行综合性实验。实验表明，本文提出的方法在确保人机交互安全性的基础上，可精确且柔顺地控制机器人的运动状态，从而紧密地跟随人体步态。该非接触式控制方法可以应用于具有类似结构的助行机器人，以及弱机能群体的日常助行与步行康复训练。     

虚拟仪器环境下光纤位移测试系统

介绍了基于专用虚拟仪器开发工具LabVIEW，利用数据采集卡，开发光纤位移测试系统。对系统硬件平台构成和软件设计方法分别进行了阐述，并进行了光纤传感器静态标定。利用该虚拟仪器测试系统，可实现信号的实时采集、分析和处理。实际测试表明，系统简化了操作步骤，提高了测量精度，具有性能稳定、可靠，操作方便等特点。     

Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles

The use of neural computing for gait analysis widely known as computational intelligent gait analysis is addressed recently. This research work reports multilayer feed-forward neural networks for walking gait pattern identification using multi-sensor data fusion; electromyography (EMG) signals and soft tissue deformation analysis using successive frames of video sequence extracted from lower limb muscles according to each gait phase within the considered gait cycle. Neural computing framework for walking gait pattern identification consists of system hardware and intelligent system software. System hardware comprises a wireless surface EMG sensor unit and two video cameras for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network for classifying the gait patterns of subjects during walking. The system uses root mean square and soft tissue deformation parameter as the input features. Multilayer feed-forward back propagation neural networks (FFBPNNs) with different network training functions were designed, and their classification results were compared. The intelligent gait analysis system validation has been carried out for a group of healthy and injured subjects. The results demonstrated that the overall accuracy of 98 % prediction is achieved for gait patterns classification established by multi-sensor data fusion of lower limb muscles using FFBPNN with Levenberg–Marquardt training function resulting better performance over FFBPNN with other training functions.

     

Modelling the motion of a sensor attached to a walking person

This paper describes an implemented system to measure and track the motion of a sensor which is attached to a person or walking robot. The complex gait motion undergone by a structure-measuring sensor during walking introduces significant uncertainty into the measurements it takes. By measuring and tracking the sensor’s motion, more meaningful information can be obtained from the measurements, and predictions can be made about the future observed position of important objects in the world. The use of motion measuring devices is discussed and compared to the possible estimation of motion by the structural sensor itself. Analysis of walker motion is performed through the use of Gabor wavelets and the Extended Kalman Filter. The method tracks and predicts motions reliably while the walker walks in a straight path or turns corners. Particular attention is focussed on the case where the structural sensor is computer vision, and the benefit that a motion model can provide with it.     

A Gait Phase Measurement System Using Treadmill Motor Current

The article describes the development of a gait phase time-based split-belt treadmill measurement system. Conventional methods of measuring gait phase, such as the foot switch and force plate, require significant preparation and are costly. In this article, we propose a simple, cheap, and accurate gait phase measurement system that utilizes only the treadmill motor current value. Comparison of this algorithm with conventional methods reveals that the proposed algorithm is as accurate as the foot switch. Moreover, the proposed algorithm can estimate stance phase within a 0.2?s error of the measured value of the force plate in most cases (four out of five healthy subjects). This accuracy is higher than that of the foot switch which is widely used in the clinical field.     

Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors

This paper presents a method to regenerate lower limb joint angle trajectories during gait cycle by judging human intention using wearable sensor system. Myoelectric signals from user are used to detect the intention of gait initiation and gait phases. Multi-channel redundant fusion technique is implemented to obtain a robust stride time and gait phase calculation algorithm. Joint trajectories corresponding to particular gait events and phases are regenerated using a Radial basis neural network. The network is trained with joint angle data measured by Inertial Measurement Unit (IMU) from users with varying anthropomorphic features. Generated trajectory is adaptive to anthropomorphic as well as gait velocity variation. Contribution of this paper is in development of a wearable sensor system, multi-channel redundant fusion to calculate stride time and an adaptive gait trajectory generation algorithm. The proposed method of trajectory generation is used to regenerate lower limb joint motion in sagittal plane for wearable robotic devices like prosthesis and active lower limb exoskeleton.     

Prediction of gait speed from plantar pressure using artificial neural networks

The goal of this study was to predict gait speed over the entire cycle in reference to plantar pressure data acquired by means of the insole-type plantar pressure measuring device (Novel Pedar-x system). To predict gait speed, the artificial neural network is adopted to develop the model to predict gait speed in the stance phase (Model I) and the model to predict gait speed in the swing phase (Model II). The predicted gait speeds were validated with actual values measured using a motion capturing system (VICON 460 system) through a five-fold cross-validation method, and the correlation coefficients (R) for the gait speed were 0.963 for normal walking, 0.978 for slow walking, and 0.950 for fast walking. The method proposed in this study is expected to be widely used clinically in understanding the progress and clarifying the cause of such diseases as Parkinsonism, strike, diabetes, etc. It is expected that the method suggested in this study will be the basis for the establishment of a new research method for pathologic gait evaluation.     

一种基于CCD和DSP的纤度仪的研制

介绍了一种纤度测量的新方法。基于此研制了在线纤度仪,它以电荷耦合器件CCD为纤维直径的图像传感元件,以数字信号处理器DSP为图像处理器。这种仪器可简化纤度测量过程,还具有成本低、体积小、重量轻、测量迅速、非介入生产、不干扰生产工艺、可用性好的特点。该装置已经成功运行在涤纶短纤的在线检测中。     

A Polymer-Based Flexible Tactile Sensor for Both Normal and Shear Load Detections and Its Application for Robotics

This paper proposes and demonstrates a novel flexible tactile sensor for both normal and shear load detections. For the realization of the sensor, polyimide and polydimethylsiloxane are used as a substrate, which makes it flexible. Thin metal strain gauges, which are incorporated into the polymer, are used for measuring normal and shear loads. The salient feature of this tactile sensor is that it has no diaphragm-like structures. The unit tactile cell characteristics are evaluated against normal and shear loads. The fabricated tactile sensor can measure normal loads of up to 4 N, and the sensor output signals are saturated against loads of more than 4 N. Shear loads can be detected by different voltage drops in strain gauges. The device has no fragile structures; therefore, it can be used as a ground reaction force (GRF) sensor for balance control in humanoid robots. Four tactile unit sensors are assembled and placed in the four corners of the robots sole. By increasing bump dimensions, the tactile unit sensor can measure loads of up to 2 kgf. When loads are exerted on the sole, the GRF can be measured by these four sensors. The measured forces can be used in the balance control of biped locomotion systems.     

基于隐马尔可夫模型的助行器依赖人群步态相位识别

步态相位识别为老年人步态异常变化监测、跌倒风险预测和康复训练评估等提供了一种方便有效的方法.将激光测距仪安装在助行器上来获取双腿的运动信息,以无须穿戴、活动范围自由的方式对助行器依赖人群进行步态相位识别.针对使用激光传感器识别腿部时衣物因素对腿部数据段分割的影响,提出基于IEPF的数据段再分割方法.为了去除身高、步速等...     

无线无源声表面波气压传感器

阐述了声表面波气压传感器的设计原理、结构及相应的遥测系统和数据采样处理系统。应用正交相位检测法,将几纳秒的时间差测量转化成较大的相位测量。采用回波幅值触发采样,有效提高采样及数据处理效率。设计的SAW气压传感器理论灵敏度可达0.7kPa,测量范围在为10～350kPa。