人体步态相位检测实验研究

人体步态分析系统对于两足机器人、康复训练机器人、人工假肢等技术的发展具有重要意义,在线的步态相位识别检测系统更为重要。基于dSPACE构建了人体步态相位检测系统,传感器采用鞋底传感器和1个光纤传感器,鞋底传感器由4只电阻式力传感器、1只位置传感器构成,用光纤传感器测量腿部的弯曲。它可以识别步行周期的5个重要状态和4个不规则状态。系统的可靠性好,能够自动识别第一个步态相位,并具有自校正功能。     

柔性喷管六自由度真实摆角测量系统研制

通过对发动机柔性喷管推力矢量控制伺服系统动态特性进行了研究,研制了一种能够真实反映柔性喷管在全轴摆动状态下的动态特性的试验系统。两组角位移传感器输出轴通过十字型构件组合在一起,正交配制,线位移传感器则和十字型构件上固定的一组角位移传感器输出轴联结在一起,组成极坐标传感器,可以准确实时获得柔性喷管在全轴摆动过程中的六自由度数据。该真摆角测量系统能够用于测量固体发动机柔性喷管空间摆角、摆心变化量及推力矢量控制伺服系统性能特性,具有广阔的应用前景和良好的经济价值。     

惯性动捕数据驱动下的智能下肢假肢运动意图识别方法

为了解决传统意图识别方法使用多模态传感器信号所带来的复杂性以及识别转换模式一般具有滞后性等问题, 本文提出了基于惯性传感器的智能下肢假肢的运动意图实时识别方法.从模式识别的角度看, 在对象空间到模式空间的转换中, 对运动模式尤其是运动转换模式进行了重定义; 在模式采集中, 采用在患侧的运动模式进行转换之前, 采集绑定在健侧的传感器于摆动相前期所产生的时序运动数据, 选择均值、方差等特征统计量和支持向量机分类器对其进行特征选择提取与特征分类的策略, 实现对残疾人运动意图准确、实时地识别.实验结果表明, 本文所提出的方法可以识别出单肢截肢患者在不同地形下的运动意图, 包括平地行走、上楼、下楼、上坡、下坡5种稳态模式, 识别率可达到97.52 %, 并且加入在5种模式之间相互转换的转换模式之后, 识别率可达到95.12 %.本文方法可以极大提高智能下肢假肢的控制性能, 实现智能假肢能根据人的运动意图在多种运动模式之间进行自然、无缝的状态切换.     

基于力敏传感器的假肢手倾斜抓握滑动检测

为了防止假肢手因受干扰而导致抓握失稳,分析并建立倾斜抓握状态下的假肢手抓握力模型,提出一种基于力敏传感器和短时傅里叶变换的滑动检测方法,对不同干扰下的滑动检测进行了实验研究。结果表明:在不同抓握角度下,运用力敏传感器和短时傅里叶变换,能够实时有效地检测出被抓握物体滑动情况,从而可以进一步实施防滑控制。     

基于无线传感器网络的危险物在途监控系统

提出了一种基于无线传感器网络的在途危险物实时动态监控系统.该系统可根据不同运输工具及危险物种类选择安装相应传感器节点并无线自主组网,通过通用分组无线业务(GPRS)实现远程无线数据访问与传输,使得控制中心能够对运输过程中危险物的状态进行全程跟踪、实时动态监控.给出了系统结构和软硬件的设计方案.实验结果表明:设计的系统测量误差较小,能够满足实际使用要求.     

基于改进模板匹配的智能下肢假肢运动意图实时识别

传统下肢假肢运动意图识别常使用多模态传感器采集残肢侧时频域特征,在短时意图识别中,具有一定的复杂性和滞后性,且时频域特征不稳定难以达到实时意图识别的目的.鉴于此,提出基于改进模板匹配技术的智能下肢假肢运动意图实时识别的方法.在重新定义单侧下肢截肢者的运动模式后,仅采用惯性传感器采集健肢侧位于摆动相的数据,基于改进的模板匹配,通过滑动窗口创建完备的模板库,使得每类运动模式在库中有充足的原子模式,对下肢假肢的运动意图进行实时识别.实验结果表明,所提出方法在5种稳态模式(平地行走、上下楼、上下坡)的识别率为99.50%,在引入8种转换模式后的识别率为97.03%,可以大大提高下肢假肢实时识别性能,助力单侧下肢截肢者更自然地行走.     

水下金刚石绳锯机张力检测系统

介绍了串珠绳张力传感器的主要结构和测量原理,在此基础上,设计出一种新型动静态串珠绳张力检测系统.该系统实现了由应变计组成的电桥测量线路,以及传感器输出信号的处理,并实时传送至显示仪表.该系统能够准确、可靠地测试和显示串珠绳张力状态,有利于提高绳锯机的工作效率.该装置具有良好的防水密封性能和抗振动性能,保证了在水下安全稳定地工作.实际应用表明:该检测系统具有结构简单、操作方便、测量准确度达到0.1 %FS,适用范围为3 000 N以内,能满足工程现场使用要求.     

基于无线多传感器信息融合的火灾检测系统

传统的火灾监控系统往往采用基于单传感器的有线信息检测与传输系统,布线不方便,环境适应性和抗干扰能力较差.本文提出了一种基于ZigBee无线多传感器网络的火灾监控系统,运用包括烟雾、温度、CO气体等多传感器感知火燃烧状态,对是否发生火灾分配不同信任度函数,利用D-S证据理论融合3种传感器信息以判断火灾状态.本文详细阐述了系统工作原理、多传感器信息、D-S证据理论等关键技术,并给出了多传感器响应燃烧实验的曲线图,理论分析和实验结果表明,该无线传感器火灾监测系统能更准确地检测火情,减少误报率,提高系统的可信度.     

深海起重机系统的实时轨迹规划方法

近年来, 随着海洋资源的不断开发与海洋工程的全球化推进, 深海起重机得到了广泛应用, 其控制问题也引起研究人员的极大关注. 在深海作业环境中, 由于吊运过程受到水流作用力的影响, 负载摆动幅度增大, 系统状态量间非线性耦合关系增强, 使系统控制难度加大. 为此, 本文针对深海起重机系统提出了一种实时轨迹规划方法. 具体而言, 通过分析系统动力学特性和状态变量之间复杂的耦合关系, 提出了一种实时规划轨迹的方法, 并从理论上证明了该方法可在使台车准确快速到达指定位置的同时, 有效抑制负载摆动. 最后, 一系列仿真结果证明了所提方法的良好性能.     

基于GPRS的输电线路多参量远程采集系统

在分析现有输电线路实时监测系统数据传输模式的基础上,提出一种基于GPRS技术的远程数据采集系统.该系统利用GPRS网络和Socket网络通信技术开发,能够全天候工作,实现输电线路实时状态监测数据的无线传输.结果表明,系统能够实时、准确地传输输电线路的状态数据,满足实时传输数据的需要,为用户监测、分析输电线路状态奠定基础,在输电线路监测平台中具有较强的针对性和可靠性.     

Two walking gaits for a planar bipedal robot equipped with a four-bar mechanism for the knee joint

The design of a knee joint is a key issue in robotics and biomechanics to improve the compatibility between prosthesis and human movements, and to improve the bipedal robot performances. We propose a novel design for the knee joint of a planar bipedal robot, based on a four-bar linkage. The dynamic model of the planar bipedal robot is calculated. Two kinds of cyclic walking gaits are considered. The first gait is composed of successive single support phases with stance flat-foot on the ground separated by impacts. The second gait is a succession of finite time double support phases, single support phases, and impacts. During the double support phase, both feet rotate. This phase is ended by an impact of the toe of the forward foot, while the rear foot is taking off. The single support phase is ended by an impact of the swing foot heel, the other foot keeping contact with the ground through its toe. For both gaits, the reference trajectories of the rotational joints are prescribed by cubic spline functions in time. A parametric optimization problem is presented for the determination of the parameters corresponding to the optimal cyclic walking gaits. The main contribution of this paper is the design of a dynamical stable walking gait with double support phases with feet rotation, impacts, and single support phases for this bipedal robot.     

Walking algorithm for a robotic transfemoral prosthesis capable of walking pattern recognition and posture stabilization

This paper describes a walking pattern generation algorithm for a robotic transfemoral prosthesis that is synchronized with the walking motion of a transfemoral amputee, and posture stabilization for ground adaptation and maintaining balance on inclined grounds. The developed robotic transfemoral prosthesis in this study has a knee joint and ankle roll/pitch joints for walking on complex slopes. The walking motion data obtained from the motion capture system are used as the standard walking pattern data to accurately imitate the inherent gait of the wearer. Walking intention, percent of gait cycle (PGC), and walking stride are predicted through two inertial sensors attached at both thighs, and the joint angles of the robotic transfemoral prosthesis are then generated in real-time from the PGC and the standard walking pattern data. Additionally, variable impedance control and zero moment point (ZMP) control are carried out with a force/torque sensor for posture stabilization against variable ground slopes, and ground slope compensation and disturbance rejection are also done with the use of inertial sensors at the foot and shank. Consequently, the performance of the walking pattern generation algorithm and posture stabilization control was verified through walking experiments of an author on an inclined treadmill.     

表面垂足刺激器的研究现状与趋势

足下垂是由腓总神经功能障碍引起踝关节无法背屈、行走时足趾拖地的症状。它不仅影响患者日常行走,还会使 其产生自卑心理。表面垂足刺激器将刺激电极贴附在腓总神经或者胫骨前肌上,使用传感器来侦测脚步动作,通过电刺 激使脚踝产生背曲屈及翻转动作,改善步行摆动期所发生的足下垂现象。本文阐述了表面垂足刺激器的工作原理及研究 进展,并对基于生物信号反馈的闭环足下垂刺激器的研究趋势进行了介绍。     

Gait event detection based on inter-joint coordination using only angular information

The detection of gait events with wearable sensors is necessary for a robotic system interacting with walking people. Conventional gait phase detection methods are based on machine learning. However, this method cannot detect a gait event every gait cycle because it is difficult to extract characteristic points. Additionally, using only angular information for detection is beneficial because angular information is needed for the control and evaluation of the robots. This paper proposes a novel algorithm for the detection of heel contact and toe-off using the inter-joint coordination of the hip, knee, and ankle joints that has a lower-dimensional structure. The proposed algorithm derives the four planes in the angular space and finds the switching points of the planes. Seven participants walked on force plates that measured the force of the foot against the floor. The error was less than 0.035 s when the gait events were detected after calculating planes using the first gait datum. The change in the patterns of the inter-joint coordination reflected the change in gait phases. Although the data were calculated offline, the results show that the heel contact and toe-off could be detected as soon as the angles were sensed once the planes were derived.     

Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion

Gait detection plays an important role in areas where spatial-temporal gait parameters are needed. Inertial sensors are now sufficiently small in size and light in weight for collection of human gait data with body sensor networks (BSNs). However, gait detection methods usually rely on careful sensor alignment and a set of rule-based thresholds, which are brittle or difficult to implement. This paper presents an adaptive method for gait detection, which models human gait with a hidden Markov model (HMM), and employs a neural network (NN) to deal with the raw measurements and feed the HMM with classifications. Six gait events are involved for a detailed analysis, i.e., heel strike, foot flat, mid-stance, heel off, toe off, and mid-swing. In order to obtain enough gait data for training a gait model, the gait events are labeled by a rule-based detection method, in which the predefined rules are verified with an optical motion capture system. Experiments were conducted by nine subjects, based on a dual-sensor configuration with one sensor on each foot. Detection performance is quantified using metrics of accuracy, sensitivity and specificity, and the averaged performance values are 98.11%, 94.32% and 98.86% respectively with a timing error less than 2.5 ms.     

基于行走时脚摆角的步态识别方法

提出一种利用脚摆动特征进行步态识别的方法。对步态序列图像进行背景提取、图像差分、阈值分割、形态学后处理后,提取行走时的脚摆角作为特征参数,再分别采用BP神经网络、最近邻分类器和K近邻分类器法对这些特征数据进行识别分类与比较分析。实验结果表明,与同类方法相比,该方法可以更快速地进行步态识别,且识别性能较好。     

Walking and steering control for a 3D biped robot considering ground contact and stability

This paper presents a stable walking control method for a 3D bipedal robot with 14 joint actuators. The overall control law consists of a ZMP (zero moment point) controller, a swing ankle rotation controller and a partial joint angles controller. The ZMP controller guarantees that the stance foot remains in flat contact with the ground. The swing ankle rotation controller ensures a flat foot impact at the end of the swinging phase. Each of these controllers creates 2 constraints on joint accelerations. As a consequence, the partial joint angles controller is implemented to track only 10 independent outputs. These outputs are defined as a linear combination of the 14 joint angles. The most important question addressed in this paper is how this linear combination can be defined in order to ensure walking stability. The stability of the walking gait under closed loop control is evaluated with the linearization of the restricted Poincare map of the hybrid zero dynamics. As a result, the robot can achieve an asymptotically stable and periodic walking along a straight line. Finally, another feedback controller is supplemented to adjust the walking direction of the robot and some examples of the robot steered to walk along different paths with mild curvature are given.     

Design of a powered ankle-foot prosthesis with an adjustable stiffness toe joint

ABSTRACT

This paper proposes a powered ankle-foot prosthesis called PANTOE II, which includes an ankle and a segmented foot with a toe joint. The ankle and toe joints are both driven by series elastic actuators, which can reduce the required velocity and energy consumption of the actuators. The mechanical design of the ankle and toe joints makes the prosthesis more compact. In addition, PANTOE II employs a more human-like foot, which includes the heel, the mid foot, and the toe. Both the heel and the toe are made of leaf springs, and the mid foot is used to install the transmission mechanisms. The finite state control strategy is used for controlling the prosthesis. To evaluate the basic performance of PANTOE II, experiments are conducted on a subject with a transtibial amputation. Wearing PANTOE II, the amputee feels more comfortable and presents more symmetrical walking gaits.     

Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors

Based on the regularity nature of lower-limb motion, an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram (EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient (ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model (HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground, stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.