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.     

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.     

一种面向下肢假肢的运动意图识别方法及验证

对截肢者下肢运动意图的准确识别是提高下肢假肢人机交互性能，降低假肢使用者运动能耗的关键。基于健康受试者在不同运动模式的下肢表面肌电信号(sEMG)和由六自由度惯性测量单元(IMU)采集到的角速度信号、加速度信号等运动学信号设计了一种面向下肢假肢的运动意图识别方法，并通过髋截肢者的健侧肌电和两侧下肢的运动学数据对上述方法进行可行性和有效性验证。结果表明，该方法能在健康受试者的多源传感信息中选出最适于分类的最小特征子集，并在精细K最近邻(KNN)分类器中实现对站、平地走、上下楼梯、上下斜坡这6种不同运动意图高达99.2%的识别准确率；同时在髋截肢者这一类高位截肢患者的多源传感信息中依然能筛选出最小特征子集并实现高达99.8%的识别精度。实验结果说明了所提出方法的有效性和普遍适用性。     

利用多源运动信息的下肢假肢多模式多步态识别研究

运动状态识别对智能下肢假肢的控制非常关键,本文利用下肢表面肌电信号、腿部角度和足底压力信号在运动模式和步态分析中的优势和特点,对下肢假肢的多模式多步态识别进行研究.通过建立下肢运动信息系统,获取下肢多源运动信息.先提取下肢肌电信号的小波包能量作为特征,建立多个HMM对下肢假肢的运动模式进行识别;再根据大小腿和膝关节的角...     

面向人机融合的智能动力下肢假肢研究现状与挑战

智能动力下肢假肢在残疾人生活中起着越来越重要的作用.解决人-智能假肢-环境融合中的关键科学问题是实现假肢穿戴者安全、流畅运动的必要条件.本文针对此问题,综述了面向人机融合的智能动力下肢假肢研究,包括智能动力下肢假肢的仿生结构和控制方法、人体运动意图识别、复杂环境下的人-智能假肢融合、以及用于下肢假肢的感知替代和反馈,深入探讨了智能动力下肢假肢人机融合研究中所面临的挑战和问题,最后,本文对该领域的未来发展方向进行了展望和总结.     

一种人体步态轨迹测量方法

为研究人体的步态运动规律,采用基于拉线传感器检测系统和人体关节角度测量装置,对人在不同路况下行走时踝关节运动轨迹和下肢关节角度进行了测量,根据人体下肢刚体模型和数据融合方法对测量结果进行了分析和处理,得到了人在上楼梯、平地行走和下楼梯时的步态轨迹和3种路况下的支撑相;上楼梯时的支撑相最大,平地行走次之,下楼梯最小.测量结果为了解人体运动规律和康复治疗提供了依据.     

基于物理疗法轮椅式下肢康复训练系统设计

针对目前下肢康复训练设备适配性差和康复训练效果不佳的现状,并结合居家日常康复的使用要求,提出一种基于传统物理康复训练关节运动轨迹的轮椅式下肢康复训练机构。详细分析了物理疗法中下肢髋-膝关节康复训练运动规律和特点,并对一个周期内不同治疗阶段进行了系统化分析;利用该规律对可实现关节运动特征的机构构型进行尺寸综合与分析,并基于该构型研制一款轮椅式下肢康复训练系统。该款下肢康复训练系统可以实现髋关节训练运动角度为:91.37°～126.51°,膝关节训练运动角度为:89.11°～135.35°。基于该构型的实验样机可以实现物理疗法所规划的运动轨迹,实际可以实现髋关节运动角度为91°～128°,膝关节运动角度为:90°～131°。该下肢康复训练系统可以模拟康复治疗的师物理治疗时的操作手法,辅助患者进行下肢康复训练,为患者提供稳定可靠的物理治疗,具有积极的社会意义和临床价值。     

基于隐马尔可夫模型的动力型下肢假肢步态预识别

为使动力型假肢膝关节协调配合人体的运动,关键是对人体行走步态进行有效预识别.本文利用安装在假肢接受腔上的加速度传感器和安装在足底的压力传感器采集人体的运动信息,根据人体运动的规律性和重复性特点,通过将隐马尔可夫模型引入到所获得的运动信息中来分析并预识别人体的运动步态.实验表明,基于隐马尔可夫模型的动力型下肢假肢的步态预识别方法是有效并且准确的.     

基于有监督Kohonen神经网络的步态识别

表面肌电信号随着时间的变化而改变,这将影响运动模式的分类精度.传统人体下肢假肢运动模式的识别算法不能保证在整个肌电控制时间内达到对运动模式的有效识别.为了解决这些问题,本文提取步态初期200ms的信号的特征值,将无监督和有监督的Kohonen神经网络算法应用到大腿截肢者残肢侧的步态识别中,并与传统BP神经网络进行了对比.结果表明,有监督的Kohonen神经网络算法将五种路况下步态的平均识别率提高到88.4%,优于无监督的Kohonen神经网络算法和BP神经网络.     

下肢假肢穿戴者跌倒保护控制系统设计

从人机共融角度出发设计了假肢跌倒保护控制系统,包括跌倒预警和跌倒保护控制两个方面.首先通过综合分析所采集的人机信息设计跌倒预警系统,完成假肢穿戴者异常步态的有效识别与预警.通过基于健康人有效保护策略的经验知识库方法提取跌倒保护动作经验,制订对应假肢膝关节的跌倒保护策略.以四连杆假肢机构作为被控对象,通过多项式拟合建立运动控制模型,设计基于事件触发的下肢假肢跌倒保护控制系统.以跌倒预警信息作为事件触发条件,以健康人在将要跌倒状况下的膝关节恢复动作作为目标意图,来控制假肢膝关节完成预定转动.实验结果表明,假肢膝关节与人体残肢能在同一自然空间紧密协调,做出正确的保护动作.     

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.     

基于阻抗模型的下肢康复机器人交互控制系统设计

为帮助下肢功能障碍患者进行康复训练,设计了下肢康复机器人。对于该机器人的控制,采用传统系统无法柔顺控制,导致机器人运动轨迹偏离预设轨迹。针对该现象,提出了基于阻抗模型的下肢康复机器人交互控制系统设计。通过分析总体控制方案,设计系统硬件结构框图。采用L型二维力传感器,确定两个方向的人机交互力。使用绝对值编码器安装在各个关节处,其输出值作为髋关节、膝关节、踝关节电机的转动位置,增量编码器安装在电机轴上,测量值用来作为后期控制方法的输入参数。构建阻抗控制模型,能够调节机器人位置和速度,具有消除力误差功能。依据此力矩对参考运动轨迹进行设计,实时获取患者康复训练的跟踪、主动柔顺和接近状态信息。在柔顺训练实验测出人机交互力,通过实验结果知,在检测到人体主动力矩异常时,系统能够重新优化轨迹,具有良好柔顺控制效果。     

Trajectory generation and control of a knee exoskeleton based on dynamic movement primitives for sit-to-stand assistance

This paper presents a novel control approach for a knee exoskeleton to assist individuals with lower extremity weakness during sit-to-stand motion. The proposed method consists of a trajectory generator and an impedance controller. The trajectory generator uses a library of sample trajectories as the training data and the initial joint angles as the input to predict the user’s intended sit-to-stand trajectory. Utilizing the dynamic movement primitives theory, the trajectory generator represents the predicted trajectory in a time-normalized and rather a flexible framework. The impedance controller is then employed to provide assistance by guiding the knee joint to move along the predicted trajectory. Moreover, the human-exoskeleton interaction force is used as the feedback for on-line adaptation of the trajectory speed. The proposed control strategy was tested on a healthy adult who wore the knee exoskeleton on his leg. The subject was asked to perform a number of sit-to-stand movements from different sitting positions. Next, the measured data and the inverse dynamic model of the human-exoskeleton system are used to calculate the knee power and torque profiles. The results reveal that average muscle activity decreases when the subject is assisted by the exoskeleton.     

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.      

Development of vehicles with legs and wheels

This paper deals with the design of the foot trajectory for a quadruped walking machine. Such walking machines should be capable of both uneven terrain walking and high-speed flat surface walking. The static walking method was used for uneven terrain walking and the dynamic walking method was used for plane walking. In the case of dynamic walking, the relative speed between the foot and the ground causes instability in the balance of the body. A foot trajectory is designed based on two points: the kinematics of foot motion and the relationship between joint motion and joint driving torque. A method for reducing the impact force upon initial contact with a floor by designing a periodic foot trajectory based on the wave motion of a cam is discussed. In this method, vertical and horizontal motions of the foot trajectory were generated independently using cycloidic motion. We named this trajectory the composite cycloid foot trajectory. We further developed a modified cycloidic foot trajectory by smoothing the joint angular acceleration.     

Myoelectric arm using artificial neural networks to reduce cognitive load of the user

Today’s multiple degree-of-freedom myoelectric prosthesis relies only on direct control by the processed electromyographic signal. However, it is difficult for the wearer to learn unnatural muscle contractions in order to wield more than three DoFs of the arm. This makes it almost impossible to use more complex prostheses with a larger number of actuators. Methods based on sensor–actuator loop and artificial intelligence may reduce cognitive load of the user by removing low level control, and an intelligent control system would make it needless to micromanage every action. For this purpose, sensor system for body segments motion capture was developed, as well as sensor system for prosthetic limb’s environment motion capture. Neural networks were designed to process data from the sensor systems. For the identification of the knee angle, orientation trackers were used. Neural network predictor of arm positions predicts the shoulder angle using the information about movement of the lower limb. In the case of the periodic/cyclic movements of the legs, such as walking, the control unit uses typical movement patterns of the healthy upper limb. Ultrasonic range sensors are used to create 3D map of objects in the environment around the arm. Neural network predictor of object positions predicts collisions. If the potential collisions are identified, the control unit stops arm movement. The new methods were verified by MATLAB and are designed as a part of assistive technology for disabled people and are to be understood as an original contribution to the investigation of new prosthesis control units and international debate on the design of new myoelectric prostheses.

     

基于库伦-粘性摩擦模型的大腿假肢动力学参数辨识

基于模型的智能假肢控制方式具有物理意义明确、参数变量少的优势,但受建模误差、模型不确定等因素的影响,其控制精度仍有待进一步提高,而有效措施之一便是假肢先验动力学建模与辨识.本文针对实验室新设计的动力大腿假肢,研究了基于库伦-粘性摩擦的大腿假肢动力学参数辨识问题.首先,基于拉格朗日方法建立了具有固定传动比动力膝关节和非线性传动比动力踝关节的大腿假肢动力学模型;其次,采用库伦-粘性摩擦模型来描述假肢动力学模型中的关节摩擦行为;最后,通过粒子群优化算法辨识了大腿假肢的动力学参数.结果表明,相比于基于3D建模软件的估计参数,基于辨识参数重构的膝、踝关节电机扭矩与实测扭矩的均方根误差分别降低了93.55%和80.83%,模型精度得到了显著提高.这一结果不仅验证了本文假肢动力学建模与参数辨识方法的有效性,也为假肢后续的高精度控制提供了技术支撑.     

Bionic control of exoskeleton robot based on motion intention for rehabilitation training

As the complexity of obtaining irregular daily motion trajectory during upper limb rehabilitation training, this paper proposes the bionic control method for the presented exoskeleton robot arm based on motion intention. Firstly, the collected motion signal is pre-processing by filtering. Then the motion intention and motion mode of the processed signal are classified by using the hierarchical multi-classification support vector machine. Meanwhile, the adaptive Hopf oscillator network based on dynamic learning is used for offline learning of joint motion trajectory, and the parameters of the reproduced signal in different motion modes are obtained. Finally, the corresponding parameters are transferred according to the user’s intention, and the oscillator network is reconstructed to realize the periodic motion control of rehabilitation training. With experimental verification, the proposed method can follow the human body’s motion intention and reproduce the daily motion trajectory of the upper limb. The results show it can be used to conduct rehabilitation training for the patient.     

基于上臂关节角度和肌电信号的二自由度假肢控制方法

肌电信号的采集易受到空气湿度和皮肤表面汗液等多种随机因素的干扰,使采集到的肌电信号极不稳定.为了应对此问题,市售的肌电假肢普遍采用基于开关量的控制方法,但是开关量对多自由度假肢的控制依赖于顺序动作切换,这使得假肢的实际使用过程比较繁琐.利用肢体运动学信息的假肢控制方法常见于下肢假肢,这是因为上肢的运动受抓取物体的形状和位置等因素变化,其肢体运动的规律性较差.本文提出一种利用上臂关节角度和肌电信号的控制方法,利用人体在抓握时肩关节的运动模式区分使用者对不同形状物体的抓握,并将此方法应用在二自由度假肢的控制中.通过与开关量控制的假肢在日常物品抓握实验中的对比,表明本文所提出方法在稳定性和使用效率方面都优于开关量控制的方式.     

Prosthetic Hand Finger Control Using Fuzzy Sliding Modes

In order to improve the life quality of amputees, providing approximate manipulation ability of a human hand to that of a prosthetic hand is considered by many researchers. In this study, a biomechanical model of the index finger of the human hand is developed based on the human anatomy. Since the activation of finger bones are carried out by tendons, a tendon configuration of the index finger is introduced and used in the model to imitate the human hand characteristics and functionality. Then, fuzzy sliding mode control where the slope of the sliding surface is tuned by a fuzzy logic unit is proposed and applied to have the finger model to follow a certain trajectory. The trajectory of the finger model, which mimics the motion characteristics of the human hand, is pre-determined from the camera images of a real hand during closing and opening motion. Also, in order to check the robust behaviour of the controller, an unexpected joint friction is induced on the prosthetic finger on its way. Finally, the resultant prosthetic finger motion and the tendon forces produced are given and results are discussed.