脑机接口对脑卒中偏瘫患者下肢功能的长期疗效研究

目的 探讨兼有视觉运动反馈脑机接口技术的运动想象训练在脑卒中患者康复中的长期应用效果,为脑卒中患者的康复治疗提供依据。方法 选择2022年8月—2023年8月在安徽医科大学第二附属医院康复医学科住院的45例脑卒中偏瘫患者作为研究对象。随机分为A组(对照)、B组(运动想象)、C组(脑机接口),均采用住院(2～3周)、回家(2～3周)、住院(2～3周)的模式进行康复。住院期间,A组仅采用常规康复治疗;B组在常规康复基础上嘱患者本人自行进行运动想象反馈治疗;C组在常规康复基础上使用脑机接口技术进行干预。比较三组患者治疗前后Fugl-Meyer下肢运动功能评定量表(Fugl-Meyer Assessment-Lower Extremity, FMA-LE)、改良Ashworth分级(Modified Ashworth Scale, MAS)、徒手肌力检测(Manual Muscle Test, MMT)。结果 治疗前,各组FMA-LE、MAS、MMT比较,差异均无统计学意义(P>0.05)。治疗后,各组观察指标均优于治疗前(P<0.05)。组间比较结果显示：第一疗程后,C组FMA-...     

Motomed康复训练器在脑卒中偏瘫中的效果及对肢体功能的影响

目的:分析Motomed上下肢康复训练器在脑卒中偏瘫患者中的应用效果及对肢体功能的影响.方法:以2019年1月-2020年1月本院收治的92例脑卒中偏瘫患者为对象,根据随机数字表法将其分为两组,对照组46例予以常规康复训练,试验组46例予以Motomed上下肢康复训练器训练,对比分析两组治疗后上肢、下肢康复效果、神经功...     

复合平衡抽油机的动态测试与分析

利用非电量测试方法对新设计的复合平衡抽油机在额定载荷下主要构件进行了动态应力测试,并对抽油机悬点加速度进行了测定。测试结果表明：整机工作状态良好,受力基本对称,换向平稳,强度设计余量较大,可以保证在额定载荷下正常工作。     

基于能量平衡原理的机床动态性能分析

机床被广泛应用于工业生产中,是最主要的生产加工设备。机床动态设计的特点是在机床研发阶段解决各类问题,它能较为全面地考虑到机床在后期实际运行过程中各种动态因素对机床动态性能的影响。本文基于能量平衡原理对机床的动态性能进行了分析,并提出了一些改进方案。     

平衡悬架钢板弹簧动态特性的研究

在Adams/Chassis Leafspring专业模块自动生成钢板弹簧模型的基础上,考虑到该专业模块本质上是针对传统钢板弹簧建模所设计的,同时又存在板簧的端部接触和各片之间的摩擦等关键部分处理粗糙的问题,因此对模型的关键部分进行重新定义从而得到了基于Adams/Chassis Leafspring专业模块基础上的二次开发模型。利用相关试验设备对平衡悬架钢板弹簧实物进行力学特性试验,并将试验测量结果与模型仿真结果进行对比分析,保证了板簧二次开发模型的可靠性。对于平衡悬架钢板弹簧而言,还提出一种计算动刚度的公式并对该公式的定义进行详细的解释。针对板簧二次开发模型进行不同工况下的仿真分析,总结出平衡悬架钢板弹簧所具有的动态特性,为改善整车平顺性提供了理论基础。     

基于能量平衡原理的机床动态性能研究

对基于能量平衡的机床动态设计技术进行了研究,提出将能量平衡原理的动态优化设计方法与有限元法相结合,从能量的角度分析机床的薄弱环节以及动态特性,并将该方法应用于SL-500/Hz超精密平面磨床的动态分析。     

有限元技术在基于能量平衡的机床动态设计中的应用

结构动态系统的能量平衡是实现机床结构动态设计的依据之一.文中尝试将有限元技术应用于基于能量平衡的机床结构动态设计,在介绍结构动态系统能量分析方法的基础上,针对结构动态系统的有限元模型,提出定量描述结构动态特性优劣的方法,通过算例对悬臂梁结构的集中参数模型能量分析结果与有限元能量分析结果进行比较,并以有限元能量分析实现悬臂梁结构的改进.结果表明该结构动态设计技术具有明显的优越性,最后将该技术应用于某平面磨床结构的动态特性分析,找出该磨床结构的薄弱环节,对该磨床结构的优化设计起到指导作用.     

基于能量平衡原理的机床结构动态优化

提出了一种基于能量平衡原理的机床结构动态优化的方法,通过提高机床部件能量分布的均匀性来提高机床动态性能.以数控雕铣机龙门架的动态性能优化设计为例,对这一方法做了具体的阐述.首先,利用有限元软件模态分析确定出部件危险模态的固有频率及振型;然后,根据能量分布确定出动能和势能集中的区域,再根据能量平衡原理对零件结构进行优化;...     

基于动态响应的高速压力机综合平衡优化

针对曲柄式高速压力机动平衡中存在的机身激振力与激振力矩平衡相互制约的问题,提出了一种基于动态响应的综合优化模型。通过有限元模拟获得机身在单位简谐载荷作用下的动态响应向量,通过傅里叶级数分解得到实际激振载荷的振幅向量,以此构造了一个直接以机身振动响应为对象的综合平衡优化模型,该模型能够适用于一般机械系统。     

加权动态时间扭曲的不平衡结构转子故障诊断

为了弥补实际工业数据和实验实验室数据之间的差距,提出了一种基于加权动态时间扭曲的不平衡结构转子故障诊断方法。通过使用基于故障信息内容的加权方案改进的软动态时间扭曲方法处理数据安全性和不平衡问题。在故障分类阶段,引入了一种结构转子故障的早期分类方法,通过仅将准确度作为目标来开发序列深度学习分类器,然后通过考虑准确性和早期性来定义早期决策策略。在试验台数据集上产生的结果证明了提出方法能够有效提升结构转子故障诊断的精度,并且有效同化数据之间的差异。     

建模分析悬臂式掘进机的液压行走系统

正确地把握基于液压动力的电机动作控制,才能保障掘进机行走系统的可靠性,从而保障掘进机行走路线的准确性。介绍了悬臂式掘进机液压行走系统的基本结构,论述了悬臂式掘进机液压行走系统的基本原理,对液压行走系统的关键点进行分析建模,对掘进机液压行走系统进行仿真应用。事实证明,所建系统模型具有良好的动态适应性以及稳定性。     

一种智能医疗机器人行走机构的研究

介绍了一种在移动机器人中常用的全方位移动机构一麦卡纳姆轮系的原理以及结构。然后在此基础上对其进行了运动学分析和控制系统的设计，实现了其在平面上的全方位移动。     

基于CosMosMotion的四足步行机构的设计

根据仿生学的原理,通过对四足动物的步态分析,设计出快速步行的的四足步行机构,并基于CosMosMotion,对其进行了运动学分析.     

基于弹簧小车模型和预观控制的双足快速步行研究

针对机器人的滑动和滑转,补充了机器人稳定步行的修正条件;利用三阶泰勒公式引入舒适度的概念,推导出基于舒适度的ZMP公式;提出一种用于动力学分析的三维弹簧平台-小车模型,通过简化的舒适度ZMP公式推导出该模型的运动规律;基于三维弹簧平台-小车模型,结合预观控制理论生成步行样本;最后,进行了机器人虚拟样机快速步行仿真,步速达到2.088km/h。     

SP型平衡阀流量特性的实验研究

通过实验研究：①得到了SP型平衡阀流量系数与开度的关系,为这种平衡阀的应用性计算提供了条件;②得到了SP型平衡阀的理想流量特性曲线,并分析了这种平衡阀的调节性能。     

P-FUZZY-PID复合控制在步进式加热炉温度控制中的应用

步进式加热炉是轧钢生产线上的重要设备之一,其动态特性包括非线性、不确定性、强耦合等．用常规PID方法难以进行有效的控制。本文提出了一种基于模糊规则切换的P-FUZZY-PID复合控制器,芳进行了系统仿真,仿真结果说明该控制器对加热炉温度控制效果良好。     

Landing control method of a lightweight four-legged landing and walking robot

The prober with an immovable lander and a movable rover is commonly used to explore the Moon’s surface. The rover can complete the detection on relatively flat terrain of the lunar surface well, but its detection efficiency on deep craters and mountains is relatively low due to the difficulties of reaching such places. A lightweight four-legged landing and walking robot called “FLLWR” is designed in this study. It can take off and land repeatedly between any two sites wherever on deep craters, mountains or other challenging landforms that are difficult to reach by direct ground movement. The robot integrates the functions of a lander and a rover, including folding, deploying, repetitive landing, and walking. A landing control method via compliance control is proposed to solve the critical problem of impact energy dissipation to realize buffer landing. Repetitive landing experiments on a five-degree-of-freedom lunar gravity testing platform are performed. Under the landing conditions with a vertical velocity of 2.1 m/s and a loading weight of 140 kg, the torque safety margin is 10.3% and 16.7%, and the height safety margin is 36.4% and 50.1% for the cases with or without an additional horizontal disturbance velocity of 0.4 m/s, respectively. The study provides a novel insight into the next-generation lunar exploration equipment.     

Impact of descending brain neurons on the control of stridulation, walking, and flight in orthoptera

Orthopteran insects (crickets, bushcrickets, and acridid grasshoppers) are preferred preparations for the study of the central nervous mechanisms that underlie behavior. Many of their behaviors are based on central rhythm-generating circuits located in the ganglia of the ventral nerve cord. Activities of these circuits must be coordinated and adapted to the behavioral context by sensory information, which can derive from proprioceptive or exteroceptive inputs. Information from various sensory modalities converges in yet unidentified "higher brain centers" that integrate and transform it into activity patterns across populations of descending brain neurons (DBNs). Transmission of "decisions" to the thoracic motor centers leads to adjustment of their functions in order to fit the sensory situation encountered. A number of unique DBNs has been identified by morphological and physiological criteria and their role in controlling aspects of specific behaviors has been the subject of various studies. Their functions range from "switch-like" transmitters of brain output to complex integration units for sensory inputs of various modalities and their appropriate insertion into the ongoing activities of the thoracic rhythm generators. This paper highlights some of the characteristics of DBNs by focussing on three motor behaviors: stridulation, a stereotyped behavior that seems to be mainly controlled by command-like DBNs; walking, a plastic behavior whose various parameters must continuously be adjusted to a changing sensory environment; and flight, in which the information for course corrections encoded for by different types of DBNs is transformed to match the rhythmic activity of the flight oscillators before it affects the respective motoneurons.     

六足步行机器人分级控制及通信

研究了六足步行机器人的分级控制策略;实现了Windows的PC机和工控机之间的数据通信;制定了基于RS—232C的串口通信协议;研究了Windows平台下串口通信的实现方法;并用Visual C 编写通信类实现了上位机Windows环境下的通信编程。实验证明,数据通信稳定可靠;分级控制实时、有效。     

Image-based fall detection and classification of a user with a walking support system

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user’s depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called “RT Walker”) by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user’s motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.