Coordinated control strategy for robotic-assisted gait training with partial body weight support

Walking is the most basic and essential part of the activities of daily living. To enable the elderly and non-ambulatory gait-impaired patients, the repetitive practice of this task, a novel gait training robot(GTR) was designed followed the end-effector principle, and an active partial body weight support(PBWS) system was introduced to facilitate successful gait training. For successful establishment of a walking gait on the GTR with PBWS, the motion laws of the GTR were planned to enable the phase distribution relationships of the cycle step, and the center of gravity(COG) trajectory of the human body during gait training on the GTR was measured. A coordinated control strategy was proposed based on the impedance control principle. A robotic prototype was developed as a platform for evaluating the design concepts and control strategies. Preliminary gait training with a healthy subject was implemented by the robotic-assisted gait training system and the experimental results are encouraging.

Regional cerebral blood flow imaging assessment of brain function reconstruction in elderly hemiplegia patients by body weight support treadmill training

The mechanism underlying body weight support treadmill training in elderly hemiplegic stroke patients is largely unknown. This study aimed to elucidate the changes of cortical blood flow in seven elderly patients with post-stroke hemiplegia before and after body weight support treadmill training by semi-quantitative analysis of regional cerebral blood flow assessed by single photon emission computed tomography. Body weight support treadmill training for 6 months was effective in improving cerebral blood flow and promoting the walking speed and balance recovery in elderly patients with post-stroke hemiplegia.

Experimental Study of an Active Body-Weight Support System for Low Limbs Rehabilitation Training

An experimental study of an active body-weight support（BWS） system for improving treadmill-based locomotion training is performed.The dynamical foundation of the proposed system is developed based on a simplified cable suspended mass-spring-damping system which is used to mimic the vertical gait of a walking human.A specifically designed cable pulley suspended cam-slider system is used to mimic the walking gait of a human in vertical direction.A load cell is installed to connect the slider and the cable which is driven by a winch based on the acceleration feedback.The contact force between the slider and the cam is measured to evaluate the walking load of the system.The experimental results demonstrate that the proposed active BWS system can simultaneously reduce both gravitational and inertial load of the walking body,which implies that the walking body suspended in such a BWS system will dynamically behave as if certain amount of body mass had been removed.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Fast Adaptive Support-Weight Stereo Matching Algorithm

Adaptive support-weight (ASW) stereo matching algorithm is widely used in the field of three-dimensional (3D) reconstruction owing to its relatively high matching accuracy. However, since all the weight coefficients need to be calculated in the whole disparity range for each pixel, the algorithm is extremely time-consuming. To solve this problem, a fast ASW algorithm is proposed using twice aggregation. First, a novel weight coefficient which adapts cosine function to satisfy the weight distribution discipline is proposed to accomplish the first cost aggregation. Then, the disparity range is divided into several sub-ranges and local optimal disparities are selected from each of them. For each pixel, only the ASW at the location of local optimal disparities is calculated, and thus, the complexity of the algorithm is greatly reduced. Experimental results show that the proposed algorithm can reduce the amount of calculation by 70% and improve the matching accuracy by 6% for the 15 images on Middlebury Website on average. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.

ON THE WEIGHTED OCCUPATION TIME AND SUPPORT OF SUPER-BROWNIAN MOTION CONDITIONED ON NON-EXTINCTION

This paper investigates the property of super-Brownian motion conditioned on non-extinction. The authors obtain a representation of Laplace functional for the weighted occupation time of this class of processes. By this, they get a result about the distribution of the support of it.

Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

下肢康复减重支撑系统的结构设计与分析

面向我国脑卒中和老龄化等原因导致的下肢运动功能障碍患者,开发了一种基于人体足底压力分布和重心轨迹的双腋支撑下肢康复减重系统。首先,分析研究了减重支撑训练对足底压力的影响;其次,基于人机工程学理论并结合人体重心轨迹研究,对总体结构进行了分析与设计,并确定了相关尺寸;然后,通过静力学分析,验证了结构的可靠性;最后,对减重模块进行了运动学分析和动力学仿真。该系统可帮助下肢运动功能障碍的患者实现模仿正常人行走的康复训练。

基于特征判定系数的电力变压器振动信号故障诊断

变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义。针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法。通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断。对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率。

全髋关节置换后减重太极步法可改善平衡功能

背景:全髋关节置换后患者大多存在平衡障碍,需要应用多种康复方法来改善,减重太极步法可以作为一种安全有效的平衡训练方法。目的:观察减重太极步法对全髋关节置换后患者平衡功能的影响。方法:招募74例全髋关节置换后患者,随机分为对照组和试验组(n=37)。对照组实施减重步行训练30 min和常规康复训练60 min;试验组实施减重太极步法训练30 min和常规康复训练60 min;两组均每天1次,一周5次,持续12周。于干预前、干预4,8,12周后分别采用Berg平衡量表和动态平衡能力测试来评估平衡功能,Harris评分评估髋关节功能,跌倒风险指数评估跌倒风险。结果与结论:①4个观察指标均显示有显著的时间效应(P<0.001);②Berg平衡量表、Harris评分与跌倒风险指数均具有交互作用(P<0.001),并在干预12周后具有显著的组间差异(P<0.001),且试验组效果优于对照组;③干预12周后动态平衡能力测试的前和左方向评分有交互作用和组别效应(P<0.001),整体、前、左、右方向评分具有显著组别差异(P<0.001);④结果表明,干预12周后,试验组与对照组的平衡功能均有改善,且减重太极步法训练对全髋关节置换后患者的改善效果优于减重步行训练。

适应人体重心起伏的悬吊减重康复系统设计

减重系统对下肢疾病患者的行走康复训练有重要影响。现有的下肢康复外骨骼减重装置大多只考虑如何减去患者体重的百分比,而忽略了患者身体重心的起伏。由于外骨骼的骨盆支架在竖直方向运动轨迹固定,患者步态的微小变化就会导致重心高度与骨盆支架运动轨迹不符,这一差异会作用到患者的骨盆位置,影响下肢关节的活动,并产生额外的风险。针对这一问题,提出通过采集足底压力来预测重心位置变化,并利用获得的重心轨迹解算出应施加的减重力方法,为患者训练提供安全有效的减重系统。所提方法的可行性已获仿真证实,并在研发的外骨骼减重系统上获得实际验证,模糊控制器跟踪重心轨迹的误差相比PID控制减少了21.2%,稳态误差维持在±1 mm范围内,髋膝关节的活动范围相比常规减重分别增加了14.36%和13.77%。

基于腺苷酸激活蛋白激酶-自噬信号通路探讨减重平板训练对脊髓损伤大鼠运动功能的影响

目的 基于腺苷酸激活蛋白激酶(AMPK)-自噬信号通路探讨减重平板训练对脊髓损伤(SCI)大鼠运动功能的影响及其作用机制。方法 选择30只10周龄SPF级雌性SD大鼠,按照随机数字表法分为假手术组、模型组和运动组,每组10只。模型组和运动组均采用Allen's法制作T10不完全性脊髓损伤模型,假手术组仅进行假手术。3组均于SCI造模前3 d进行预适应运动训练,以适应跑台环境。运动组于造模后第8天进行减重平板训练,减重为大鼠体质量的20%~40%,跑台速度6 m/min,20 min/次,2次/d,持续训练3周;假手术组和模型组造模后均自由活动,不进行减重平板训练。分别在造模后第1、7、14、21、28天采用脊髓损伤行为学(BBB)评分法评估各组大鼠后肢运动功能;于干预结束后(SCI后第28天)采用步态分析处理系统评估大鼠的步态参数(足印面积、步幅长度、触地强度、摆动速度和运动速度);采用尼氏染色法观察脊髓神经元形态及数目;采用Western blot法检测脊髓组织p-AMPK、t-AMPK、LC3、p62蛋白表达水平。结果(1) BBB评分:与假手术组比较,模型组、运动组造模后第1、7、14、21、28天BBB评分明显更低,差异均具有统计学意义(P<0.05);与模型组同一时间点比较,运动组造模后14、21、28天BBB评分明显更高,差异均具有统计学意义(P<0.05);模型组、运动组内不同时间点BBB评分比较,差异均具有统计学意义(P<0.05)。(2)步态参数:与假手术组比较,模型组足印面积、触地强度、摆动速度、步幅长度和运动速度均明显更低,运动组足印面积明显更低,差异均具有统计学意义(P<0.05);与模型组比较,运动组第28天足印面积、触地强度、摆动速度、步幅长度和运动速度均明显更高,差异均具有统计学意义(P<0.05)。(3)脊髓前角神经元形态及数目:与假手术组比较,模型组脊髓前角神经元数目明显减少(P<0.05),可见细胞核固缩。与模型组比较,运动组脊髓前角可见较为清晰的尼氏体,细胞核固缩程度降低,脊髓前角神经元存活数目增加(P<0.05)。(4) p-AMPK、t-AMPK、LC3、p62蛋白表达水平:与假手术组比较,模型组LC3Ⅱ/LC3Ⅰ明显降低,p62明显升高,差异均具有统计学意义(P<0.05);与模型组比较,运动组LC3Ⅱ/LC3Ⅰ、p-AMPK/t-AMPK明显升高,p62明显降低,差异均具有统计学意义(P<0.05)。结论 减重平板训练可改善SCI大鼠神经功能和后肢运动功能,这可能与激活脊髓AMPK-自噬信号通路有关。

基于ICEEMDAN和时变权重集成预测模型的变压器油中溶解气体含量预测

为了实现对变压器油中溶解气体体积分数的精确预测,同时克服仅使用单一预测模型导致预测精度及泛化能力不足的局限,提出了一种基于改进完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition,ICEEMDAN)和灰色关联系数时变权重集成预测模型的变压器油中溶解气体预测方法。首先将溶解气体含量序列模态分解为一系列具有不同时间尺度的子序列。然后,使用门控循环神经网络和麻雀搜索算法优化支持向量机对各子序列进行训练,组合为一个集成预测模型;并比较不同预测方法的预测精度,计算灰色关联系数时变权重,形成各子系列的预测结果。最后将各子序列的预测结果叠加重构,得到最终预测结果。算例分析结果显示:该方法单步预测的均方根误差、平均绝对误差和相关系数分别为0.593、0.422和0.768,相比其他算法在预测精度上有明显提升,同时具有很强的泛化性能,可以为油浸式变压器内部状态监测提供依据。

非线性时变PSO优化SVM的入侵检测方法

针对现有使用时变粒子群算法(TVCPSO)优化支持向量机(Support Vector Machines,SVM)对网络流量数据进行入侵检测的方法存在的粒子搜索能力不足等问题,提出了一种非线性时变粒子群算法优化SVM参数的入侵检测方法。该方法首先融合ReliefF算法与信息增益算法对网络流量数据进行特征降维,然后通过非线性学习因子和自适应权重改进时变粒子群算法优化支持SVM,最后通过SVM完成网络流量的检测。NSL-KDD上的结果表明论文方法达到了97.86%的准确率、97.67%的检测率和2%的误报率,验证了方法的有效性。

杭来湾煤矿30201综采面矿压显现及支架适应性分析

综采面顶板控制是矿井安全生产中面临的关键问题,有必要对综采面矿压显现规律和支架适应性开展研究,以保障综采面安全生产。通过对杭来湾煤矿30201工作面矿压显现规律进行现场观测和分析,发现工作面支架工作阻力整体表现为“中间大、两头小”的分布规律;上、中、下部支架平均工作阻力分别约为31.76 MPa、37.91 MPa和45.44 MPa,平均动载系数分别为1.228、1.413、1.291,支架安全阀平均开启次数分别为7.8、29.5、8.6;建立了工作面正常开采期间与周期来压期间支架-顶板力学模型,给出了相应的支架临界工作阻力计算方法,计算得到正常开采期间支架所需载荷为14700 kN,周期来压期间所需载荷为17050 kN。经验证,30201工作面ZY21000/36/75D号支架能够满足安全生产要求,研究成果可为后续矿井开采设计、顶板控制等提供借鉴。

基于特征加权混合隶属度的模糊孪生支持向量机

模糊孪生支持向量机(FTSVM)忽略了不同特征间的差异,导致核函数或距离的计算无法准确反映样本间的相似性,使FTSVM在处理含有大量不相关或弱相关特征的高维数据分类时,难以达到良好分类效果;且隶属度的设计未有效区分离群点或噪声。针对以上问题,提出了一种基于特征加权混合隶属度的FM-FTSVM。首先计算每个特征的信息增益,并依据信息增益值的大小为特征赋予权重,降低不相关或弱相关特征的作用,使其能更好地应用于高维数据分类;然后,为每一类样本构造一个最小包围球计算基于紧密度的特征加权隶属度,并结合基于距离的特征加权隶属度得到特征加权混合隶属度,综合考虑样本点到类中心的特征加权欧式距离和样本间的紧密程度,可更好识别离群点或噪声数据;最后,融合特征加权核函数,降低不相关特征对核函数或距离计算产生的影响。与对比算法在人工数据集、高维数据集和UCI数据集上进行比较,发现本文提出的方法在区分离群点、噪声和有效样本上有明显优势,且在高维数据集上可获得更好分类效果。

减重结合功能性电刺激对脑卒中患者下肢运动功能的疗效

目的观察减重结合功能性电刺激对脑卒中患者下肢运动功能的疗效。方法2023年1月至2024年2月,选择佛山市禅城区人民医院康复医院住院脑卒中患者60例,随机分为对照组(n=30)和观察组(n=30)。对照组接受常规康复,观察组增加天轨系统减重结合功能性电刺激,共3周。治疗前后采用平衡评定与训练仪、Berg平衡量表(BBS)、FMA评定量表下肢部分(FMA-LE)、Holden步行功能分级(FAC)进行评定。结果治疗后,两组X轴轨迹长、动摇平均速度、动摇总轨迹长、Y轴轨迹长、单位面积轨迹长、BBS评分、FMA-LE评分、FAC评分均较治疗前显著改善(|t|>4.809,P<0.001)。除X轴轨迹长和动摇平均速度外,其他指标观察组均优于对照组(|t|>2.268,P<0.05)。结论增加减重结合功能性电刺激能更有效改善脑卒中患者的下肢运动功能、平衡功能和步行功能。

MOTOmed智能训练系统联合减重步态训练改善脑卒中偏瘫患者偏瘫步态研究

目的观察MOTOmed智能训练系统联合减重步态训练对脑卒中偏瘫患者伸肌协同偏瘫步态的运动学影响。方法收集2021年12月—2022年12月在沧州市人民医院住院治疗的脑卒中偏瘫患者共90例,并按照随机数字表法将其分为MOTOmed智能训练系统组(对照组)以及MOTOmed智能训练系统联合减重步态训练组(试验组),每组45例。2组均采用常规康复治疗,辅以MOTOmed智能系统训练,包括利用MOTOmed智能系统的下肢环转运动训练和主动模式进行抗阻力训练。试验组增加减重步态训练,减小重量从体质量的30%开始,根据患者身体情况酌量减少至体质量15%水平,或完全负重,平板运行速度为0.2~1.0 m/s。2组在治疗前及治疗4周后进行康复评估,应用Barthel指数评定量表、Holden步行功能评定、偏瘫步态分析评价量表、下肢运动功能评估(FMA-LE)及平衡功能评估(BBS),比较2组的康复治疗效果。结果与组内治疗前比较,2组治疗4周后Barthel指数评定量表评分、Holden步行功能分级、偏瘫步态分析均有所改善(P<0.05)。与对照组相比较,试验组治疗4周后Barthel指数评定提高(P<0.05),患侧支撑期百分比、步速、步频和步长增大(P>0.05),双支撑期百分比减少(P<0.05),Holden步行功能分级改善(P<0.05),FMA-LE和BBS评分增加(P<0.05)。结论MOTOmed智能训练系统联合减重步态训练可有效地改善脑卒中伸肌协同偏瘫患者的步态及步行的能力。

个体化发育支持护理及评估在极低出生体质量儿护理中的应用效果分析

目的:分析个体化发育支持护理及评估在极低出生体质量儿护理中的应用效果。方法:选取2022年1月—2023年4月泰安市妇幼保健院新生儿科收治的极低出生体质量儿78例作为研究对象,随机分为对照组和观察组,各39例。对照组实施常规护理,观察组实施个体化发育支持护理及评估。比较两组体格发育指标、神经行为发育情况。结果:护理后,两组体质量、身长、上臂围、头围大于护理前,且观察组大于对照组(P<0.05)。护理后,两组神经行为发育评分高于护理前,且观察组高于对照组(P<0.05)。结论 :极低出生体质量儿护理中应用个体化发育支持护理及评估,能够改善极低出生体质量儿的体格发育、神经行为发育。