连杆机构在肌电假肢肘关节上的应用

本文探讨了一种新型的假肢,采用最简单的四连杆机构,配上减速装置,形成一种新型假肢的肘关节,解决了残肢短于70毫米无法安装肌电控制假肢的矛盾。并利用计算机编程,减少了设计计算的工作量,可根据每位病人的具体尺寸,定制各种规格的四连杆机构,来满足需要,为假肢学的研究和探讨,提供了一种新的思路和方法。     

基于脑电信号识别采集的假肢控制系统

本文介绍了脑电信号（EEG）的模式识别和步骤,分析了EEG采集领域的发展和医学原理。通过研究脑电信号和假肢运动的联系,总结脑电控制假肢的可行性结论。设计出从头皮电极到模／数转换器的基于脑电信号识别采集的假肢控制系统,能够满足脑电假肢的各种要求。     

荷木叶片生理特性在树冠空间层次上的差异

通过对荷木(Schima superba)树冠空间层次上叶片形态、光合日变化和叶绿素含量的分析,揭示荷木树冠在空间层次(顶层、中层、底层)上叶片生理的变化规律。结果表明:顶层叶片的比叶面积显著低于中层和底层,叶长和最大叶宽表现为顶层中层底层的趋势(P0.01);顶层光合有效辐射显著高于中层和底层,并且顶层叶片具有更高的净光合速率(P_n)、气孔导度(g_s)、蒸腾速率(T_r)和羧化效率(V_c),底层叶片的胞间二氧化碳(C_i)则显著高于顶层;各层次的光合有效辐射与叶片的P_n、g_s、V_c、T_r和L_s有显著地正相关,与Ci呈显著负相关;顶层叶片的叶绿素a+b(Chl-a+b)和类胡萝卜素(Car)含量显著低于中层和底层,而叶绿素a/b(Chl-a/b)的均值则高于中层和底层。研究表明,光照是导致荷木叶片生理特性在树冠垂直空间层次上出现差异的主要因素。     

长白落叶松冠层光合作用的空间异质性

以2014年黑龙江省帽儿山林场14年生人工长白落叶松为研究对象,对比分析了各项光合指标、环境因子及光合生理参数在冠层内的空间差异性,并探讨了净光合速率(P_n)与其他指标的关系.结果表明: 在树冠垂直方向,上层P_n、气孔导度(g_s)和蒸腾速率(T_r)显著高于中层和下层,胞间CO₂浓度(C_i)表现为下层>中层>上层;光合有效辐射(PAR)从上层外部到下层内部呈显著降低趋势,水汽压差(VPD)和叶片温度(T_l)表现为上层显著高于中层和下层,相对湿度(RH)则无显著差异;最大净光合速率(P_{n max})、暗呼吸速率(R_d)、光补偿点(LCP)和光饱和点(LSP)均表现为上层>中层>下层,下层比上层分别降低32.7%、55.8%、80.2%和51.6%,表观量子效率(AQY)表现为下层>中层>上层,下层分别是中层和上层的1.2和1.3倍.水平方向,光合指标和环境因子的差异性主要体现在树冠上层,P_n、g_s、T_r、PAR和VPD表现为树冠外部显著高于树冠内部,而C_i和RH差异不显著;P_{n max}、R_d、LCP和LSP表现为外部>内部,内部比外部分别降低0.4%、37.7%、42.0%和16.4%,而AQY在内部比外部高0.7%.C_i是限制P_n的主要生理因子,PAR是影响P_n的主要环境因子,尤其在弱光区域PAR对P_n的影响十分明显.因此,在模拟和预估树木冠层光合作用时,考虑空间异质性有一定的必要性.     

南极地区苔藓剖面中地球化学元素的营养运移特征

对采自南极的苔藓剖面上各层苔藓的地球化学元素含量进行分析,得到以下的初步结论：地球化学元素在苔藓剖面上的分布具有很强的继承性;不同地球化学元素在苔藓剖面上的分布特征不同,这与元素在苔藓中的营养特征有关,研究发现元素Ca在苔藓体内,异常活跃,它极易被累积在新鲜苔藓体内,Zn也是苔藓易吸收的元素;地球化学元素在老新苔藓中的转移能力不同;底层苔藓的元素转移能力很低,中层苔藓中元素维持在基本平衡的状态,上层苔藓中元素的转移系数较大。     

华南地区八种人工林的土壤物理性质

研究了华南地区8种林地的土壤物理性质,根据其土壤特点分为4类。第1类为马占相思林地、黎蒴-加勒比松林地、火力楠-木荷林地、木荷林地和湿地松林地,特点是上层的土壤容重小于中层和下层,毛管孔隙度中等或较小,非毛管孔隙度中等或较大,非毛管孔隙度和总孔隙度为上层大于中层和下层,自然含水量中等或较大,随着土壤深度的增加而下降或稳定。除了马占相思林地上层外,毛管持水量为中等或较小;第2类为柚木林地,土壤容重较大,且3层土壤的容重相近,毛管孔隙度和毛管持水量较大,非毛管空隙度和自然含水量较小;第3类为落羽杉林地,各层土壤的自然含水量远远大于其他林地。上层和中层的土壤容重、毛管孔隙度和非毛管空隙度中等,毛管持水量大。下层的土壤容重和非毛管空隙度大、毛管空隙度和毛管持水量小。第4类为尾叶桉林地,上层的土壤容重大于中层和下层,毛管孔隙度和毛管持水量较大,非毛管空隙度和自然含水量较小。     

九段沙芦苇湿地生态系统N、P、K的循环特征

对九段沙(上沙、中沙、下沙)芦苇湿地土壤、植物中N、P、K含量进行了测定,研究其分布规律.结果表明:水平分布上,湿地土壤全量养分与速效性养分含量均是中沙最低,并以中沙为中心向两端递增,速效性养分的这种变化尤为显著.垂直分布上,土壤全N和全K含量的分布规律一致,为上层＞下层＞中层;全P含量分布为中层＞上层＞下层.速效N含量分布为上层＞中层＞下层;速效P含量上层土壤最高,中、下层土壤含量因湿地类型而异;速效K含量多以中层土壤最低,上、下层土壤含量变化不稳定,以不同湿地而异.不同芦苇湿地的土壤速效性养分含量差异显著大于全量养分的差异,0～60 cm深度土壤的全量养分和速效性养分含量排序均为K＞N＞P.中沙湿地植物中养分含量最低,与土壤中营养元素总体水平分布一致;多数芦苇湿地植物中营养元素含量排序为N＞K＞P,与土壤养分含量排序不一致.不同芦苇湿地的吸收系数排序均为N＞P＞K;芦苇湿地利用系数排序为P＞K＞N;循环系数排序为N＞K＞P.     

喀斯特高原深水水库-万峰湖富营养化特征分析

以贵州喀斯特高原深水水库-万峰湖为例,研究了其夏季和冬季湖沼学和富营养化特征。结果表明:冬季和夏季湖沼学及富营养化特征区别明显,夏季水温,pH值,溶解氧,电导率等指标在10 m和30 m分别出现明显的分界点,而冬季仅在30 m出现分界点,表明夏季属于双分层型,冬季属于单分层型。表层和底层的水温变化幅度较小,底层温度较高,在10℃以上;底层全年处于厌氧状态,钙离子浓度在0.192~0.235 mg/L 之间,镁离子的浓度在0.149~0.196 mg/L 之间,但是垂直和季节变化较小。总磷浓度在0~40 m较低,在0.02~0.04 mg/L之间,底层达0.794 mg/L;总氮浓度在0~40 m维持在2.18~3.54 mg/L,夏季略高于冬季,总氮浓度随深度逐渐降低。根据单因子指数判断,万峰湖大多时期属于中富营养状态。万峰湖不同季节的分层特征,反映出了喀斯特高原深水水库湖沼学变化特征,水体分层影响着水环境因子的变化和水体富营养化程度。     

有“感觉”的智能仿生腿

随着社会的发展科技的进步,人类的假肢制作的材料及工艺较以往有了很大的提高。德国奥托博克公司最近研制的智能仿生腿,使假肢有了“感觉”。 这种智能仿生腿(C-leg)实际上是一个由电脑控制的液压人工膝关节系统。该系统在膝关节及脚部设有受力的传感装置,可将假肢的运动状态以每秒50次的采样频率,向电脑提供信     

贵州红枫湖水体叶绿素a的分布与磷循环

于2009年8月(夏季)和2010年1月(冬季)在贵州红枫湖采集了分层湖水和分层沉积物样品,分析了湖水样品的总N(TN)、总P(TP)及叶绿素a(Chl-a)含量,结果表明,湖水TN含量在2个季节无明显变化,平均含量为1.58±0.73 mg·L-1,湖水TP含量夏季(0.091±0.070 mg·L-1)高于冬季(0.026±0.055 mg·L-1).夏季湖水在8 m处有季节性分层,下层湖水TN、TP含量高于上层;夏季湖水Chl-a主要集中在上层,上层平均含量为33.2±13.0 mg·m-3,冬季湖水Chl-a平均含量为11.1±3.7 mg·m-3,分析发现,湖水上层(8 m)Chl-a与TP有明显的线性相关关系(r=0.965,P＜0.01),表明红枫湖富营养化主要受P元素限制.沉积物孔隙水中的溶解态P(DP)浓度和湖水的磷酸盐(PO3-4-P)浓度比上覆水体高,具有向上扩散的趋势,利用费克第一定律计算了沉积物向上覆水体的释P速率,发现夏季沉积物向上覆水体释P速率高于冬季,可能主要是由于夏季湖水底层的还原环境下沉积物表层的早期成岩作用生成磷酸盐进入孔隙水而促进了沉积物向上覆水体释放P.根据通量释放结果估算了全湖沉积物向水体的释P通量,约为每年5.0±5.6 t.红枫湖富营养化受P控制,沉积物向水体有很大的释放P的潜力,是湖水P的重要内源,严格控制流域的外源输入才能彻底治理该湖的富营养化.     

Implementation of controlling strategy in a biomechanical lower limb model with active muscles for coupling multibody dynamics and finite element analysis

Computational biomechanics for human body modeling has generally been categorized into two separated domains: finite element analysis and multibody dynamics. Combining the advantages of both domains is necessary when tissue stress and physical body motion are both of interest. However, the method for this topic is still in exploration. The aim of this study is to implement unique controlling strategies in finite element model for simultaneously simulating musculoskeletal body dynamics and in vivo stress inside human tissues. A finite element lower limb model with 3D active muscles was selected for the implementation of controlling strategies, which was further validated against in-vivo human motion experiments. A unique feedback control strategy that couples together a basic Proportion-Integration-Differentiation (PID) controller and generic active signals from Computed Muscle Control (CMC) method of the musculoskeletal model or normalized EMG singles was proposed and applied in the present model. The results show that the new proposed controlling strategy show a good correlation with experimental test data of the normal gait considering joint kinematics, while stress distribution of local lower limb tissue can be also detected in real-time with lower limb motion. In summary, the present work is the first step for the application of active controlling strategy in the finite element model for concurrent simulation of both body dynamics and tissue stress. In the future, the present method can be further developed to apply it in various fields for human biomechanical analysis to monitor local stress and strain distribution by simultaneously simulating human locomotion.     

Adaptative control of above knee electro-hydraulic prosthesis.

Conventional designs of an above-knee prosthesis are based on mechanisms with mechanical properties (such as friction, spring and damping coefficients) that remain constant during changing cadence. These designs are unable to replace natural legs due to the lack of active knee joint control. Since the nonlinear and time-varying dynamic coupling between the thigh and the prosthetic limb is high during swing phase, an adaptive control is employed to control the knee joint motion. Two dimensional simulation indicates that the adaptive controller can improve the appearance of gait pattern. It is adaptable to walking speed and can compensate for the variations of hip moment, hip trajectory and toe-off conditions.     

Limb Dominance Results from Asymmetries in Predictive and Impedance Control Mechanisms

Handedness is a pronounced feature of human motor behavior, yet the underlying neural mechanisms remain unclear. We hypothesize that motor lateralization results from asymmetries in predictive control of task dynamics and in control of limb impedance. To test this hypothesis, we present an experiment with two different force field environments, a field with a predictable magnitude that varies with the square of velocity, and a field with a less predictable magnitude that varies linearly with velocity. These fields were designed to be compatible with controllers that are specialized in predicting limb and task dynamics, and modulating position and velocity dependent impedance, respectively. Because the velocity square field does not change the form of the equations of motion for the reaching arm, we reasoned that a forward dynamic-type controller should perform well in this field, while control of linear damping and stiffness terms should be less effective. In contrast, the unpredictable linear field should be most compatible with impedance control, but incompatible with predictive dynamics control. We measured steady state final position accuracy and 3 trajectory features during exposure to these fields: Mean squared jerk, Straightness, and Movement time. Our results confirmed that each arm made straighter, smoother, and quicker movements in its compatible field. Both arms showed similar final position accuracies, which were achieved using more extensive corrective sub-movements when either arm performed in its incompatible field. Finally, each arm showed limited adaptation to its incompatible field. Analysis of the dependence of trajectory errors on field magnitude suggested that dominant arm adaptation occurred by prediction of the mean field, thus exploiting predictive mechanisms for adaptation to the unpredictable field. Overall, our results support the hypothesis that motor lateralization reflects asymmetries in specific motor control mechanisms associated with predictive control of limb and task dynamics, and modulation of limb impedance.     

Identification of isometric contractions based on High Density EMG maps

Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve pattern-recognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it.The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the “isolated masses” method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.     

Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.     

A physiological basis for control of a prosthetic hand

Recent surveys from upper limb amputees indicate the sentiment that prosthetic hands do not function in a life-like manner and are not intuitively controlled. Thus, two methods of control for a prosthetic hand are presented. A proportional derivative (PD) force controller is compared to a novel biomimetic application of sliding mode control. The biomimetic sliding mode (BSM) controller was designed to map human muscle signals into prosthesis motor command signals in a physiologically expected manner.The BSM and PD controllers were evaluated analytically and subjectively by one amputee and nine nonamputee test subjects. The posture of the hands of the nonamputee test subjects were measured with a CyberGlove and used to determine if the position of the prosthesis (when driven by both controllers) was highly correlated to the posture of the human hands. Force tracking experiments were also performed by all test subjects with both controllers to evaluate the ability to control the applied force. Finally, a dual object lifting task was performed by all test subjects to determine if the mapping of electromyogram (EMG) signals with the BSM controller resulted in physiologically expected motions. A nonparametric Mann–Whitney U-test was performed on the subjective evaluations to determine the statistical significance of the evaluations.The BSM controller was shown to replicate the posture of the human hand much more accurately than the PD force controller. The BSM controller also enabled better average force tracking results and higher success rates with the dual object lifting experiment while the same task was nearly impossible to perform with the PD controller. Finally, the BSM controller was subjectively rated to be more similar to control in comparison to the human hand with respect to position and force.     

MODEM: a multi-agent hierarchical structure to model the human motor control system

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a “gate selector” block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).     

Control of constraint forces and trajectories in a rich sensory and actuation environment

A simple control strategy is proposed and applied to a class of non-linear systems that have abundant sensory and actuation channels as in living systems. The main objective is the independent control of constrained trajectories of motion, and control of the corresponding constraint forces. The peripheral controller is a proportional, derivative and integral (PID) controller. A central controller produces, via pattern generators, reference signals that are the desired constrained position and velocity trajectories, and the desired constraint forces. The basic tenet of the this hybrid control strategy is the use of two mechanisms: 1. linear state and force feedback, and 2. non-linear constraint velocity feedback - sliding mode feedback. The first mechanism can be envisioned as a high gain feedback systems. The high gain attribute imitates the agonist-antagonist co-activation in natural systems. The strategy is applied to the control of the force and trajectory of a two-segment thigh-leg planar biped leg with a mass-less foot cranking a pedal that is analogous to a bicycle pedal. Five computational experiments are presented to show the effectiveness of the strategy and the performance of the controller. The findings of this paper are applicable to the design of orthoses and prostheses to supplement functional electrical stimulation for support purposes in the spinally injured cases.     

Phase plane modeling of leg motion

Phase plane analysis of dynamical systems, in which variables are plotted against their time derivatives, has been recently emphasized as a general method for reconstructing system dynamics from data. The purpose of this experiment was to develop a model of leg movement in a stepping task using the phase plane approach. In this model, the leg is represented as a three-body linkage and the motion of the leg is assumed to be planar with four degrees of freedom. Experimental data was collected on one subject stepping six times, using a two dimensional videomotion analysis system with reflective markers placed on the lower limb joints. A computer program able to solve the equations of motion and compute the state of the system for a given task was implemented. This computer program was written to generate the motion of the leg for a given task using inverse kinematics and a preplanned foot path. Foot trajectories with cycloidal, constant acceleration/deceleration and sinusoidal velocity profiles were studied. From the results, an attempt was made to identify the variables which are measured and to determine the motion characteristics during stepping. The preliminary results support the concept of a hierarchical control structure with openloop control during normal operation. During routine activity there is no direct intervention of the Central Nervous System (CNS). The results support the existence of preprogramming and provide a starting point for the study of the development of control in multiarticulate movements.     

Upper limb H reflexes and somatosensory evoked potentials modulated by movement.

In the human lower limb, the magnitudes of both Hoffmann (H) reflexes and primary somatosensory evoked potentials (SEPs) from scalp electrodes, are reduced by active and/or passive movement. We surmised that similar effects occur for the upper limb and specifically hypothesised that amplitudes of median nerve induced flexor carpii radialis H reflexes and cortical SEPs are reduced with passive movement about the wrist or elbow. The results showed (P<0. 05) that either movement significantly attenuated mean magnitudes of SEPs elicited from stimulation at elbow or wrist and that reflex magnitudes attenuated with wrist movement. Thus, the upper limb shows similar movement-induced modulation to the lower limb. These attenuations of fast conducting sensory paths consequent to movement per se, may be a basic level of motor control, initiated from muscle mechanoreceptor discharge. Upon this basic level, more complex modulations then may be laid as appropriate for the particular characteristics of active motor tasks.