A comparison of forearm and thumb muscle electromyographic responses to the use of laparoscopic instruments with either a finger grasp or a palm grasp

Laparoscopic techniques allow for less-invasive treatment of common surgical problems. Laparoscopic instruments are different from standard surgical instruments and generally incorporate a pistol-grip handle configuration with rings for the fingers. This handle configuration has been reported as being uncomfortable, leading to finger compression neuropathies in some cases. As an alternative, the surgeon can choose to grasp laparoscopic instruments using a more powerful palm grip during grasping motions. This study evaluates the hypothesis that the use of the palm grip requires less muscle tension than the finger-grip when grasping with laparoscopic instruments. Nine general surgeons used an Autosuture laparoscopic grasper with a ringed pistol-grip handle held in both a finger-in-ring (F) or palm (P) hand grip position to grasp and close two spring-loaded metal plates. The same task was performed with a surgical haemostat clamp (H) for comparison. Each subject performed the grasping task in a random sequence for the three instrument configurations at two grasping forces levels (0.7 and 4.2 N), and with the instrument at three angles to the subjects' sagittal plane (0 degree, 45 degrees and 90 degrees). Surface electromyographic (EMG) signals were acquired from the flexor carpi ulnaris (FCU), flexor digitorum profundus (FDP), flexor digitorum superficialis (FDS), extensor carpi ulnaris (ECU), extensor digitorum comunis (EDC) and the thenar compartment (TH). The peak root mean squared (RMS) EMG voltage was averaged for five repetitions at each instrument, force and angle condition. Statistical analysis was carried out by repeated measures ANOVA. The muscle EMG RMS amplitude while using the palm grip was decreased in the FDS, TH and EDC, was unchanged in the ECU and FCU, and was slightly higher in the FDP when compared with the finger grip. These differences were most prominent at 90 degrees to the sagittal plane where the subjects' wrists neared maximal flexion. It is concluded that the palm grip is more powerful than the finger grip when grasping with laparoscopic instruments, particularly at angles perpendicular to the surgeon's sagittal plane.     

基于自更新混合分类模型的肌电运动识别方法

传统基于肌电（Electromyography,EMG）的运动识别方法多是利用训练后的固定参数模型,分类已预先定义的有限个目标动作,但对肌肉疲劳导致的肌电变化,以及未定义的外部动作等干扰因素无能为力.针对这一问题,提出一种自更新混合分类模型（Self-update hybrid classification model,SUHC）,该模型融合了用于排除外部动作干扰的一类支持向量机（Support vector machine,SVM）,以及用于分类目标动作数据的多类线性判别分析（Linear discriminant analysis,LDA）,并引入自更新机制以对抗肌电时变性干扰.通过手部动作识别实验验证提出方法的效果,在肌电大幅变化干扰下,SUHC的目标动作识别精度达到89%,对比传统的支持向量机、多层感知器（Multiple layer perceptron,MLP）和核线性判别分析（Kernel LDA,KLDA）,提高了约18%,并且SUHC具备排除外部动作干扰能力,排除精度高达93%.     

Dexterous motion recognition for myoelectric control of multifunctional transradial prostheses

Attributed to its superior dexterity, the human’s thumb plays a significant role in the activities of daily living (ADLs). In order to promote the operational capability of the prosthetic hand, state-of-the-art designs tend to integrate multiple degrees of freedom (DOFs) in thumb’s trapeziometacarpal (TM) joint for achieving versatile movements. However, without proper control methods, this fine dexterity of multi-DOF prosthetic hand is hard to be demonstrated. This problem becomes more serious when the control signals, electromyography (EMG), are highly limited on the residual stump of the amputee. Through experiments, the paper examines the feasibility of recognizing 4 thumb motions (flexion/extension and abduction/adduction) together with the other 12 finger- and wrist-related motions under a general surface EMG configuration. After surveying the classification accuracy of several selective motion groups (thumb motions, thumb-involved finger motions, thumb-involved wrist motions, etc.), we report here the current limitations of the prevalent pattern recognition-based EMG control schemes. Considering the severely impaired neuromuscular systems of the amputees, controlling those thumb motions for achieving dexterous operations still faces many challenges. We finally discuss several alternative strategies, including switching, extended physiological proprioception (EPP) and postural synergy, for dealing with this problem.     

Decentralised control of continuous Petri nets

This paper focuses on decentralised control of systems modelled by continuous Petri nets, in which a target marking control problem is discussed. In some previous works, an efficient ON/OFF strategy-based minimum-time controller was developed. Nevertheless, the convergence is only proved for subclasses like Choice-Free nets. For a general net, the pre-conditions of applying the ON/OFF strategy are not given; therefore, the application scope of the method is unclear. In this work, we provide two sufficient conditions of applying the ON/OFF strategy-based controller to general nets. Furthermore, an extended algorithm for general nets is proposed, in which control laws are computed based on some limited information, without knowing the detailed structure of subsystems.     

Electromyographic prosthetic hand using grasping-force-magnification mechanism with five independently driven fingers

This paper proposes an electromyographic (EMG) prosthetic hand that has five independently driven fingers, a flexion drive, and a force-magnification drive. The flexion drive allows for rapid finger motion, and the force-magnification drive allows for a firm grasp. To realize the natural feeling of control similar to that of movements with nonamputated parts, the control system includes the impedance model of human forearms and utilizes the muscle contraction level extracted from a user’s EMG signals. We experimentally verified that the maximum fingertip force of the hand exceeds 20 N, and the time required to fully close the hand by the flexion drive is 0.53 s. We also experimentally verified that in response to EMG signals, the fingers can flex smoothly and the grasping force can be modulated. Furthermore, we show that taking EMG signals as inputs makes it possible to control six operations, including ones that use the five fingers in distinctive ways.     

3D-printed membrane microvalves and microdecoder

A microfluidic system for multichannel switching and multiphase flow control has potential uses in pneumatic soft robotics and biological sampling systems. At present, the membrane microvalves used in microfluidic systems are mostly constructed using a multilayer bonding process so that the device cannot withstand high pressures. In this paper, we demonstrate a design method and the properties of a bondless membrane microvalve fabricated using a commercial 3D printer. We used a multijet (MJP) 3D printer to print a 100-μm-thick and 6-mm-diameter membrane from a relatively hard material (1700 MPa). The membrane’s high toughness ensures that it does not need negative pressure to reopen. The measured operation frequency was less than 2.5 Hz under a pneumatic pressure of 14.5 kPa. We also 3D-printed an integrated Quake-style microfluidic decoder network by combining 8 valves in series to demonstrate the integrability of the microvalve. The decoder chip was demonstrated to control the ON/OFF state of the four coded fluidic channels, with the droplets being generated from selected channels according to the valve action. Therefore, such 3D-printed microvalves are highly integrable, have a high manufacturing efficiency, and can be applied in pneumatic controllers, sample switchers and integrated print heads.

     

State of the Art in Hand and Finger Modeling and Animation

The human hand is a complex biological system able to perform numerous tasks with impressive accuracy and dexterity. Gestures furthermore play an important role in our daily interactions, and humans are particularly skilled at perceiving and interpreting detailed signals in communications. Creating believable hand motions for virtual characters is an important and challenging task. Many new methods have been proposed in the Computer Graphics community within the last years, and significant progress has been made towards creating convincing, detailed hand and finger motions. This state of the art report presents a review of the research in the area of hand and finger modeling and animation. Starting with the biological structure of the hand and its implications for how the hand moves, we discuss current methods in motion capturing hands, data‐driven and physics‐based algorithms to synthesize their motions, and techniques to make the appearance of the hand model surface more realistic. We then focus on areas in which detailed hand motions are crucial such as manipulation and communication. Our report concludes by describing emerging trends and applications for virtual hand animation.     

间歇式化工系统的Petri网优化调度与控制方法

为了解决复杂间歇式化工系统的优化调度和控制问题,提出了一种基于Petri网的优化调度与控制方法:首先,根据加工工艺,建立加工过程的赋时Petri网模型;其次,根据间歇式化工生产对象的拓扑结构,在该赋时Petri网模型中引入阀门系统的网结构,从而获得系统的受控Petri网模型;最后,利用可达图来计算加工时间最短的控制策略,借助网结构信息,得到了控制策略的阀门控制矩阵,并用一个示例演示验证了本文方法.     

基于细径McKibben型气动人工肌肉的仿生手研发

以细径McKibben型气动人工肌肉作为执行器,研发了一种质量轻且具有良好柔顺性、灵巧性的仿生机械手.分别以直径为1.3mm和2mm的细径McKibben型人工肌肉模拟人手的内在肌和外在肌,以人手骨骼、关节、肌肉的生理构造为设计准则,设计并制作仿生手样机.该样机由35根人工肌肉驱动,与人手大小相仿,质量小于0.5 kg.通过实验,对该仿生手的运动性能、目标抓握能力和鲁棒性进行验证,该仿生手可以完成与人手相近的动作.     

A finger motion model for reach and grasp

This study aimed to develop a model that describes human finger motion for simulation of reach and grasp for selected objects and tasks. Finger joint angles and timing of their changes were measured for six subjects as they reached 20-40 cm and grasped cylindrical handles (1.3-10.2 cm D) of varying orientation (vertical/axial). The empirical results from multiple regression analyses served as inputs to allow a fourth order polynomial to predict motion of each finger joint. The proposed model showed good fit with observations, with high coefficients of determination from 0.54 to 1 and reasonable errors from 0.04° to 5.44° for all conditions considered. The proposed finger motion model was implemented in an existing kinematic hand model to employ a contact algorithm for refined prediction of grip posture and to illustrate its predictive power by graphically displaying the opening and closing of the hand.

Relevance to industry

Finger joint motions during reach and grasp are needed for prediction of (1) tendon excursions for study of work-related musculoskeletal disorders, (2) required space for the hand, (3) finger locations on work objects, and (4) hand grip postures and strength.     

High-fidelity sliding mode control of a pneumatic haptic teleoperation system

For a pneumatic teleoperation system with on/off solenoid valves, sliding mode control laws for position and force ensuring low switching (open/close) activity of the valves are developed. Since each pneumatic actuator has two pneumatic chambers with a total of four on/off valves, 16 possible combinations (‘operating modes’) for the valves’ on/off positions exist, but only seven of which are both functional and unique. While previous work has focused on three-mode sliding-based position control of one pneumatic actuator, this paper develops the seven-mode sliding-based bilateral control of a teleoperation system comprising a pair of pneumatic actuators. The proposed bilateral sliding control schemes are experimentally validated on a pair of actuators utilizing position-position and force-position teleoperation architectures. The results demonstrate that leveraging the additional modes of operation leads to more efficient and smoother control of the pneumatic teleoperation system. It was found that viscous friction forces were crippling haptic feedback in the position-position architecture. Through the use of force sensors, the force-position architecture was able to compensate for the heavy viscous friction forces.     

基于小波变换和样本熵的假手肌电模式识别方法

提出一种新的模式分类器，利用安置在拇长屈肌、指深屈肌和指伸肌上的3个电极所测得的肌电信号,实现了对3自由度假手手指运动的控制．该分类器采用小波变换和样本熵的方法构造特征矢量．经过由变学习速率算法和RP算法构建的集成3层前馈神经网络的分类，能够成功地分辨出拇指、食指和中指的弯曲与伸展运动，平均识别率可达96％以上．实验结果表明，该分类器为多自由度肌电假手的控制提供了一种有效的方法．     

Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

A fundamental component of many modern prostheses is the myoelectric control system, which uses the electromyogram (EMG) signals from an individual’s muscles to control the prosthesis movements. Despite the extensive research focus on the myoelectric control of arm and gross hand movements, more dexterous individual and combined fingers control has not received the same attention. The main contribution of this paper is an investigation into accurately discriminating between individual and combined fingers movements using surface EMG signals, so that different finger postures of a prosthetic hand can be controlled in response. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG data from eight participants. Various feature sets are extracted and projected in a manner that ensures maximum separation between the finger movements and then fed to two different classifiers. The second contribution is the use of a Bayesian data fusion postprocessing approach to maximize the probability of correct classification of the EMG data belonging to different movements. Practical results and statistical significance tests prove the feasibility of the proposed approach with an average classification accuracy of ≈90% across different subjects proving the significance of the proposed fusion scheme in finger movement classification.     

Sliding-mode bilateral teleoperation control design for master–slave pneumatic servo systems

This paper presents a novel bilateral control design scheme for pneumatic master–slave teleoperation systems that are actuated by low-cost solenoid valves. The motivation for using pneumatic actuators in lieu of electrical actuators is that the former has higher force to mass ratio than the latter and is inert to magnetic fields, which is crucial in certain teleoperation applications such as MRI-guided, robot-assisted surgery. A sliding mode approach, called the three-mode control scheme, is incorporated into a two-channel bilateral teleoperation architecture, which can implement a position–position, force–force, or force–position scheme. An analysis of stability and transparency of the closed-loop teleoperation system is carried out. The proposed control design performance is experimentally verified on a single-degree-of-freedom pneumatic teleoperation system actuated by on/off valves. Experimental results show high accuracies in terms of position and force tracking under free-space motion and hard-contact motion in the teleoperation system. Another purpose of this paper is to demonstrate the possibility to improve the valve lifetime by increasing the number of control levels. To do this, a new control design, called the five-mode control scheme, is developed and compared with the three-mode scheme in time domain as well as in frequency domain.     

Design of optimum grip and control area for one-handed manual control devices

The objective of this paper was to make a design specification of the control area in dual tasks of the hand grip and manual control. The grip postures were analyzed for three types of hand tools. An experiment was performed to measure the position of the fingers and the maximum finger forces at 4 different postures for nine subjects. It was found out that the finger forces were significantly affected by the subjects, the fingers, and the grip postures. The maximum force of women was 62% of men's. From the experiment, the primary control area was defined as 10–13cm and the secondary control area as 8–12cm from the wrist origin. The preferred hand posture of the index and the middle fingers was found to be 3045 degrees at metacarpophalangeal joint and 4050 degrees at proximal interphalangeal joint. It was also found out that the design of one-handed manual control devices should include the characteristics of the user, grip posture, finger force, and the control arrangement.     

A modified direct adaptive robust motion trajectory tracking controller of a pneumatic system

In this study, we developed and tested a high-precision motion trajectory tracking controller of a pneumatic cylinder driven by four costless on/off solenoid valves rather than by a proportional directional control valve. The relationship between the pulse width modulation （PWM） of a signal＇s duty cycle and control law was determined experimentally, and a mathematical model of the whole system established. Owing to unknown disturbances and unmodeled dynamics, there are considerable uncertain nonlinearities and parametric uncertainties in this pneumatic system. A modified direct adaptive robust controller （DARC） was constructed to cope with these issues. The controller employs a gradient type adaptation law based on discontinuous projection mapping to guarantee that estimated unknown model parameters stay within a known bounded region, and uses a deterministic robust control strategy to weaken the effects of unmodeled dynamics, disturbances, and parameter estimation errors. By using discontinuous projection mapping, the parameter adaptation law and the robust control law can be synthesized separately. A recursive backstepping technology is applied to account for unmatched model uncertainties. Kalman filters were designed sepa- rately to estimate the motion states and the derivative of the intermediate control law in synthesizing the deterministic robust control law. Experimental results illustrate the effectiveness of the proposed controller.     

基于改进型BP神经网络的手部动作识别

对手部动作进行模式识别,首先将采集到的肌电信号进行降噪处理,选择时域分析法中的方差算法对采集信号进行特征提取。将特征信号进行归一化处理,实验发现普通BP神经网络分类器出现学习速率慢,泛化能力较差,不同动作识别准确率差别较大等问题。针对以上问题,提出了一种改进型BP神经网络,将神经网络输入数据进行人工升维处理,并对网络学习速率慢的原因进行理论推导,然后引入交叉熵代价函数并对其进行正则化处理,以提高网络的泛化能力以及网络的识别准确率。实验结果表明,改进型BP神经网络的学习速率、泛化能力以及动作分类的准确率均优于普通网络,识别准确率平均为94.34%。     

运载火箭电磁阀判读算法设计

针对运载火箭领域中出现的传统电磁阀,提出了只截取包含开启时间和关闭时间内特征参数的部分的截取拼接算法,实现传统电磁阀的波形显示和判读。对于发生器点火器和推力室点火器等新型电磁阀,针对它们的特性对算法进行修改,从而实现新型电磁阀的波形显示和判读。在实验室环境下进行了电磁阀信号采集实验,使用电磁阀自动判读软件完成了波形绘制,并对开启时间、关闭时间、稳态电流等特征参数进行判读,结果表明：该算法可以满足运载火箭领域常见的传统电磁阀的判读要求,也适用于出现的新型电磁阀。     

基于分数Langevin方程的通信量模型及其应用

建立了一个考虑QoS和多流复用的通信量模型。模型中的通信量由大量有相同源和目的地址的,独立同分布的数据流汇聚而成,其中每个数据流又由许多独立的符合Pareto分布的ON/OFF支流构成。文章首先分析了支流构成的数据流的概率分布,然后分析得到了数据流复用后的流速的概率分布,接着结合分数Langevin运动方程,得到了一个流量计算公式,该公式属于扩展的分数布朗运动。通过引入分数Langevin运动方程可以将外部影响因子,内部影响因子和Hurst参数有机地联系起来,给通信量的单分形和多重分形一个清晰合理的物理解释。最后使用该模型分析了VoIP中多流复用后的QoS参数,得到了几个重要结论。     

Experimental Study of an EMG-Controlled 5-DOF Anthropomorphic Prosthetic Hand for Motion Restoration

In this paper, we attempted to evaluate the performance of an electromyography (EMG)-controlled 5-DOF prosthetic hand on ten transradial amputees. The proposed prosthesis is composed of a five-fingered hand, a passive wrist, and a customized socket for each subject. The EMG control methods included both a commonly used pattern recognition-based scheme (DD-SVM) and a novel digital encoding strategy (double-channel template matching (DCTM)). A virtual 3D hand platform was developed for training the subjects and rapidly testing the control methods. For each subject, the performance of the EMG control methods was firstly measured by off-line classification accuracy; then, according to the accuracy, a particular control method was selected and embedded in the EMG controller for further validation on ordinary daily life activities. Our experiments were conducted to test not only the hand’s grasp ability but also other multifinger cooperation skills. The result indicated that the subjects of rich control experience can accomplish several intuitive motion control over their hands. However, the kinds of the motions and their relative recognition accuracy may depend on some individual differences, such as the amputation level, the activity of the residual nerve-muscle system, and the richness of control experience. Meanwhile, the proposed digital encoding method, DCTM, which only utilized two channels of EMG, was necessary for those amputees with few available control signals. This paper suggested that the EMG control method should be differently considered according to the particular condition of each subject.