An Anthropomorphic Robot Hand Developed Based on Underactuated Mechanism and Controlled by EMG Signals

When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a highdexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into account to fully accomplishthe actuation tasks.This paper presents a novel humanoid robotic myocontrol hand (AR hand Ⅲ) which adopted an underac-tuated mechanism and a forearm myocontrol EMG method.The AR hand Ⅲ has five fingers and 15 joints,and actuated by threeembedded motors.Underactuation can be found within each finger and between the rest three fingers (the middle finger,the ringfinger and the little finger) when the hand is grasping objects.For the EMG control,two specific methods are proposed:thethree-fingered hand gesture configuration of the AR hand Ⅲ and a pattern classification method of EMG signals based on astatistical learning algorithm-Support Vector Machine (SVM).Eighteen active hand gestures of a testee are recognized ef-fectively,which can be directly mapped into the motions of AR hand Ⅲ.An on-line EMG control scheme is established basedon two different decision functions:one is for the discrimination between the idle and active modes,the other is for the recog-nition of the active modes.As a result,the AR hand Ⅲ can swiftly follow the gesture instructions of the testee with a time delayless than 100 ms.     

Surface EMG in advanced hand prosthetics

One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible. This work is partially supported by the project NEURObotics, FP6-IST-001917.     

Hand function: peripheral and central constraints on performance.

The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.     

Matching object size by controlling finger span and hand shape

The ability of human subjects to accurately control finger span (distance between thumb and one finger) was studied. The experiments were performed without visual feedback of the hand and were designed to study the dependence of accuracy on object size, shape, distance, orientation and finger configuration. The effects of finger combination and sensory modality used to perceive object size (vision and haptics) were also studied. Subjects were quite proficient at this task; the small errors tended to be predominantly negative, i.e., finger span object size. The thumb-little finger combination was less accurate than the other finger combinations, irrespective of the sensory modality used. Subjects made larger under-estimating errors when matching the size of cylinders than when matching cubes and parallelepipeds. No effect of viewing distance, object orientation and finger configuration was found. Accuracy in matching object size was not dependent on the sensory modality used. The question of how the individual degrees of freedom of the fingers and thumb contributed to the control of finger span was also addressed. Principal components analysis showed that two components could characterize the hand postures used, irrespective of object size. The amplitude of the first principal component was constant, and the amplitude of the second scaled linearly with object size. This finding suggests that all of the degrees of freedom of the hand are controlled as a unit. This result is discussed in relation to the 'virtual finger' hypothesis for grasping.     

Two coupled oscillators as a model for the coordinated finger tapping by both hands

Recently, it was found that rhythmic movements (e.g. locomotion, swimmeret beating) are controlled by mutually coupled endogeneous neural oscillators (Kennedy and Davis, 1977; Pearson and Iles, 1973; Stein, 1974; Shik and Orlovsky, 1976; Grillner and Zangger, 1979). Meanwhile, it has been found out that the phase resetting experiment is useful to investigate the interaction of neural oscillators (Perkel et al., 1963; Stein, 1974). In the preceding paper (Yamanishi et al., 1979), we studied the functional interaction between the neural oscillator which is assumed to control finger tapping and the neural networks which control some tasks. The tasks were imposed on the subject as the perturbation of the phase resetting experiment. In this paper, we investigate the control mechanism of the coordinated finger tapping by both hands. First, the subjects were instructed to coordinate the finger tapping by both hands so as to keep the phase difference between two hands constant. The performance was evaluated by a systematic error and a standard deviation of phase differences. Second, we propose two coupled neural oscillators as a model for the coordinated finger tapping. Dynamical behavior of the model system is analyzed by using phase transition curves which were measured on one hand finger tapping in the previous experiment (Yamanishi et al., 1979). Prediction by the model is in good agreement with the results of the experiments. Therefore, it is suggested that the neural mechanism which controls the coordinated finger tapping may be composed of a coupled system of two neural oscillators each of which controls the right and the left finger tapping respectively.     

Understanding finger coordination through analysis of the structure of force variability

 Most common motor acts involve highly redundant effector systems. Understanding how such systems are controlled by the nervous system is a long-standing scientific challenge. Most proposals for solving this problem are based on the assumption that a particular solution, which optimizes additional constraints, is selected by the nervous system out of the many possible solutions. This study attempts to address this question in the context of coordinating individual finger forces to produce a controlled total force oscillation between 5% and 35% of each subject's maximum force of voluntary contraction, under two different combinations of four fingers. The structure of variability of individual finger forces was evaluated with respect to hypotheses that, at each instance in time, subjects attempt to: (1) stabilize the value of total force and (2) stabilize the total moment created by the fingers about the long axis passing through the forearm and midline of the hand. The results provide evidence that a range of goal-equivalent finger force combinations is generated to stabilize the values of total force and the total moment. The control of total force was specified explicitly by the task. However, it was stabilized only near the time of peak force. In contrast, the total moment was stabilized throughout most of the force cycle. The results lead to the suggestion that successful task performance is achieved, not by selecting a single optimal solution, but by discovering an appropriate control law that selectively stabilizes certain combinations of degrees of freedom relevant to the task while releasing from control other combinations. Received: 2 February 2001 / Accepted in revised form: 21 June 2001     

Fully decentralized control of a soft-bodied robot inspired by true slime mold

Animals exhibit astoundingly adaptive and supple locomotion under real world constraints. In order to endow robots with similar capabilities, we must implement many degrees of freedom, equivalent to animals, into the robots’ bodies. For taming many degrees of freedom, the concept of autonomous decentralized control plays a pivotal role. However a systematic way of designing such autonomous decentralized control system is still missing. Aiming at understanding the principles that underlie animals’ locomotion, we have focused on a true slime mold, a primitive living organism, and extracted a design scheme for autonomous decentralized control system. In order to validate this design scheme, this article presents a soft-bodied amoeboid robot inspired by the true slime mold. Significant features of this robot are twofold: (1) the robot has a truly soft and deformable body stemming from real-time tunable springs and protoplasm, the former is used for an outer skin of the body and the latter is to satisfy the law of conservation of mass; and (2) fully decentralized control using coupled oscillators with completely local sensory feedback mechanism is realized by exploiting the long-distance physical interaction between the body parts stemming from the law of conservation of protoplasmic mass. Simulation results show that this robot exhibits highly supple and adaptive locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on design methodology for autonomous decentralized control system.     

Local thermal sensation and finger vasoconstriction in the locally heated hand

The effects of local heating on finger blood flow (BF) and local thermal sensation (Sensw) were studied. Finger BFs in both hands were measured simultaneously; one hand was immersed in water the temperature (Tw) of which was raised from 35 degrees C to 43 degrees C by steps of 2 degrees C every 10 min, while the other hand was kept at Tw 35 degrees C. Finger BF in the locally heated hand decreased at Tw 37 to 41 degrees C, while finger BF in the control hand did not alter. Sensw in the heated hand showed a dynamic response, initially increasing concomitantly with an increase in Tw, then gradually returning and adapting to a new level of Sensw. The dynamic response of Sensw was not perceived during mental calculation even when Tw was raised to 40 degrees C, and the reduction in finger blood flow was not observed. These results suggest that finger vasoconstriction caused by local heating closely relates to the dynamic response characteristic of local thermal sensation at Tw above core temperature, and that the perception of local thermal sensation in the central nervous system is involved in the mechanism of this vasoconstrictor response.     

Application of EMG signals for controlling exoskeleton robots.

Exoskeleton robots are mechanical constructions attached to human body parts, containing actuators for influencing human motion. One important application area for exoskeletons is human motion support, for example, for disabled people, including rehabilitation training, and for force enhancement in healthy subjects. This paper surveys two exoskeleton systems developed in our laboratory. The first system is a lower-extremity exoskeleton with one actuated degree of freedom in the knee joint. This system was designed for motion support in disabled people. The second system is an exoskeleton for a human hand with 16 actuated joints, four for each finger. This hand exoskeleton will be used in rehabilitation training after hand surgeries. The application of EMG signals for motion control is presented. An overview of the design and control methods, and first experimental results for the leg exoskeleton are reported.     

Evidence of muscle synergies during human grasping

Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.     

Estimation of the effective static moment arms of the tendons in the index finger extensor mechanism

A novel technique to estimate the contribution of finger extensor tendons to joint moment generation was proposed. Effective static moment arms (ESMAs), which represent the net effects of the tendon force on joint moments in static finger postures, were estimated for the 4 degrees of freedom (DOFs) in the index finger. Specifically, the ESMAs for the five tendons contributing to the finger extensor apparatus were estimated by directly correlating the applied tendon force to the measured resultant joint moments in cadaveric hand specimens. Repeated measures analysis of variance revealed that the finger posture, specifically interphalangeal joint angles, had significant effects on the measured ESMA values in 7 out of 20 conditions (four DOFs for each of the five muscles). Extensor digitorum communis and extensor indicis proprius tendons were found to have greater MCP ESMA values when IP joints are flexed, whereas abduction ESMAs of all muscles except extensor digitorum profundus were mainly affected by MCP flexion. The ESMAs were generally smaller than the moment arms estimated in previous studies that employed kinematic measurement techniques. Tendon force distribution within the extensor hood and dissipation into adjacent structures are believed to contribute to the joint moment reductions, which result in smaller ESMA values.     

Flexibility and Repeatability of Finger Movements During Typing: Analysis of Multiple Degrees of Freedom

The kinematics of the hand and fingers were studied during various keystrokes in typing. These movements were defined by 17 degrees of freedom of motion, and methods were developed to identify simplifying strategies inthe execution of the task. Most of the analysis was restrictedto the 11 degrees of freedom of the fingers, neglecting thumband wrist motion. Temporal characteristics of the motion weredefined by computing principal components, and it was found thatonly a few (two to four) principal components were needed tocharacterize motion of each of the degrees of freedom.Hierarchical relationships among patterns within and betweendifferent degrees of freedom were identified using clusteranalysis. There was a considerable amount of consistency eachtime a given keystroke was executed by a subject, and thisrepeatability may imply a reduction in the number of degrees offreedom independently controlled by the nervous system. However,there also appears to be considerable flexibility in thecoordination of the many joints of the hand when examined acrossdifferent keys and across different subjects.     

Virtual human hand: model and kinematics

The human hand plays an important role in daily life. It is the interface between the human and the exterior world by positioning, orienting, touching and grasping objects. The human hand has multiple degrees of freedom (DOFs) to enable mobility and dexterity. A virtual human hand model can be inserted into CAD (Computer Aided Design) models to assess the manipulation capabilities in the early design stage to reduce design time and cost. Joystick assessment is one of the important design cases. This study is a first step towards a comprehensive hand simulation tool to simulate the manipulation and grasping of objects. This paper presents a novel 25 DOFs' hand skeletal model based on hand anatomy and hand kinematics: (1) joint range of motion, (2) Denavit–Hartenberg method to define the joint relationship and (3) finger workspace determination. Novelty for this hand model includes arching the palm with the four DOFs added in the carpometacarpal and wrist joints for the ring and small fingers.     

How Variability and Effort Determine Coordination at Large Forces

Motor control is a challenging task for the central nervous system, since it involves redundant degrees of freedom, nonlinear dynamics of actuators and limbs, as well as noise. When an action is carried out, which factors does your nervous system consider to determine the appropriate set of muscle forces between redundant degrees-of-freedom? Important factors determining motor output likely encompass effort and the resulting motor noise. However, the tasks used in many previous motor control studies could not identify these two factors uniquely, as signal-dependent noise monotonically increases as a function of the effort. To address this, a recent paper introduced a force control paradigm involving one finger in each hand that can disambiguate these two factors. It showed that the central nervous system considers both force noise and amplitude, with a larger weight on the absolute force and lower weights on both noise and normalized force. While these results are valid for the relatively low force range considered in that paper, the magnitude of the force shared between the fingers for large forces is not known. This paper investigates this question experimentally, and develops an appropriate Markov chain Monte Carlo method in order to estimate the weightings given to these factors. Our results demonstrate that the force sharing strongly depends on the force level required, so that for higher force levels the normalized force is considered as much as the absolute force, whereas the role of noise minimization becomes negligible.     

An open-source model and solution method to predict co-contraction in the finger

A novel open-source biomechanical model of the index finger with an electromyography (EMG)-constrained static optimization solution method are developed with the goal of improving co-contraction estimates and providing means to assess tendon tension distribution through the finger. The Intrinsic model has four degrees of freedom and seven muscles (with a 14 component extensor mechanism). A novel plugin developed for the OpenSim modelling software applied the EMG-constrained static optimization solution method. Ten participants performed static pressing in three finger postures and five dynamic free motion tasks. Index finger 3D kinematics, force (5, 15, 30 N), and EMG (4 extrinsic muscles and first dorsal interosseous) were used in the analysis. The Intrinsic model predicted co-contraction increased by 29% during static pressing over the existing model. Further, tendon tension distribution patterns and forces, known to be essential to produce finger action, were determined by the model across all postures. The Intrinsic model and custom solution method improved co-contraction estimates to facilitate force propagation through the finger. These tools improve our interpretation of loads in the finger to develop better rehabilitation and workplace injury risk reduction strategies.     

Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production

Determining tendon tensions of the finger muscles is crucial for the understanding and the rehabilitation of hand pathologies. Since no direct measurement is possible for a large number of finger muscle tendons, biomechanical modelling presents an alternative solution to indirectly evaluate these forces. However, the main problem is that the number of muscles spanning a joint exceeds the number of degrees of freedom of the joint resulting in mathematical under-determinate problems. In the current study, a method using both numerical optimization and the intra-muscular electromyography (EMG) data was developed to estimate the middle finger tendon tensions during static fingertip force production. The method used a numerical optimization procedure with the muscle stress squared criterion to determine a solution while the EMG data of three extrinsic hand muscles serve to enforce additional inequality constraints. The results were compared with those obtained with a classical numerical optimization and a method based on EMG only. The proposed method provides satisfactory results since the tendon tension estimations respected the mechanical equilibrium of the musculoskeletal system and were concordant with the EMG distribution pattern of the subjects. These results were not observed neither with the classical numerical optimization nor with the EMG-based method. This study demonstrates that including the EMG data of the three extrinsic muscles of the middle finger as inequality constraints in an optimization process can yield relevant tendon tensions with regard to individual muscle activation patterns, particularly concerning the antagonist muscles.     

Dynamic coordinate data for describing muscle-tendon paths: a mathematical approach

When modelling the musculoskeletal system over a range of joint angles the use of fixed points to describe muscle-tendon paths has inherent limitations. These result in fewer deflection points and the use of effective insertions to accommodate both relative marker movement and avoid muscle paths contacting bony structures. Model performance is dependent on the joint angle relative to the anatomical position where the muscle-tendon paths were defined. The present study proposes a scheme for the implementation of dynamic coordinates for describing muscle-tendon paths. For each muscle-tendon element a plane is defined in which the muscle-tendon complex acts when crossing a given joint. The muscle-tendon plane is dependent on 3D segment orientations and describes one degree of freedom, while the remaining two degrees of freedom are described by polar coordinates and locate the dynamic point in the muscle-tendon plane. The dynamic approach is implemented on four muscles of the lower limb in modelled and simulated joint movements and offers a significant improvement on previous approaches based on fixed deflection points. The scheme accommodates compound 3D rotations about joint axes, is not computationally difficult or require large data sets, and does not impose limitations on the number of points that may be defined along a muscle-tendon path.     

A method to predict muscle control in the kinematically and mechanically indeterminate human masticatory system

A method is proposed to generate muscle activation patterns for goal-directed movements of the human masticatory system. This system is special because apart from a larger amount of muscles than degrees of freedom its joints do not restrict its movements a priori. Therefore, each muscle is able to influence all six degrees of freedom which makes the system kinematically and mechanically indeterminate. Furthermore, its working space is principally determined by the dynamical properties of its muscles and not by passive constraints.The presented method determines the contribution of each degree of freedom to a movement of a reference point on the mandible. It avails of straightforward mathematical techniques like Linear Programming. It does not require a separate trajectory planning step. It was applied in a six degrees of freedom dynamical mathematical model of the human masticatory system. This model which was based upon rigid-body dynamics incorporating skull morphology and muscle architecture including dynamical properties. Movements were exclusively defined by a goal position of the mandibular reference point.The method proved to be robust in generating muscle activation patterns for both feasible and infeasible movement tasks. Generally, they were accomplished faster than habitually observed. If the task was infeasible the movement stopped at the outer boundary of the working space at the side of the unreachable goal. The method, therefore, enables to explore the working space of the mandible and the factors that are relevant for its boundaries.     

Classification of finger movements by using the ultra-wide band radar

The coding system of finger movements depends on the differences in the characteristics of the muscles that are responsible for these movements. The ability of ultra-wide band (UWB) radar for use as a tool for identifying the movements of each finger is presented. This will facilitate the ability of the UWB radar in designing a coding system for the movement of fingers of each hand.