Human Prehension Research Articles

A Wearable Haptic Device Based on Twisting Wire Actuators for Feedback of Tactile Pressure Information

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/12.jpg"" width=""300"" /> A wearable haptic device</div> This paper presents a novel wearable haptic device that provides the user with knowledge of a vertical force, measured at the fingertips, by applying pressure at three different locations on the user’s body. Human prehension and manipulation abilities rely on the ability to convert tactile information into controlled actions, such as the regulation of gripping force. Current upper-limb prosthetics are able to partially replicate the mechanical functions of the human hand, but most do not provide any sensory information to the user. This greatly affects amputees, as they must rely solely on their vision to perform grasping actions. Our device uses a twisted wire actuator to convert rotational motion into linear displacement, which allows the device to remain compact and light-weight. In the past, the main shortcoming of this type of actuator was its limited linear range of motion; but with a slight modification of the principle, we have extended our actuator’s linear range of motion by 40%. In this paper, we present the design of our haptic device, the kinematic and dynamic modelling of the actuator, and the results of the experiments that were used to validate the system’s functionality. </span>

The Pransky interview: Dr Robert Ambrose, Chief, Software, Robotics and Simulation Division at NASA

Purpose – This paper, a “Q & A interview” conducted by Joanne Pransky of Industrial Robot Journal, aims to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach – The interviewee is Dr Robert Ambrose, Chief, Software, Robotics and Simulation Division at National Aeronautics and Space Administration (NASA)’s Johnson Space Center in Houston, Texas. As a young child, even before he started school, Dr Ambrose knew, after seeing the Apollo 11 moonshot, that he wanted to work for NASA. Dr Ambrose describes his career journey into space robotics and shares his teams’ experiences and the importance of the development of Robonaut, a humanoid robotic project designed to work with humans both on Earth and in space. Findings – Dr Ambrose received his MS and BS degrees in mechanical engineering from Washington University in St. Louis, and his PhD in mechanical engineering from the University of Texas at Austin. Dr Ambrose heads the flight spacecraft software, space robotics and system simulations for human spaceflight missions. He oversees on-orbit robotic systems for the International Space Station (ISS), the development of software for the Multi-Purpose Crew Vehicle and future human spaceflight systems, simulations for engineering development and training, hardware in the loop facilities for anomaly resolution and crew training and the technology branch for development of new robotic systems. Dr Ambrose also serves as a Principal Investigator for NASA’s Space Technologies Mission Directorate, overseeing research and formulating new starts in the domains of robotics and autonomous systems. He co-chairs the Office of the Chief Technologist (OCT) Robotics, Tele-Robotics and Autonomous Systems roadmap team for the agency’s technology program, and is the robotics lead for the agency’s human spaceflight architecture study teams. Working with the Office of Science and Technology Policy (OSTP), Dr Ambrose is the Technical Point of Contact for NASA’s collaboration in the National Robotics Initiative (NRI). Originality/value – Dr Ambrose not only realized his own childhood dream by pursuing a career at NASA, but he also fulfilled a 15-year national dream by putting the first humanoid robot into space. After seeking a graduate university that would allow him to do research at NASA, it didn’t take long for Dr Ambrose to foresee that the importance of NASA’s future would be in robots and humans working side-by-side. Through the leadership of Dr Ambrose, NASA formed a strategic partnership with General Motors (GM) and together they built Robonaut, a highly dexterous, anthropomorphic robot. The latest Robonaut version, R2, has nearly 50 patents available for licensing. One of the many technology spinoffs from R2 is the innovative Human Grasp Assist device, or Robo-Glove, designed to increase the strength of a human’s grasp.

Gesture Recognition Based on Human Grasping Activities Using PCA-BMU

This research study presents the recognition of fingers grasps for various grasping styles of daily living. In general, the posture of the human hand determines the fingers that are used to create contact between an object at the same time while developing the touching contact. Human grasping can detect by studying the movement of fingers while bending during object holding. Ten right-handed subjects are participated in the experiment; each subject was fitted with a right-handed GloveMAP, which recorded all movement of the thumb, index, and middle of human fingers while grasping selected objects. GloveMAP is constructed using flexible bend sensors placed back of a glove. Based on the grasp human taxonomy by Cutkosky, the object grasping is distinguished by two dominant prehensile postures; that is, the power grip and the precision grip. The dataset signal is extracted using GloveMAP, and all the signals are filtered using Gaussian filtering method. The method is capable to improving the amplitude transmission characteristic with the minimal combination of time and amplitude response. The result was no overshoot in order to smoothen the grasping signal from unneeded signal (noise) that occurs on the input / original grasping data. Principal Component Analysis – Best Matching Unit (PCA-BMU) is a process of justifying the human grasping data involves several grasping groups and forming a component identified as nodes or neuron.

Analysis of human grasping behavior: correlating tasks, objects and grasps.

This paper is the second in a two-part series analyzing human grasping behavior during a wide range of unstructured tasks. It investigates the tasks performed during the daily work of two housekeepers and two machinists and correlates grasp type and object properties with the attributes of the tasks being performed. The task or activity is classified according to the force required, the degrees of freedom, and the functional task type. We found that 46 percent of tasks are constrained, where the manipulated object is not allowed to move in a full six degrees of freedom. Analyzing the interrelationships between the grasp, object, and task data show that the best predictors of the grasp type are object size, task constraints, and object mass. Using these attributes, the grasp type can be predictedwith 47 percent accuracy. Those parameters likely make useful heuristics for grasp planning systems. The results further suggest the common sub-categorization of grasps into power, intermediate, and precision categories may not be appropriate, indicatingthat grasps are generally more multi-functional than previously thought. We find large and heavy objects are grasped with a power grasp, but small and lightweight objects are not necessarily grasped with precision grasps-even with grasped object size less than 2cm and mass less than 20g, precision grasps are only used 61 percent of the time. These results have important implications for robotic hand design and grasp planners, since it appears while power grasps are frequently used for heavy objects, they can still be quite practical for small, lightweight objects.

Analysis of human grasping behavior: object characteristics and grasp type.

This paper is the first of a two-part series analyzing human grasping behavior during a wide range of unstructured tasks. The results help clarify overall characteristics of human hand to inform many domains, such as the design of robotic manipulators, targeting rehabilitation toward important hand functionality, and designing haptic devices for use by the hand. It investigates the properties of objects grasped by two housekeepers and two machinists during the course of almost 10,000 grasp instances and correlates the grasp types used to the properties of the object. We establish an object classification that assigns each object properties from a set of seven classes, including mass, shape and size of the grasp location, grasped dimension, rigidity, and roundness. The results showed that 55 percent of grasped objects had at least one dimension larger than 15 cm, suggesting that more than half of objects cannot physically be grasped using their largest axis. Ninety-two percent of objects had a mass of 500 g or less, implying that a high payload capacity may be unnecessary to accomplish a large subset of human grasping behavior. In terms of grasps, 96 percent of grasp locations were 7 cm or less in width, which can help to define requirements for hand rehabilitation and defines a reasonable grasp aperture size for a robotic hand. Subjects grasped the smallest overall major dimension of the object in 94 percent of the instances. This suggests that grasping the smallest axis of an object could be a reliable default behavior to implement in grasp planners.

An introductory study of common grasps used by adults during performance of activities of daily living

This paper presents the results of a descriptive survey on human grasps. Sixty-four videos were selected to represent tasks performed in the main areas of activities of daily living (ADL) (personal care, meal preparation, eating, housekeeping, etc.). All the participants were right-handed. Elementary grasps were identified for each hand, and the grasp type (from a 9-type classification), the hands involved, and the duration were registered for each case. The results show that the most commonly used grasps are: pinch, non-prehensile, cylindrical, lateral pinch and lumbrical. The presence of these grasps in the areas of ADL is, however, very different (e.g., pinch is widely used in food preparation and very little in driving). Some grasps were used more frequently with one hand or when both hands were used simultaneously (e.g., special pinch was hardly used by the left hand). Knowing the grasp types most frequently used in ADL is essential to be able to assess grasp rehabilitation processes or hand prostheses development.

Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping

An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands continuously morphing hand postures including functional grasps using between two and four surface electromyography (EMG) electrodes pairs. Four unique maps were developed to transform the EMG control signals in the principal component domain. A preliminary validation experiment was performed by 10 nonamputee subjects to determine the map with highest performance. The subjects used the myoelectric controller to morph a virtual hand between functional grasps in a series of randomized trials. The number of joints controlled accurately was evaluated to characterize the performance of each map. Additional metrics were studied including completion rate, time to completion, and path efficiency. The highest performing map controlled over 13 out of 15 joints accurately.

The influence of body posture on the kinematics of prehension in humans and gorillas (Gorilla gorilla)

Much of our current understanding of human prehension in a comparative context is based on macaque models in a sitting, constrained body posture. In a previous study, we clearly showed differences in the amplitude of the forelimb joints between five primate species (lemur, capuchin, chimpanzee, gorilla and human) during unconstrained grasping where the animals were free to choose their body posture. One of our interrogations was to know if these differences could be due to the body posture. To address this question, this study compares humans with new data for gorillas during an unconstrained food prehension task in two body postures, a sitting and a quadrupedal one. The objective is to determine the behavioral and kinematic strategies (amplitudes and patterns of evolution of the articular angles) as well as differences and invariants of trunk and forelimb motions between species. The subjects were recorded by five cameras, and landmarks were digitized frame by frame to reconstruct 3D movement. Our results show that (1) despite significant influences of body postures on ranges of motion in gorillas and humans, species preserve their specific forelimb joint and trunk contribution; (2) body posture has a limited effect on the basic pattern of wrist velocity. Our study indicates that different primate species have specific kinematic features of limb coordination during prehension, which dose not alter with changes in posture. Therefore, across varying species, it is possible to compare limb kinematics irrespective of postural constraints and unconstrained condition need to be explored in other primates to understand the evolution of primate prehension.

Gravity Affects the Vertical Curvature in Human Grasping Movements

ABSTRACT When humans make grasping movements their digits’ paths are curved vertically. In a previous study the authors found that this curvature is largely caused by the local constraints at the start and end of the movement. Here the authors examined the contribution of gravity to the part of the curvature that was not explained by the local constraints. Subjects had to grasp a tealight (small cylinder) while sitting on a chair. The authors could rotate the whole setup, including the subject, relative to gravity, whereby the positions of the starting point and of the tealight relative to the subject did not change. They found differences between the paths that are consistent with a direct effect of gravity pulling the arm downward.

Evaluation of Human Prehension Using Grasp Quality Measures

One of the main features of the human hand is its grasping ability. Robot grasping has been studied for years and different quality measures have been proposed to evaluate the stability and manipulability of grasps. Although the human hand is obviously more complex than robot hands, the methods used in robotics might be adopted to study the human grasp. The purpose of this work is to propose a set of measures that allow the evaluation of different aspects of the human grasp. The most common robotic grasp quality measures have been adapted to the evaluation of the human hand and a new quality measure – the fatigue index – is proposed in order to incorporate the biomechanical aspect into the evaluation. The minimum set of indices that allows the evaluation of the different aspects of the grasp is obtained from the analysis of a human prehension experiment.

A biomimetic reach and grasp approach for mechanical hands

Reach and grasp are the two key functions of human prehension. The Central Nervous System controls these two functions in a separate but interdependent way. The choice between different solutions to reach and grasp an object–provided by multiple and redundant degrees of freedom (dof)–depends both on the properties and on the use (affordance) of the object to be manipulated. This same control paradigm, i.e. subdivision of prehension into reach and grasp as well as the corresponding multimodal (sensory/motor) information fusion schemes, can also be applied to a mechanical hand carried by a robotic arm. The robotic arm will then be responsible for positioning the hand with respect to the object, and the hand will then grasp and manipulate the object. In this article, we present a biomimetic sensory–motor control scheme in the aim of providing an object-dependent and intelligent reach and grasp ability to such systems. The proposed model is based on a multi-network architecture which incorporates multiple Matching Units trained by a statistical learning algorithm (LWPR). Matching Units perform a multimodal signal integration by correlating sensory and motor information analogous to that observed in cerebral neuronal networks. The simulated network of multiple Matching Units provided estimations of object-dependent 5-finger grasp configurations with endpoint positional errors in the order of a few millimeters. For validation, these estimations were then applied to the control of movement kinematics on an experimental robot composed of a 6 dof robot arm carrying a 16 dof mechanical 4-finger hand. Precision of the kinematics control was such that successful reach, grasp and lift was obtained in all the tests.

Lesions of the dentate and interposed nuclei are associated with impaired prehension in cerebellar patients

In a recent study using voxel based lesion symptom mapping (VLSM) in cerebellar patients following stroke we found associations of prehensile deficits to lesions of the cerebellar cortex and dentate nucleus (DN). Associations to lesions of the interposed nucleus (IN), which has been shown to contribute to prehension in monkeys, could not be established. One possible reason was that the IN was largely unaffected in the stroke patients. To further address the question of IN involvement in prehension we performed VLSM in patients with surgical cerebellar lesions (n=20), exhibiting high lesion overlap in the medial and intermediate cerebellum including the IN. Prehensile deficits were quantified by analyses of movement kinematics and finger forces. In the patient population prehensile deficits comprised lower movement velocity in reaching and increased lift-off time in grasping. These were associated with lesions of the intermediate and lateral cerebellar cortex together with their output nuclei. Specifically, IN lesions were linked to increased lift-off time in grasping and not to slower reaching movements. Thus, our data support IN contribution particularly for the fluent production of grip forces during dexterous prehension in humans.

Coordination of Contact Forces During Multifinger Static Prehension

This study investigated the effects of modifying contact finger forces in one direction-normal or tangential-on the entire set of the contact forces, while statically holding an object. Subjects grasped a handle instrumented with finger force-moment sensors, maintained it at rest in the air, and then slowly: (1) increased the grasping force, (2) tried to spread fingers apart, and (3) tried to squeeze fingers together. Analysis was mostly performed at the virtual finger (VF) level (the VF is an imaginable finger that generates the same force and moment as the four fingers combined). For all three tasks there were statistically significant changes in the VF normal and tangential forces. For finger spreading/squeezing the tangential force neutral point was located between the index and middle fingers. We conclude that the internal forces are regulated as a whole, including adjustments in both normal and tangential force, instead of only a subset of forces (normal or tangential). The effects of such factors as EFFORT and TORQUE were additive; their interaction was not statistically significant, thus supporting the principle of superposition in human prehension.

Grasp programming by demonstration in virtual reality with automatic environment reconstruction

A virtual reality system enabling high-level programming of robot grasps is described. The system is designed to support programming by demonstration (PbD), an approach aimed at simplifying robot programming and empowering even unexperienced users with the ability to easily transfer knowledge to a robotic system. Programming robot grasps from human demonstrations requires an analysis phase, comprising learning and classification of human grasps, as well as a synthesis phase, where an appropriate human-demonstrated grasp is imitated and adapted to a specific robotic device and object to be grasped. The virtual reality system described in this paper supports both phases, thereby enabling end-to-end imitation-based programming of robot grasps. Moreover, as in the PbD approach robot environment interactions are no longer explicitly programmed, the system includes a method for automatic environment reconstruction that relieves the designer from manually editing the pose of the objects in the scene and enables intelligent manipulation. A workspace modeling technique based on monocular vision and computation of edge-face graphs is proposed. The modeling algorithm works in real time and supports registration of multiple views. Object recognition and workspace reconstruction features, along with grasp analysis and synthesis, have been tested in simulated tasks involving 3D user interaction and programming of assembly operations. Experiments reported in the paper assess the capabilities of the three main components of the system: the grasp recognizer, the vision-based environment modeling system, and the grasp synthesizer.

An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension

We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem. Until now the main approach to this problems has been the cut-and-try method, which consists of introducing an objective function and checking how it reflects the experimental data. Using this approach, different objective functions have been proposed for the same motor action. In the current paper we focus on inverse optimization problems with additive objective functions and linear constraints. Such problems are typical in human movement science. The problem of muscle (or finger) force sharing is an example. For such problems we obtain sufficient conditions for uniqueness and propose a method for determining the objective functions. To illustrate our method we analyze the problem of force sharing among the fingers in a grasping task. We estimate the objective function from the experimental data and show that it can predict the force-sharing pattern for a vast range of external forces and torques applied to the grasped object. The resulting objective function is quadratic with essentially non-zero linear terms.

Gesture Recognition of Human Grasping Behaviors for Partner Robots

Gesture Recognition of Human Grasping Behaviors for Partner Robots

A New Methodology for Human Grasp Prediction

Grasping is an essential requirement for digital human models (DHMs). It is a complex process and thus a challenging problem for DHMs, involving a skeletal structure with many degrees-of-freedom (DOFs), cognition, and interaction between the human and objects in the environment. Furthermore, grasp planning involves not only finding the shape of the hand and the position and orientation of the wrist but also the posture of the upper body required for producing realistic grasping simulations. In this paper, a new methodology is developed for grasping prediction by combining a shape-matching method and an optimization-based posture prediction technique. We use shape matching to pick a hand shape from a database of stored grasps, then position the hand around the object. The posture prediction algorithm then calculates the optimal posture for the whole upper body necessary to execute the grasp. The proposed algorithm is tested on a variety of objects in a 3-D environment. The results are realistic and suggest that the new method is more suitable for grasp planning than conventional methods. This improved performance is particularly apparent when the nature of the grasped objects is not known <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> , and when a complex high-DOF hand model is necessary.

Recognition of human grasps by time-clustering and fuzzy modeling

In this paper, we address the problem of recognition of human grasps for five-fingered robotic hands and industrial robots in the context of programming-by-demonstration. The robot is instructed by a human operator wearing a data glove capturing the hand poses. For a number of human grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzy clustering and Takagi–Sugeno (TS) modeling. This so-called time-clustering leads to grasp models using time as an input parameter and fingertip positions as outputs. For a sequence of grasps, the control system of the robot hand identifies the grasp segments, classifies the grasps and generates the sequence of grasps shown before. For this purpose, each grasp is correlated with a training sequence. By means of a hybrid fuzzy model, the demonstrated grasp sequence can be reconstructed.

Recognition of Human Grasps by Time-clustering, Fuzzy Modeling, and Hidden Markov Models

Grasp recognition is a part of Programming-by- Demonstration (PbD) for a five-fingered robotic hand. The robot receives instructions from a human operator to perform different grasps to be used for a robot task. For this purpose the finger joint angle trajectories are recorded by a dataglove and modeled by fuzzy clustering and Takagi-Sugeno modeling. The resulting grasp models use time as input and the joint angles as outputs. Given a test grasp by the human operator the robot control system recognizes the grasp and generates the type of grasp shown before. Three methods of grasp recognition are presented and compared with each other. In the first method a test grasp is compared with model grasps using the difference between the model outputs. In the second method qualitative fuzzy models are used for recognition and classification. The third method uses Hidden-Markov-Models (HMM) for recognition.

Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning

Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically-based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments.