Human Hand Function Research Articles

Soft Robots: Implementation, Modeling, and Methods of control

Soft robotics is a branch of robotics that focuses on technologies with physical features that are like those of live biological creatures. Additionally, they have many details that are hard, if not impossible, to realize with traditional robots which are composed of solid materials. This study concentrates on the current expansion of soft pneumatic actuators for modern soft robotics in recent years, with an emphasis on three areas: Implementation of soft robots, Modeling, and Methods of control systems. Therefore, numerous soft robotic designs and ways to make them suitable for medical, manufacturing, and agricultural applications have been presented. Moreover, a set of functional and technological aspects have been given to review models similar to human hand functionality and motions. To realize the advanced soft robotic hand manipulation function, robotic hands must be equipped with tactile sensing, that sensing is required to provide continuous data on the volume and direction of forces at all contact locations. The research examines achievements in material science, actuation, sensing techniques, and manufacturing technologies, as well as how to model and control a soft robot's motion, all of which are scientifically challenging and, more importantly, practical.

Upper limb prostheses: bridging the sensory gap.

Replacing human hand function with prostheses goes far beyond only recreating muscle movement with feedforward motor control. Natural sensory feedback is pivotal for fine dexterous control and finding both engineering and surgical solutions to replace this complex biological function is imperative to achieve prosthetic hand function that matches the human hand. This review outlines the nature of the problems underlying sensory restitution, the engineering methods that attempt to address this deficit and the surgical techniques that have been developed to integrate advanced neural interfaces with biological systems. Currently, there is no single solution to restore sensory feedback. Rather, encouraging animal models and early human studies have demonstrated that some elements of sensation can be restored to improve prosthetic control. However, these techniques are limited to highly specialized institutions and much further work is required to reproduce the results achieved, with the goal of increasing availability of advanced closed loop prostheses that allow sensory feedback to inform more precise feedforward control movements and increase functionality.

A Biomimetic Tactile Palm for Robotic Object Manipulation

Tactile sensing plays a vital role for human hand function in terms of object exploration, grasping and manipulation. Different types of tactile sensors have been designed during the past decades, which are mainly focused on either the fingertips for grasping or the upper-body for human-robot interaction. In this letter, a novel soft tactile sensor has been designed to mimic the functionality of human palm that can estimate the contact state of different objects. The tactile palm mainly consists of three parts including an electrode array, a soft cover skin and the liquid resistance between them. The design principle and the fabrication process are described in details, with a number of experiments showcasing the effectiveness of the proposed design.

An Accessible, Open-Source Dexterity Test: Evaluating the Grasping and Dexterous Manipulation Capabilities of Humans and Robots.

Evaluating the dexterity of human and robotic hands through appropriate benchmarks, scores, and metrics is of paramount importance for determining how skillful humans are and for designing and developing new bioinspired or even biomimetic end-effectors (e.g., robotic grippers and hands). Dexterity tests have been used in industrial and medical settings to assess how dexterous the hands of workers and surgeons are as well as in robotic rehabilitation settings to determine the improvement or deterioration of the hand function after a stroke or a surgery. In robotics, having a comprehensive dexterity test can allow us to evaluate and compare grippers and hands irrespectively of their design characteristics. However, there is a lack of well defined metrics, benchmarks, and tests that quantify robot dexterity. Previous work has focused on a number of widely accepted functional tests that are used for the evaluation of manual dexterity and human hand function improvement post injury. Each of these tests focuses on a different set of specific tasks and objects. Deriving from these tests, this work proposes a new modular, affordable, accessible, open-source dexterity test for both humans and robots. This test evaluates the grasping and manipulation capabilities by combining the features and best practices of the aforementioned tests, as well as new task categories specifically designed to evaluate dexterous manipulation capabilities. The dexterity test and the accompanying benchmarks allow us to determine the overall hand function recovery and dexterity of robotic end-effectors with ease. More precisely, a dexterity score that ranges from 0 (simplistic, non-dexterous system) to 1 (human-like system) is calculated using the weighted sum of the accuracy and task execution speed subscores. It should also be noted that the dexterity of a robotic system can be evaluated assessing the efficiency of either the robotic hardware, or the robotic perception system, or both. The test and the benchmarks proposed in the study have been validated using extensive human and robot trials. The human trials have been used to determine the baseline scores for the evaluation system. The results show that the time required to complete the tasks reduces significantly with trials indicating a clear learning curve in mastering the dexterous manipulation capabilities associated with the imposed tasks. Finally, the time required to complete the tasks with restricted tactile feedback is significantly higher indicating its importance.

The Precision of the Human Hand: Variability in Pinch Strength and Manual Dexterity

Changes in hand morphology throughout human evolution have facilitated the use of forceful pad-to-pad precision grips, contributing to the development of fine motor movement and dexterous manipulation typical of modern humans. Today, variation in human hand function may be affected by demographic and/or lifestyle factors, but these remain largely unexplored. We measured pinch grip strength and dexterity in a heterogeneous cross-sectional sample of human participants (n = 556) to test for the potential effects of sex, age, hand asymmetries, hand morphology, and frequently practiced manual activities across the lifespan. We found a significant effect of sex on pinch strength, dexterity, and different directional asymmetries, with the practice of manual musical instruments, significantly increasing female dexterity for both hands. Males and females with wider hands were also stronger, but not more precise, than those with longer hands, while the thumb-index ratio had no effect. Hand dominance asymmetry further had a significant effect on dexterity but not on pinch strength. These results indicate that different patterns of hand asymmetries and hand function are influenced in part by life experiences, improving our understanding of the link between hand form and function and offering a referential context for interpreting the evolution of human dexterity.

Real time motion intention recognition method with limited number of surface electromyography sensors for A 7-DOF hand/wrist rehabilitation exoskeleton

The functions of human hand are rich, and the motor dysfunction of hand of chronic stroke patients can be alleviated to some extent through active rehabilitation training. Hand rehabilitation exoskeleton can assist patients to do active rehabilitation training. However, how to realize more motion with less surface electromyogrphy (sEMG) sensors, and how to realize the real-time motion intention recognition are two important issues. This paper introduces real-time motion intention recognition method with limited number of sEMG sensors for a 7-DOF wearable hand/wrist rehabilitation exoskeleton to realize the real-time motion intention recognition and rehabilitation training. Root mean square (RMS) and Bens Spiker Algorithm (BSA) features of three-channel sEMG signals are extracted, and they are mapped to seven different intention movements by combining the Bagging method. The finger structure part of the exoskeleton is composed of a rotary-spatial-spatial-rotary (RSSR) mechanism and a double-parallelogram mechanism, which makes the projection center of exoskeleton coincide with the rotation center of the hand joint. The average real-time motion intention recognition accuracy is 95.37 ± 0.97%.

EMG control of a 3D printed myo electric prosthetic hand

Recent technological advances have enabled the prosthetic developers to derive an ideal replacement for the human arm in near future. This research presents design of a 3D Printed Myo Electric Prosthetic Hand that grasp symmetrical objects through Electromyographic signals and is intended for people suffering from Trans-radial Amputation. In the current study, a set of data about three motions that include hand open, close and rest position was been acquired from forearm muscles of a human arm. The computer aided model of the prosthetic hand was developed in SolidWorks® which was later 3D printed using Poly Lactic Acid (PLA).The proposed design was based closely on tendon actuation, related to human hand functionality. Signal acquisition and processing has been done using Myo Armband. Different features were selected such that they can be passed through classification process in order to control the hand motions. Using the Bluetooth transmitter, the filtered data was sent and saved in Arduino Uno® controller. Later, the Support Vector Machine (SVM) was been evaluated as a classifier. The classification accuracy obtained from SVM was 96.7%. The results were found significant (p<0.01) for twelve able bodied subjects. EMG based grasp control was implemented with successful testing of designed prosthetic hand for three different motions. The practical effectiveness of the Myoelectric Hand was demonstrated by grasping household objects such battery charger, wallet and water bottle.

Proprioceptive Localization of the Fingers: Coarse, Biased, and Context-Sensitive.

The proprioceptive sense provides somatosensory information about positions of parts of the body, information that is essential for guiding behavior and monitoring the body. Few studies have investigated the perceptual localization of individual fingers, despite their importance for tactile exploration and fine manipulation. We present two experiments assessing the performance of proprioceptive localization of multiple fingers, either alone or in combination with visual cues. In the first experiment, we used a virtual reality paradigm to assess localization of multiple fingers. Surprisingly, the errors averaged 3.7 cm per digit, which represents a significant fraction of the range of motion of any finger. Both random and systematic errors were large. The latter included participant-specific biases and participant-independent distortions that evoked similar observations from prior studies of perceptual representations of hand shape. In a second experiment, we introduced visual cues about positions of nearby fingers, and observed that this contextual information could greatly decrease localization errors. The results suggest that only coarse proprioceptive information is available through somatosensation, and that finer information may not be necessary for fine motor behavior. These findings may help elucidate human hand function, and inform new applications to the design of human-computer interfaces or interactions in virtual reality.

Optimal visual\u2013haptic integration with articulated tools

When we feel and see an object, the nervous system integrates visual and haptic information optimally, exploiting the redundancy in multiple signals to estimate properties more precisely than is possible from either signal alone. We examined whether optimal integration is similarly achieved when using articulated tools. Such tools (tongs, pliers, etc) are a defining characteristic of human hand function, but complicate the classical sensory ‘correspondence problem’ underlying multisensory integration. Optimal integration requires establishing the relationship between signals acquired by different sensors (hand and eye) and, therefore, in fundamentally unrelated units. The system must also determine when signals refer to the same property of the world—seeing and feeling the same thing—and only integrate those that do. This could be achieved by comparing the pattern of current visual and haptic input to known statistics of their normal relationship. Articulated tools disrupt this relationship, however, by altering the geometrical relationship between object properties and hand posture (the haptic signal). We examined whether different tool configurations are taken into account in visual–haptic integration. We indexed integration by measuring the precision of size estimates, and compared our results to optimal predictions from a maximum-likelihood integrator. Integration was near optimal, independent of tool configuration/hand posture, provided that visual and haptic signals referred to the same object in the world. Thus, sensory correspondence was determined correctly (trial-by-trial), taking tool configuration into account. This reveals highly flexible multisensory integration underlying tool use, consistent with the brain constructing internal models of tools’ properties.

On the development of intrinsically-actuated, multisensory dexterous robotic hands

Restoring human hand function by mechatronic means is very challenging in robotics research. In this paper, we first make a brief review on the development of dexterous robotic/prosthetic hands, and then detail our design philosophy of several robot hands. We make a concentration on a type of intrinsically-actuated robot hands, wherein the driving, transmission, and control elements are totally embedded in the hand. According to different application scenarios, we develop robot hands in two parallel lines, dexterous robotic hand and anthropomorphic prosthetic hand. In both, the hand’s actuation, sensing, and control subsystems are highly integrated and modularized. This feature endows our robot hands with compact appearances, simple integration, and large flexibilities. At last, we give some perspectives on the future development of dexterous hands from the aspects of structure, functionality, and control strategies.

Перспектива протезирования/роботизации утраченных функций руки человека на основе электромиографического аутосигнала

Перспектива протезирования/роботизации утраченных функций руки человека на основе электромиографического аутосигнала

Classification of Multiple Finger Motions During Dynamic Upper Limb Movements.

To better restore human hand function, advanced hand prostheses should be able to deal with a variety of daily living conditions. In this paper, we addressed myoelectric signal variations introduced by different muscle contractions, dynamic arm movements, and outer interfering forces in the practice of pattern recognition-based myoelectric control schemes. We examined four different training paradigms (data-collection protocols) and quantified their effectiveness for obtaining a robust classification. We further depicted the classification accuracy according to different arm/wrist motion primitives. Our results indicate the training paradigm that collects myoelectric signals on dynamic arm postures and varying muscular contractions (DPDE) can largely mitigate the motions' misclassification rate. The misclassification rate of finger motions seems to highly correlate to wrist pronation and supination, rather than different arm positions. Combining proprioceptive information, such as the hand's orientation, with myoelectric signals for classification only slightly alleviates the misclassification rate.

Development of a prototype over-actuated biomimetic prosthetic hand.

The loss of a hand can greatly affect quality of life. A prosthetic device that can mimic normal hand function is very important to physical and mental recuperation after hand amputation, but the currently available prosthetics do not fully meet the needs of the amputee community. Most prosthetic hands are not dexterous enough to grasp a variety of shaped objects, and those that are tend to be heavy, leading to discomfort while wearing the device. In order to attempt to better simulate human hand function, a dexterous hand was developed that uses an over-actuated mechanism to form grasp shape using intrinsic joint mounted motors in addition to a finger tendon to produce large flexion force for a tight grip. This novel actuation method allows the hand to use small actuators for grip shape formation, and the tendon to produce high grip strength. The hand was capable of producing fingertip flexion force suitable for most activities of daily living. In addition, it was able to produce a range of grasp shapes with natural, independent finger motion, and appearance similar to that of a human hand. The hand also had a mass distribution more similar to a natural forearm and hand compared to contemporary prosthetics due to the more proximal location of the heavier components of the system. This paper describes the design of the hand and controller, as well as the test results.

人の精密な手指機能を再建する五指型筋電義手

人の精密な手指機能を再建する五指型筋電義手

Development of the UB Hand IV: Overview of Design Solutions and Enabling Technologies

The replication of the human hand's functionality and appearance is one of the main reasons for the development of robot hands. Despite 40 years of research in the field [1], the reproduction of human capabilities, in terms of dexterous manipulation, still seems unachievable by the state-of-the-art technologies. From a design perspective, even defining the optimal functionalities of a robotic end-effector is quite a challenging task since possible applications of these devices span industrial robotics, humanoid robotics, rehabilitation medicines, and prosthetics, to name a few. Therefore, it is reasonable to think that the design solutions, which are well suited to a single domain, might not be readily taken as general guidelines. For example, industrial manipulators are often equipped with basic grippers, which are conceived so as to increase the throughput and the reliability, and are assumed to operate in structured environments. In this case, the enhanced manipulation skills and the subsequent cost increases must be carefully motivated by the application requirements.

Constraint Study for a Hand Exoskeleton: Human Hand Kinematics and Dynamics

In the last few years, the number of projects studying the human hand from the robotic point of view has increased rapidly, due to the growing interest in academic and industrial applications. Nevertheless, the complexity of the human hand given its large number of degrees of freedom (DoF) within a significantly reduced space requires an exhaustive analysis, before proposing any applications. The aim of this paper is to provide a complete summary of the kinematic and dynamic characteristics of the human hand as a preliminary step towards the development of hand devices such as prosthetic/robotic hands and exoskeletons imitating the human hand shape and functionality. A collection of data and constraints relevant to hand movements is presented, and the direct and inverse kinematics are solved for all the fingers as well as the dynamics; anthropometric data and dynamics equations allow performing simulations to understand the behavior of the finger.

A design approach for myoelectric arm with hand and wrist motions using single actuator

This paper relates to the development of prosthetic myoelectric hand that performs many functions of real human hand like opening and closing of fingers and wrist rotation. This dual movement of prosthetic hand is controlled by a single DC motor. Below-elbow amputee persons with missing limbs can append this prosthetic hand with the available stump and can do some of the hand operations with multiple degrees of freedom by voluntary activation of muscles using electromyogram (EMG) electrodes. To rehabilitate such a person, facilities like opening, closing, grasping, and lifting objects of different weight with variable grip force are available like natural hand. The main design consideration includes degrees of freedom, number of actuators, power of actuators, reliability, electronic control, light weight, economic viability, wrist rotation, variable grip force pattern, and ease of attachment with limb. Use of electromagnetic clutches along with the coupling logic of DC motor and microcontroller based on grip force generation and wrist rotation based on EMG signals imparts a new function to the device. It will be useful for both robotic and prosthetic industry.

The Journal of Physiology Annual Report 2011–12

Throughout 2010, the editorial policy of The Journal of Physiology has focused on maintaining high and consistent standards of peer review of submitted research articles and providing readers with a range of informative reviews and commentaries by recognised experts. The Journal has continued to attract papers across all areas of physiology and to increase online readership. Initiatives to extend the reach of The Journal to readers outside the academic community, framed by our policy to promote the discipline of physiology more widely, have been successful, judged by downloading of content. The Journal's ISI metrics improved, indicating that our content has both immediate impact and an enduring quality.

Human hand function: the limitations of brain and brawn

Human hand function: the limitations of brain and brawn

Prediction of hand strength by hand injury severity scoring system in hand injured patients

Purpose: Hand strength is a key component in human hand function, especially to those with strength-required jobs. To evaluate post-injury hand strength, this study was conducted to assess the predictability of hand injury severity scoring (HISS) system to hand strength after a period of recovery. Methods: In this retrospective study, 80 traumatic hand-injured subjects were recruited. Hand strength tests measured by dynamometers were performed by subjects in ways of palmar pinch, lateral pinch and cylindrical grip. The logistic regression models were used to evaluate the risk of poor hand strength among the severities represented by HISS scores. Results: A positive correlation exists between the differences of the strength of two hands, and the severity shown by HISS system in the poorer half of subjects. The risk to be in poorer half of palmar pinch subtest is significant in moderate subgroup of HISS severity. The risk to be in poorer half of cylindrical grip subtest is significant in major subgroup of HISS severity. The risk to be in poorer half of palmar pinch and cylindrical grip subtests is significant in skeletal component of HISS system. Conclusions: HISS is a descriptive severity scoring system to hand injury and also a useful instrument to predict functional outcome. This study revealed the HISS system may predict post-injury hand strength after recovery.Implications for Rehabilitation: Hand injuryTraumatic hand injury results in considerable loss of personal productivity and increased social burden. Hand injury severity scoring (HISS) system could be an adequate predictor to post-injury hand strength.Early rehabilitation intervention is mandatory to those with anticipatory poor hand strength to improve outcomes.Full motor recovery of hand injury may take years to complete. Further training is warranted to those with severe hand injury.