Advanced Robotic Hand Research Articles

Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.

Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations. We developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data). We showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors and R2 value of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy. The deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications. This study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information.

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information

A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. First, motor units (MUs) firing information were identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Our approach provides a reliable neural decoding method for dexterous finger movements. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.

With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of individual fingers continuously and concurrently during dexterous finger flexion and extension. Specifically, motor unit (MU) discharge activity was extracted from the surface high-density electromyogram (EMG) signals recorded from the finger extensors and flexors, respectively. The MU information was separated into different groups to be associated with the flexion or extension of individual fingers and was then used to predict individual finger forces during multi-finger flexion and extension tasks. Compared with the conventional EMG amplitude-based method, our method can obtain a better force estimation performance (a higher correlation and a smaller estimation error between the predicted and the measured force) when a linear regression model was used. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.

New developments in prosthetic arm systems.

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold – insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods.

Mapping Synergies From Human to Robotic Hands With Dissimilar Kinematics: An Approach in the Object Domain

One of the major limitations to the use of advanced robotic hands in industries is the complexity of the control system design due to the large number of motors needed to actuate their degrees of freedom. It is our belief that the development of a unified control framework for robotic hands will allow us to extend the use of these devices in many areas. Borrowing the terminology from software engineering, there is a need for middleware solutions to control the robotic hands independently from their specific kinematics and focus only on the manipulation tasks. To simplify and generalize the control of robotic hands, we take inspiration from studies in neuroscience concerning the sensorimotor organization of the human hand. These studies demonstrated that, notwithstanding the complexity of the hand, a few variables are able to account for most of the variance in the patterns of configurations and movements. The reduced set of parameters that humans effectively use to control their hands, which are known in the literature as synergies, can represent the set of words for the unified control language of robotic hands, provided that we solve the problem of mapping human hand synergies to actions of the robotic hands. In this study, we propose a mapping designed in the manipulated object domain in order to ensure a high level of generality with respect to the many dissimilar kinematics of robotic hands. The role of the object is played by a virtual sphere, whose radius and center position change dynamically, and the role of the human hand is played by a hand model referred to as “paradigmatic hand,” which is able to capture the idea of synergies in human hands.

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.

Precise In-Orbit Servicing by Multisensory Hand-Connected with Long Arm

This paper presents the space experiment of in-orbit servicing from a chaser satellite to a target satellite, using a multisensory hand connected with a long arm. The experiment is carried out to acquire the technology that enables a long robot arm to perform highprecision tasks through the smart hand. A three-finger multisensory hand, ARH (Advanced Robotic Hand), is connected to the 2.4m length manipulator arm, ERA (ETS RobotArm), in orbit, and sample retrieving from ""Orihime"" to ""Hikoboshi"" is achieved in this configuration. The work environment is measured by sensor fusion of the range sensors, hand-eye camera and contact sensors, and the world model is precisely calibrated before the task. The sample retrieving is successfully performed due to the position/force hybrid control of the arm and fine compensation by the hand mechanism under multisensory monitoring.

Development and Space Operation of Advanced Robotic Hand System

In this paper, we present the development and space test of the world's first precise telerobotic system boarded on an unattended space vehicle, ETS-VII (Engineering Test Satellite VII). The robotic system has features of skill and autonomy using a three-finger multisensory hand at a work site in space and a computer graphics-based desktop telerobotic system at a control site on the ground. We developed a compact robotic system with space environment adaptability and satellite system compatibility as well as dexterity and flexible operability. The robotic system was launched in November 1997. The space test reveals that the system works well in orbit.

センサ融合テレロボティクスの宇宙における精密作業実験

This paper presents the space test of the ARH (Advanced Robotic Hand System), which is the world's first precise extravehicular robot aboard the satellite“Hikoboshi”. The telerobotic system has features of dexterity, autonomy and flexible operability, using a three-finger multisensory hand at a work site in space and a computer-graphics-based desktop interface at an operation site on the ground. The concept of sensor-fused telerobotics utilizing multisensory information is introduced, and the system is implemented to perform high precision tasks under the barrier of inter-satellite space communication. The robot system was launched into low earth orbit, and the capability of sensor-fused telerobotics was successfully demonstrated in precise in-orbit servicing.

A wire-handling experiment using a teleoperated advanced robotic hand on ETS-VII

(2000). A wire-handling experiment using a teleoperated advanced robotic hand on ETS-VII. Advanced Robotics: Vol. 14, No. 5, pp. 351-353.