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Nowadays, artificial intelligence is used in many fields to diagnose and treat different diseases. Robots are also useful tools that substitute for human work. Despite robots being used also for touch therapy, can they substitute for the human touch? Human touch has a strong social component, and it is necessary for the correct development of newborns and the treatment of pathological situations. To substitute human touch, it is necessary to integrate robots with artificial intelligence as well as with sensors that mimic human skin. Today, the question remains without answer: Can human touch be substituted with AI in its social and affiliative components?

A significant number of individuals in the United States use assistive devices to enhance their mobility, and a considerable portion of those who depend on such aids require assistance from another individual in performing daily living activities. The introduction of robotic grippers has emerged as a transformative intervention, significantly contributing to the cultivation of independence. However, there are few grippers in the fields, which help with mimicking human hand-like movements (mostly grasping and pinching, with adoptive force control) to grasp and carry objects. Additionally, the data are not available even on how many Activities of Daily Living (ADL) objects they can handle. The goal of the research is to offer a new three-fingered gripper for daily living assistance, which can both grasp and pinch with adaptive force, enabling the capabilities of handling wide-ranging ADL objects with a minimal footprint. It is designed to handle 90 selective essential ADL objects of different shapes (cylindrical, irregular, rectangular, and round), sizes, weights, and textures (smooth, rough, bumpy, and rubbery). The gripper boasts a meticulously engineered yet simple design, facilitating seamless manufacturing through 3D printing technology without compromising its operational efficacy. The gripper extends its functionality beyond conventional grasping, featuring the capability to pinch (such as holding a credit card) and securely hold lightweight objects. Moreover, the gripper is adaptable to grasping various objects with different shapes and weights with controlled forces. In evaluation, the developed gripper went through rigorous load tests and usability tests. The results demonstrated that the users picked and placed 75 objects out of 90 daily objects. The gripper held and manipulated objects with dimensions from 25 mm to 80 mm and up to 2.9 kg. For heavy-weight objects (like books) where the centroid is far apart from the grasping areas, it is difficult to hold them due to high torque. However, objects' textures have no significant effect on grasping performance. Users perceived the simplicity of the gripper. Further investigation is required to assess the utility and longevity of grippers. This study contributes to developing assistive robots designed to enhance object manipulation, thereby improving individuals' independence and overall quality of life. [ABSTRACT FROM AUTHOR]

The purpose of prosthetic hands is to replicate the functional capabilities of the human hand, allowing users to perform daily tasks effectively. The mechanical design of artificial fingers is a key factor in determining the overall performance of these prostheses. A novel solution in this field is the Adam Hand by BionIT Labs, which belongs to the family of underactuated, multiarticulated myoelectric prostheses. This paper presents a kinetostatic analysis of the Adam’s Hand finger, which utilizes a gear train as an underactuated transmission mechanism. A test bench is also introduced to experimentally measure the grasping force when the finger spans its entire working range, defined by a 90° rotation of the proximal and distal phalanx joints. The experimental results demonstrate good agreement with the theoretical predictions, yielding mean percentage errors of less than 4%, with maximum error of about 6%. The analytical and experimental results obtained from Adam’s hand are also compared with those of an alternative prosthesis, namely Bebionic, that is fully actuated.

Tactile sensing technology is crucial for soft grippers. Soft grippers equipped with intelligent tactile sensing systems based on various sensors can interact safely with the unstructured environments and obtain precise properties of objects (e.g., size and shape). It is essential to develop state‐of‐the‐art sensing technologies for soft grippers to handle different grasping tasks. In this review, the development of tactile sensing techniques for robotic hands is first introduced. Then, the principles and structures of different types of sensors normally adopted in soft grippers, including capacitive tactile sensors, piezoresistive tactile sensors, piezoelectric tactile sensors, fiber Bragg grating (FBG) sensors, vision‐based tactile sensors, triboelectric tactile sensors, and other advanced sensors developed recently are briefly presented. Furthermore, sensing modalities and methodologies for soft grippers are also described in aspects of force measurement, perception of object properties, slip detection, and fusion of perception. The application scenarios of soft grippers are also summarized based on these advanced sensing technologies. Finally, the challenges of tactile sensing technologies for soft grippers that need to be tackled are discussed and perspectives in addressing these challenges are pointed out. [ABSTRACT FROM AUTHOR]

Designing robotic hands has been an active area of research and innovation in the last decade. However, little is known about how people perceive robot hands and react to being touched by them. To inform hand design for social robots, we created a database of 73 robot hands and ran two user studies. In the first study, 160 online users rated the hands in our database. Variations in user ratings mostly centered on the perceived Comfortableness, Interestingness, and Industrialness of the hands. In a second lab-based study, users evaluated seven physical hands and had similar ratings to results from the online study. Furthermore, we did not find a significant difference in user ratings before and after the users were touched by the hands. We provide regression models that can predict user ratings from the hand features (e.g., number of fingers) and an online interface for using our robot hand database and predictive models. [ABSTRACT FROM AUTHOR]

A significant number of individuals in the United States use assistive devices to enhance their mobility, and a considerable portion of those who depend on such aids require assistance from another individual in performing daily living activities. The introduction of robotic grippers has emerged as a transformative intervention, significantly contributing to the cultivation of independence. However, there are few grippers in the fields, which help with mimicking human hand-like movements (mostly grasping and pinching, with adoptive force control) to grasp and carry objects. Additionally, the data are not available even on how many Activities of Daily Living (ADL) objects they can handle. The goal of the research is to offer a new three-fingered gripper for daily living assistance, which can both grasp and pinch with adaptive force, enabling the capabilities of handling wide-ranging ADL objects with a minimal footprint. It is designed to handle 90 selective essential ADL objects of different shapes (cylindrical, irregular, rectangular, and round), sizes, weights, and textures (smooth, rough, bumpy, and rubbery). The gripper boasts a meticulously engineered yet simple design, facilitating seamless manufacturing through 3D printing technology without compromising its operational efficacy. The gripper extends its functionality beyond conventional grasping, featuring the capability to pinch (such as holding a credit card) and securely hold lightweight objects. Moreover, the gripper is adaptable to grasping various objects with different shapes and weights with controlled forces. In evaluation, the developed gripper went through rigorous load tests and usability tests. The results demonstrated that the users picked and placed 75 objects out of 90 daily objects. The gripper held and manipulated objects with dimensions from 25 mm to 80 mm and up to 2.9 kg. For heavy-weight objects (like books) where the centroid is far apart from the grasping areas, it is difficult to hold them due to high torque. However, objects’ textures have no significant effect on grasping performance. Users perceived the simplicity of the gripper. Further investigation is required to assess the utility and longevity of grippers. This study contributes to developing assistive robots designed to enhance object manipulation, thereby improving individuals’ independence and overall quality of life.

In this study, we address the problem of learning a posture-controllable three-dimensional (3D) representation of articulated robotic hands. Neural radiance fields (NeRFs) have outperformed grid-like 3D representations on a novel view synthesis tasks; however, rendering novel views while controlling the joints and posture of robotic hands using NeRFs is still challenging. Conventional methods address this problem by explicitly conditioning the posture or by introducing individual part-wise NeRFs. However, they are unable to scale with respect to the number of joints, thereby causing poor convergence or poor rendering performance. Based on the assumption that the difficulty in controllability results from high-dimensional inputs, we propose a NeRF conditioned on a fixed-length and low-dimensional posture representation. Specifically, we employ the eigengrasp representation to control the joints and posture of robotic hands. This strategy is called the eigengrasp-conditioned neural radiance field (Eigengrasp-NeRF). To evaluate the effectiveness of our method, we conduct experiments in which a novel robotic hand dataset that comprises multi-view images with various camera poses and postures is rendered. The experimental results reveal that our method allows the control of joints while maintaining the rendering quality of the original NeRF, outperforming the pose-conditioned NeRF. Moreover, our method generalizes to unknown grasping behaviors when trained on a dataset that covers the grasping taxonomy. Project page: https://aizawan.github.io/eigennerf/

In this paper, the realization of any specified planar Cartesian compliance for an object grasped by a compliant hand is addressed. The hands considered have 2 or more fingers for which each has 3 modulated elastic joints and predetermined link lengths. Geometric construction-based compliance synthesis procedures are developed. Using these procedures, a large set of compliant behaviors can be realized by a single hand simply by adjusting the configuration of each finger and by adjusting the joint stiffness (using variable stiffness actuation) of each finger joint. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of underactuated vacuum gripper (UVG), which combines two strategies, that is, underactuation and vacuum grasping. The idea is to achieve shape adaptation while improving grip stability by augmenting an underactuated gripper with suction cups. A general theory to predict the contact forces for a UVG is developed and used for comparison reasons with a standard underactuated counterpart. Multibody simulations have been performed to verify the analytical model and quantitatively evaluate the performance of the system in terms of three metrics, namely, grasp stability, contact force distribution, and pull-out force. Finally, the experimental results obtained from a physical prototype of an underactuated linkage-driven gripper equipped with suction cups are illustrated, attesting to the feasibility and potential gain of the system. [ABSTRACT FROM AUTHOR]

The interaction of humans with space has increased tremendously. With the growing role of Extravehicular Activities (EVAs), the astronaut gloves in space suits require technological research and innovations. The gloves may seem insignificant to design, but it is practically the most tedious as all the tasks during an EVA require significant hand usage. A space glove should have strength and capabilities like dexterity, manipulability, and tactility. Its primary objective is to allow astronauts to move their fingers as efficiently as possible and aid with locomotion, restraint, and object handling. The present glove is cumbersome due to high torque requirements at the knuckles or metacarpophalangeal (MCP) joints. Also, astronauts suffer from a condition called fingernail delamination (or onycholysis) due to sustained pressure on the fingertips. The focus is on the main challenges of the given technology and scientific enhancements and how to use them to draw all their benefits. This paper provides a review study to determine the project's plausibility with gradual and substantial technological advancements.

This paper presents a study on the effect of design parameters of an underactuated hand on its grasp performance. Three kinds of grasp performance characteristics are considered: grasp range, grasp strength, and immobilizing grasp range. Grasp strength is defined as the stiffness of the grasp. Immobilizing grasps are those in which the object cannot be moved for a force up to a certain threshold. In general, underactuated hands cannot produce immobilizing grasps. However, we show that immobilizing grasps can be created by including joint limits in the hand design. We consider the effect of two design parameters on the grasp performance: torque ratio and finger-base distance. Results show that an optimal finger-base distance and torque ratio exists that maximizes the grasp range and grasp strength. Also, the immobilizing grasp range is increased by decreasing the finger-base distance and increasing the torque ratio and joint limits. [ABSTRACT FROM AUTHOR]

This editorial discusses the advancements in human-like robotic hands for biomedical applications. Robotic hands with tactile or force/torque sensors have been used to assist robots and amputees in interacting with objects and environments. The research topic explores the design and integration of novel solutions for robotic and prosthetic hands, as well as sensing technology. The contributions come from authors and reviewers from various countries and institutions. The editorial provides a brief summary of some of the main contributions, including adaptive actuation mechanisms, compliant sensorized hands, 3D-printed flexures, soft exoskeletons for rehabilitative treatment, and multimodal fusion for prosthetic hand control. The authors hope that this research topic will encourage further collaboration and development in this field. [Extracted from the article]

In this study, for intention recognition, a convolutional neural network (CNN) classification model using the electromyography (EMG) signals acquired from the subject was developed. For sensory feedback, a rule-based wearable proprioceptive feedback haptic device, a new method for providing feedback on the grip information of a robotic prosthesis was proposed. Then, we constructed a closed-loop integrated system consisting of the CNN-based EMG classification model, the proposed haptic device, and a robotic prosthetic hand. Finally, an experiment was conducted in which the closed-loop integrated system was used to simultaneously evaluate the performance of the intention recognition and sensory feedback for a subject. The trained EMG classification model and the proposed haptic device showed the intention recognition and sensory feedback performance with 97% or higher accuracy in 10 grip states. Although some errors occurred in the intention recognition using the EMG classification model, in general, the grip intention of the subject was grasped relatively accurately, and the grip pattern was also accurately transmitted to the subject by the proposed haptic device. The integrated system which consists of the intention recognition using the CNN-based EMG classification model and the sensory feedback using the proposed haptic device is expected to be utilized for robotic prosthetic hand prosthesis control of limb loss participants. [ABSTRACT FROM AUTHOR]

Research in the field of underwater (UW) robotic applications is rapidly developing. The emergence of coupling the newest technologies on submersibles, different types of telecommunication devices, sensors, and soft robots is transforming the rigid approach to robotic design by providing solutions that bridge the gap between accuracy and adaptability in an environment where there is so much fluctuation in object targeting and environmental conditions. In this paper, we represent a review of the history, development, recent research endeavors, and projected outlook for the area of soft robotics technology pertaining to its use with tactile sensing in the UW environment.

This paper proposes a novel type of grasping strategy that draws inspiration from the role of touch and the importance of wrist motions in human grasping. The proposed algorithm, which we call Sequential Contact-based Adaptive Grasping, can be used to reactively modify a given grasp plan according to contacts arising between the hand and the object. This technique, based on a systematic constraint categorization and an iterative task inversion procedure, is shown to lead to synchronized motions of the fingers and the wrist, as it can be observed in humans, and to increase grasp success rate by substantially mitigating the relevant problems of object slippage during hand closure and of uncertainties caused by the environment and by the perception system. After describing the grasping problem in its quasi-static aspects, the algorithm is derived and discussed with some simple simulations. The proposed method is general as it can be applied to different kinds of robotic hands. It refines a priori defined grasp plans and significantly reduces their accuracy requirements by relying only on a forward kinematic model and elementary contact information. The efficacy of our approach is confirmed by experimental results of tests performed on a collaborative robot manipulator equipped with a state-of-the-art underactuated soft hand. [ABSTRACT FROM AUTHOR]

Grasping and dexterous manipulation remain fundamental challenges in robotics, above all when performed with multifingered robotic hands. Having simulation tools to design and test grasp and manipulation control strategies is paramount to get functional robotic manipulation systems. In this paper, we present a framework for modeling and simulating grasps in the Simulink environment, by connecting SynGrasp, a well established MATLAB toolbox for grasp simulation and analysis, and Simscape Multibody, a Simulink Library allowing the simulation of physical systems. The proposed approach can be used to simulate the grasp dynamics in Simscape, and then analyse the obtained grasps in SynGrasp. The devised functions and blocks can be easily customized to simulate different hands and objects.

Nowadays, artificial intelligence is used in many fields to diagnose and treat different diseases. Robots are also useful tools that substitute for human work. Despite robots being used also for touch therapy, can they substitute for the human touch? Human touch has a strong social component, and it is necessary for the correct development of newborns and the treatment of pathological situations. To substitute human touch, it is necessary to integrate robots with artificial intelligence as well as with sensors that mimic human skin. Today, the question remains without answer: Can human touch be substituted with AI in its social and affiliative components?, Competing Interests: Conflicts of InterestThe authors declare no conflicts of interest., (© 2024 by the authors.)

Objective: This study aims at investigating the functional performance of a novel prosthesis control scheme integrating an inductive tongue interface and myoelectric control. The tongue interface allowed direct selection of the desired grasp while myoelectric signals were used to open and close the robotic hand. Methods: The novel method was compared to a conventional sequential on/off myoelectric control scheme using functional tasks defined by Assistive Hand Assessment protocol. Ten able-bodied participants were fitted with the SmartHand on their left forearm. They used both the conventional myoelectric control and the Tongue and Myoelectric Hybrid interface (TMH) to accomplish two activities of daily living (i.e., preparing a sandwich and gift wrapping). Sessions were video recorded and the outcome measure was the completion time for the subtasks as well as the full tasks. Results: The sandwich task was completed significantly faster, with 19% decrease in the completion time, using the TMH when compared to the conventional sequential on/off myoelectric control scheme (p < 0.05). Conclusion: The results indicate that the TMH control scheme facilitates the active use of the prosthetic device by simplifying grasp selection, leading thereby to faster completion of challenging and relevant tasks involving bimanual activities. [ABSTRACT FROM AUTHOR]

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons., (© 2024 IOP Publishing Ltd.)

Herein, the development of complex 3D intelligent structures such as robotic hands using innovative designs and multimaterial additive manufacturing technology is presented. The distal phalanges of the 3D printed hand presented herein have inherent soft capacitive touch or pressure sensors and embedded electronics. Materials such as thermoplastic polyurethane (TPU), silver paint, conductive polylactic acid composite, graphite ink, etc. are explored to develop five different variants of the sensors using a modified 3D printer, which is capable of extruding conductive ink, metal paste, and polymers. The best‐performing 3D printed soft capacitive touch sensors, formed with silver paint and soft rubber (Ecoflex 00‐30), are integrated on the distal phalanges of the 3D printed robotic hand. These sensors exhibit a stable response with sensitivity of 0.00348 kPa−1 for pressure

Herein, the development of complex 3D intelligent structures such as robotic hands using innovative designs and multimaterial additive manufacturing technology is presented. The distal phalanges of the 3D printed hand presented herein have inherent soft capacitive touch or pressure sensors and embedded electronics. Materials such as thermoplastic polyurethane (TPU), silver paint, conductive polylactic acid composite, graphite ink, etc. are explored to develop five different variants of the sensors using a modified 3D printer, which is capable of extruding conductive ink, metal paste, and polymers. The best‐performing 3D printed soft capacitive touch sensors, formed with silver paint and soft rubber (Ecoflex 00‐30), are integrated on the distal phalanges of the 3D printed robotic hand. These sensors exhibit a stable response with sensitivity of 0.00348 kPa−1 for pressure <10 kPa and 0.00134 kPa−1 for higher pressure. To demonstrate the practical applicability, the 3D printed hand with embedded soft capacitive touch sensors is used for interacting with everyday objects. The tightly integrated sensing elements within the 3D printed structures, as presented herein, can pave the way for a new generation of truly smart material systems that can possibly change their appearance and shape autonomously. [ABSTRACT FROM AUTHOR]

Summary: Determining an appropriate grasp configuration for multi-finger grasping is difficult due to the complexity of robotic hands. The multi-finger grasp planning should consider not only geometry constraints of objects but also kinematics and dynamics of robotic hand. In this paper, a precision grasp-planning framework is presented for multi-finger hand to grasp unknown objects. First, the manipulation capabilities of the robotic hand are analyzed. The analysis results are further used as bases for the precision grasp planning. Second, the superquadric (SQ) fitting method is used for approximating unknown object models. Finally, a local–global optimization method is implemented to find appropriate grasp configurations for dexterous hand. The presented planning framework is validated in simulation experiments. Simulation results demonstrated that the presented grasp-planning framework enables the multi-finger hand to grasp unknown objects effectively. [ABSTRACT FROM AUTHOR]

Tactile sensors suffer from many types of interference and errors like crosstalk, non-linearity, drift or hysteresis, therefore calibration should be carried out to compensate for these deviations. However, this procedure is difficult in sensors mounted on artificial hands for robots or prosthetics for instance, where the sensor usually bends to cover a curved surface. Moreover, the calibration procedure should be repeated often because the correction parameters are easily altered by time and surrounding conditions. Furthermore, this intensive and complex calibration could be less determinant, or at least simpler. This is because manipulation algorithms do not commonly use the whole data set from the tactile image, but only a few parameters such as the moments of the tactile image. These parameters could be changed less by common errors and interferences, or at least their variations could be in the order of those caused by accepted limitations, like reduced spatial resolution. This paper shows results from experiments to support this idea. The experiments are carried out with a high performance commercial sensor as well as with a low-cost error-prone sensor built with a common procedure in robotics.

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors. We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands.

Developing natural control strategies represents an intriguing challenge in the design of human–robot interface (HRI) systems. The teleoperation of robotic grasping devices, especially in industrial, rescue, and aerospace applications, is mostly based on nonintuitive approaches, such as remote controllers. On the other hand, recent research efforts target solutions that mimic the human ability to manage multifinger grasps and finely modulate grasp impedance. Since electromyography (EMG) contains information about human motion control, it is possible to leverage such neuromuscular knowledge to teleoperate robotic hands for grasping tasks. In this paper, we present an HRI system based on eight fully differential EMG sensors connected to a wearable sensor node for acquisition and processing. By virtue of a novel bio-inspired approach, the embedded myocontroller merges pattern recognition and factorization techniques to combine a natural selection of the robotic hand configuration with the proportional control of the related grasps. The HRI system has been fully designed, implemented, and tested on two robotic hands: a dexterous anthropomorphic hand and a three-fingered industrial gripper mounted on a robotic manipulator. The results of the test performed on four able-bodied subjects show success rates greater than 90% reached in grasping objects that require different hand shapes and impedance regulations for the task completion. The outcomes also show that the users modulate the bio-inspired degrees of control in a natural manner, proving the pertinence of the proposed system for an effective human-like control of robotic grasping devices in a wearable form factor. [ABSTRACT FROM AUTHOR]

This paper addresses the effects of electric power quality on robotic operations. A general overview is reported to highlight the main characteristics of electric power quality and it’s effects on a powered system by considering an end-user’s viewpoint. Then, the authors outline the influence of voltage dip effects by focusing on robotic grasping applications. A specific case study is reported, namely that of LARM Hand IV, a three-fingered robotic hand which has been designed and built at LARM in Cassino, Italy. A dedicated test rig has been developed and set up to generate predefined voltage dips. Experimental tests are carried out to evaluate the effects of different types of voltage dip on the grasping of objects.

In this study, we propose a strategy for delicately grasping fragile objects using a robotic gripper with highly deformable fluid fingertips. In an earlier study, we developed a soft fingertip, referred to as a fluid fingertip, which was fabricated from a deformable rubber bag filled with incompressible fluid. The proposed strategy detects the preferable grasping point where fracturing of the target object is avoided while the applied force or pressure from the fluid fingertip is substantially transmitted to the target. In this grasping point, the behavior of the fluid pressure changes with respect to the pushing distance when pushing at a constant speed. The other features of the strategy determining the grasping point are as follows: (1) the threshold for the detection of the grasping point is fixed or constant with respect to the type of target object; (2) information regarding the deformation and stiffness of the fingertips and target object is not required. The detection of the grasping point through behavioral changes in the pressure is performed by comparing the fitting accuracies of fitting models utilizing information of the fluid pressure and pushing distance. The validity of the proposed approach is verified through several experiments.

The evaluation of robotic hands is a subjectively biased, complex process. The fields pertaining to robotic hands are human-centric in nature, making human hands a good standard for benchmark comparisons of robotic hands. To achieve this, we propose a new evaluation index, where we evaluate robotic hands on three fronts: their form, features and performance. An evaluation on how anthropomorphic robotic hands are in basic mobility, and appearance constitutes the “Form„, while features that can be read, changed and actuated for effective control of robotic hands constitutes the “Features„. We derived these key features from an extensive analysis of robotic hands in literature. Finally, the robotic hands carry out a series of tasks that evaluate their “Performance„. An individual score for each category is drawn and we carry out a three-pronged analysis. We also propose an additional feature in the form of price to provide context when analysing multiple hands.

Stimulus-responsive actuators play a vital role in the new generation of intelligent systems. However, poor mechanical performance, complicated fabrication processes, and the inability to complex deformation limit their practical applications. Herein, these challenges are overcome via designing a strong hygrothermic wood actuator with asymmetric water affinity. The actuator is readily constructed by sandwiching polypyrrole-coated wood with a Ni complex hygroscopic gel top layer for moisture absorption and a polyimide bottom layer as the water barrier. The resulting hygrothermic wood spontaneously stretches and bends itself in response to moisture and thermal/light stimulation. A robotic hand and a series of grippers made of hygrothermic wood demonstrate dexterous object-hand interactions during grasping and holding, while the reversible hygrothermic property allows the actuator to be potentially applied in fire rescue scenarios to rescue trapped objects. A combination of good mechanical properties, multi-stimulus-response, complex deformation, wide working temperature range, low manufacturing cost, and biocompatibility are simultaneously realized by one device. It is thus believed that such a strong wood actuator will open up a new avenue for building intelligent robotic hand systems., (© 2023 Wiley-VCH GmbH.)

This paper proposes a design method of transmissions for underactuated mechanisms. This transmission consists of active and passive gear trains that align a series of gears over several joints. The transmission has no kinematic singular point that restricts the workspace. Thus, arbitrary joint-connected motions of the underactuated mechanisms can be designed by combining the gear trains. We investigate the kinematics and statics of underactuated mechanisms to find a transmission matrix that realizes the desired connected motions and adaptive grasps. We also develop a method to design gear trains that corresponds to the transmission matrix. We extend this design method with stepped gears and belt–gear mechanisms to increase the design flexibility. The proposed method was experimentally validated using the two- and three-jointed mechanisms. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate the feasibility of using an inductive tongue control system (ITCS) for controlling robotic/prosthetic hands and arms. Methods: This study presents a novel dual modal control scheme for multigrasp robotic hands combining standard electromyogram (EMG) with the ITCS. The performance of the ITCS control scheme was evaluated in a comparative study. Ten healthy subjects used both the ITCS control scheme and a conventional EMG control scheme to complete grasping exercises with the IH1 Azzurra robotic hand implementing five grasps. Time to activate a desired function or grasp was used as the performance metric. Results: Statistically significant differences were found when comparing the performance of the two control schemes. On average, the ITCS control scheme was 1.15 s faster than the EMG control scheme, corresponding to a 35.4% reduction in the activation time. The largest difference was for grasp 5 with a mean AT reduction of 45.3% (2.38 s). Conclusion: The findings indicate that using the ITCS control scheme could allow for faster activation of specific grasps or functions compared with a conventional EMG control scheme. Significance: For transhumeral and especially bilateral amputees, the ITCS control scheme could have a significant impact on the prosthesis control. In addition, the ITCS would provide bilateral amputees with the additional advantage of environmental and computer control for which the ITCS was originally developed. [ABSTRACT FROM AUTHOR]