AirExGlove — A novel pneumatic exoskeleton glove for adaptive hand rehabilitation in post-stroke patients

Stroke is one of the leading causes of disability worldwide: post-stroke disabilities affect the upper and lower limbs, significantly undermining a subject's autonomy in the Activities of Daily Living (ADLs). Among post-stroke disabilities, one of the most impairing and widespread conditions is the clenched fist deformity: the subject experiences a permanent contraction of the hand, resulting in a closed hand rest pose. In this paper, the authors propose a novel light-weight inflatable soft exoskeleton device, called the AirExGlove, to deliver high-dosage, adaptive and gradual rehabilitation therapy to patients affected by clenched fist deformity. Our system is lightweight, low-cost, adaptable to any hand size and unobtrusive. The system has been extensively tested to assess the hand-opening range in which it can operate according to the severity of the patient condition, which is typically ranked on the Modified Ashworth Scale (MAS) scale. Experimental analysis demonstrates the suitability of the glove for patients affected by post-stroke muscle spasticity scoring up to 3 out of 4 in the MAS scale. Preliminary testing with clenched-fist patient confirmed a higher level of ergonomics of the system in comparison with rigid-linked robotic systems

Design Criteria of Soft Exogloves for Hand Rehabilitation- Assistance Tasks

This paper establishes design criteria for soft exogloves (SEG) to be used as rehabilitation or assistance devices. This research consists in identifying, selecting, and grouping SEG features based on the analysis of 91 systems that have been proposed during the last decade. Thus, function, mobility, and usability criteria are defined and explicitly discussed to highlight SEG design guidelines. Additionally, this study provides a detailed description of each system that was analysed including application, functional task, palm design, actuation type, assistance mode, degrees of freedom (DOF), target fingers, motions, material, weight, force, pressure (only for fluids), control strategy, and assessment. Such characteristics have been reported according to specific design methodologies and operating principles. Technological trends are contemplated in this contribution with emphasis on SEG design opportunity areas. In this review, suggestions, limitations, and implications are also discussed in order to enhance future SEG developments aimed at stroke survivors or people with hand disabilities

Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning

There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered.This research was funded by the University of Málaga, the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, grant number RTI2018-093421-B-I00 and the European Commission, grant number BES-2016-078237. Partial funding for open access charge: Universidad de Málag

Actuation and stiffening in fluid-driven soft robots using low-melting-point material

Soft material robots offer a number of advantages over traditional rigid robots in applications including human-robot interaction, rehabilitation and surgery. These robots can navigate around obstacles, elongate, squeeze through narrow openings or be squeezed - and they are considered to be inherently safe. The ability to stiffen compliant soft actuators has been achieved by embedding various mechanisms that are generally decoupled from the actuation principle. Miniaturisation becomes challenging due to space limitations which can in turn result in diminution of stiffening effects. Here, we propose to hydraulically actuate soft manipulators with low-melting-point material and, at the same time, be able to switch between a soft and stiff state. Instead of allocating an additional stiffening chamber within the soft robot, one chamber only is used for actuation and stiffening. Low Melting Point Alloy is integrated into the actuation chamber of a single-compartment soft robotic manipulator and the interfaced robotic syringe pump. Temperature change is enabled through embedded nichrome wires. Our experimental results show higher stiffness factors, from 9-12 opposing the motion of curvature, than those previously found for jamming mechanisms incorporated in separate additional chambers, in the range of 2-8 for the same motion

Actuation and stiffening in fluid-driven soft robots using low-melting-point material

Soft material robots offer a number of advantages over traditional rigid robots in applications including humanrobot interaction, rehabilitation and surgery. These robots can navigate around obstacles, elongate, squeeze through narrow openings or be squeezed - and they are considered to be inherently safe. The ability to stiffen compliant soft actuators has been achieved by embedding various mechanisms that are generally decoupled from the actuation principle. Miniaturisation becomes challenging due to space limitations which can in turn result in diminution of stiffening effects. Here, we propose to hydraulically actuate soft manipulators with lowmelting- point material and, at the same time, be able to switch between a soft and stiff state. Instead of allocating an additional stiffening chamber within the soft robot, one chamber only is used for actuation and stiffening. Low Melting Point Alloy is integrated into the actuation chamber of a single-compartment soft robotic manipulator and the interfaced robotic syringe pump. Temperature change is enabled through embedded nichrome wires. Our experimental results show higher stiffness factors, from 9-12 opposing the motion of curvature, than those previously found for jamming mechanisms incorporated in separate additional chambers, in the range of 2-8 for the same motion

Design and integration of a parallel, soft robotic end-effector for extracorporeal ultrasound

Objective: In this work we address limitations in state-of-the-art ultrasound robots by designing and integrating a novel soft robotic system for ultrasound imaging. It employs the inherent qualities of soft fluidic actuators to establish safe, adaptable interaction between ultrasound probe and patient. Methods: We acquire clinical data to determine the movement ranges and force levels required in prenatal foetal ultrasound imaging and design the soft robotic end-effector accordingly. We verify its mechanical characteristics, derive and validate a kinetostatic model and demonstrate controllability and imaging capabilities on an ultrasound phantom. Results: The soft robot exhibits the desired stiffness characteristics and is able to reach 100% of the required workspace when no external force is present, and 95% of the workspace when considering its compliance. The model can accurately predict the end-effector pose with a mean error of 1.18+/-0.29mm in position and 0.92+/-0.47deg in orientation. The derived controller is, with an average position error of 0.39mm, able to track a target pose efficiently without and with externally applied loads. Ultrasound images acquired with the system are of equally good quality compared to a manual sonographer scan. Conclusion: The system is able to withstand loads commonly applied during foetal ultrasound scans and remains controllable with a motion range similar to manual scanning. Significance: The proposed soft robot presents a safe, cost-effective solution to offloading sonographers in day-to-day scanning routines. The design and modelling paradigms are greatly generalizable and particularly suitable for designing soft robots for physical interaction tasks

Design, fabrication and stiffening of soft pneumatic robots

Although compliance allows the soft robot to be under-actuated and generalise its control, it also impacts the ability of the robot to exert forces on the environment. There is a trade-off between robots being compliant or precise and strong. Many mechanisms that change robots' stiffness on demand have been proposed, but none are perfect, usually compromising the device's compliance and restricting its motion capabilities. Keeping the above issues in mind, this thesis focuses on creating robust and reliable pneumatic actuators, that are designed to be easily manufactured with simple tools. They are optimised towards linear behaviour, which simplifies modelling and improve control strategies. The principle idea in relation to linearisation is a reinforcement strategy designed to amplify the desired, and limit the unwanted, deformation of the device. Such reinforcement can be achieved using fibres or 3D printed structures. I have shown that the linearity of the actuation is, among others, a function of the reinforcement density and shape, in that the response of dense fibre-reinforced actuators with a circular cross-section is significantly more linear than that of non-reinforced or non-circular actuators. I have explored moulding manufacturing techniques and a mixture of 3D printing and moulding. Many aspects of these techniques have been optimised for reliability, repeatability, and process simplification. I have proposed and implemented a novel moulding technique that uses disposable moulds and can easily be used by an inexperienced operator. I also tried to address the compliance-stiffness trade-off issue. As a result, I have proposed an intelligent structure that behaves differently depending on the conditions. Thanks to its properties, such a structure could be used in applications that require flexibility, but also the ability to resist external disturbances when necessary. Due to its nature, individual cells of the proposed system could be used to implement physical logic elements, resulting in embodied intelligent behaviours. As a proof-of-concept, I have demonstrated use of my actuators in several applications including prosthetic hands, octopus, and fish robots. Each of those devices benefits from a slightly different actuation system but each is based on the same core idea - fibre reinforced actuators. I have shown that the proposed design and manufacturing techniques have several advantages over the methods used so far. The manufacturing methods I developed are more reliable, repeatable, and require less manual work than the various other methods described in the literature. I have also shown that the proposed actuators can be successfully used in real-life applications. Finally, one of the most important outcomes of my research is a contribution to an orthotic device based on soft pneumatic actuators. The device has been successfully deployed, and, at the time of submission of this thesis, has been used for several months, with good results reported, by a patient

Advances of Italian Machine Design

This 2028 Special Issue presents recent developments and achievements in the field of Mechanism and Machine Science coming from the Italian community with international collaborations and ranging from theoretical contributions to experimental and practical applications. It contains selected contributions that were accepted for presentation at the Second International Conference of IFToMM Italy, IFIT2018, that has been held in Cassino on 29 and 30 November 2018. This IFIT conference is the second event of a series that was established in 2016 by IFToMM Italy in Vicenza. IFIT was established to bring together researchers, industry professionals and students, from the Italian and the international community in an intimate, collegial and stimulating environment