Innovative Hand Exoskeleton Design for Extravehicular Activities in Space

Freni, Pierluigi; Botta, Eleonora M.; Randazzo, Luca; Ariano, Paolo

doi:10.1007/978-3-319-03958-9

Cited by 7 publications

(6 citation statements)

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“…At its highest performance, its strain can reach above 40%, stress above 0.35 MPa, work density above 40 kJ m −3 and power to mass ratio above 284 W kg −1 . Previous material‐performance comparisons have been listed in the work of Brochu et al, Freni et al, and Madden et al; newly reported values are included in their respective sections here, where available.…”

Section: Overview Of Methods and Materials For Soft Actuatorsmentioning

confidence: 99%

Soft Actuators for Small‐Scale Robotics

et al. 2016

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This review comprises a detailed survey of ongoing methodologies for soft actuators, highlighting approaches suitable for nanometer- to centimeter-scale robotic applications. Soft robots present a special design challenge in that their actuation and sensing mechanisms are often highly integrated with the robot body and overall functionality. When less than a centimeter, they belong to an even more special subcategory of robots or devices, in that they often lack on-board power, sensing, computation, and control. Soft, active materials are particularly well suited for this task, with a wide range of stimulants and a number of impressive examples, demonstrating large deformations, high motion complexities, and varied multifunctionality. Recent research includes both the development of new materials and composites, as well as novel implementations leveraging the unique properties of soft materials.

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Section: Overview Of Methods and Materials For Soft Actuatorsmentioning

confidence: 99%

Soft Actuators for Small‐Scale Robotics

et al. 2016

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“…It does not include the application of electrodes on people's skin because MMG is detected using a tiny accelerometer connected to the skin's surface. MMG usually has a higher signal-to-noise ratio than EMG (Freni et al , 2014). However, MMG is not used for clinical studies because the specific muscle needs to be fixed during rehabilitative exercise.…”

Section: Signal-sensing Techniquesmentioning

confidence: 99%

A systematic review of technological advancements in signal sensing, actuation, control and training methods in robotic exoskeletons for rehabilitation

Mathew

Thomas

Navaneeth

et al. 2022

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Purpose The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research. Design/methodology/approach Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143. Findings Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially. Research limitations/implications Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject. Originality/value The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

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“…To assist users in rehabilitation and daily life activities, a reliable mechanical structure design of hand exoskeletons is indispensable. Based on the force transmission mechanism, the hand exoskeleton can be classified as pneumatic [ 35 ], cable/tendon-driven [ 36 , 37 ], smart-material-based artificial muscle-driven [ 38 , 39 , 40 ], and linkage-driven [ 41 , 42 ] technology. ‘Stiff hand’ is usually observed in stroke patients, and significant torque force is required to perform successful rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

“…‘Stiff hand’ is usually observed in stroke patients, and significant torque force is required to perform successful rehabilitation. Artificial muscles based on smart materials such as dielectric elastomers [ 39 ] and electroactive polymers [ 40 ] are not applicable as they are usually insufficient in the generation of power, force, and deformation. Due to the compressible and temperature-sensitive nature of gas, the bending angle and bending speed of each finger joint cannot be precisely controlled by a pneumatic ‘muscle’ [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

et al. 2022

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Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

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Innovative Hand Exoskeleton Design for Extravehicular Activities in Space

Cited by 7 publications

References 0 publications

Soft Actuators for Small‐Scale Robotics

Soft Actuators for Small‐Scale Robotics

A systematic review of technological advancements in signal sensing, actuation, control and training methods in robotic exoskeletons for rehabilitation

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

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