In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To ...date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user's perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as "soft", "robot", "wearable", and "exoskeleton" was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.
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This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and ...safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.
Objective: This randomized controlled feasibility study investigates the ability for clinical application of the Brain-Computer Interface-based Soft Robotic Glove (BCI-SRG) incorporating activities ...of daily living (ADL)-oriented tasks for stroke rehabilitation. Methods: Eleven recruited chronic stroke patients were randomized into BCI-SRG or Soft Robotic Glove (SRG) group. Each group underwent 120-minute intervention per session comprising 30-minute standard arm therapy and 90-minute experimental therapy (BCI-SRG or SRG). To perform ADL tasks, BCI-SRG group used motor imagery-BCI and SRG, while SRG group used SRG without motor imagery-BCI. Both groups received 18 sessions of intervention over 6 weeks. Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores were measured at baseline (week 0), post- intervention (week 6), and follow-ups (week 12 and 24). In total, 10/11 patients completed the study with 5 in each group and 1 dropped out. Results: Though there were no significant intergroup differences for FMA and ARAT during 6-week intervention, the improvement of FMA and ARAT seemed to sustain beyond 6-week intervention for BCI-SRG group, as compared with SRG control. Incidentally, all BCI-SRG subjects reported a sense of vivid movement of the stroke-impaired upper limb and 3/5 had this phenomenon persisting beyond intervention while none of SRG did. Conclusion : BCI-SRG suggested probable trends of sustained functional improvements with peculiar kinesthetic experience outlasting active intervention in chronic stroke despite the dire need for large-scale investigations to verify statistical significance. Significance: Addition of BCI to soft robotic training for ADL-oriented stroke rehabilitation holds promise for sustained improvements as well as elicited perception of motor movements.
In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation ...and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.
Various robotic exoskeletons have been proposed for hand function assistance during activities of daily living (ADL) of stroke survivors. However, traditional exoskeletons involve the use of complex ...rigid systems that impede the natural movement of joints, and thus reduce the wearability and cause discomfort to the user. The objective of this paper is to design and evaluate a soft robotic glove that is able to provide hand function assistance using fabric-reinforced soft pneumatic actuators. These actuators are made of silicone rubber which has an elastic modulus similar to human tissues. Thus, they are intrinsically soft and compliant. Upon air pressurization, they are able to support finger range of motion (ROM) and generate the desired actuation of the finger joints. In this work, the soft actuators were characterized in terms of their blocked tip force, normal and frictional grip force outputs. Combining the soft actuators and flexible textile materials, a soft robotic glove was developed for grasping assistance during ADL for stroke survivors. The glove was evaluated on five healthy participants for its assisted ROM and grip strength. Pilot test was performed in two stroke survivors to evaluate the efficacy of the glove in assisting functional grasping activities. Our results demonstrated that the actuators designed in this study could generate desired force output at a low air pressure. The glove had a high kinematic transparency and did not affect the active ROM of the finger joints when it was being worn by the participants. With the assistance of the glove, the participants were able to perform grasping actions with sufficient assisted ROM and grip strength, without any voluntary effort. Additionally, pilot test on stroke survivors demonstrated that the patient's grasping performance improved with the presence and assistance of the glove. Patient feedback questionnaires also showed high level of patient satisfaction and comfort. In conclusion, this paper has demonstrated the possibility of using soft wearable exoskeletons that are more wearable, lightweight, and suitable to be used on a daily basis for hand function assistance of stroke survivors during activities of daily living.
Despite the emergence of flexible and stretchable actuators, few possess sensing capabilities. Here, we present a facile method of integrating a flexible pneumatic actuator with stretchable strain ...sensor to form a soft sensorized actuator. The elastomeric actuator comprises a microchannel connected to a controlled air source to achieve bending. The strain sensor comprises a thin layer of screen‐printed silver nanoparticles on an elastomeric substrate to achieve its stretchability and flexibility while maintaining excellent conductivity at ≈8 Ω sq–1. By printing a mesh network of conductive structures, our strain sensor is able to detect deformations beyond 20% with a high gauge factor beyond 50 000. The integration of a pneumatic soft actuator with our sensing element enables the measurement of the extent of actuator bending. To demonstrate its potential as a rehabilitation sensing actuator, we fit the sensorized actuator in a glove to further analyze finger kinematics. With this, we are able to detect irregular movement patterns in real time and assess finger stiffness or dexterity.
A strain sensing actuator comprising silver microstructured mesh network printed on a silicone elastomer is utilized for wearable soft robotic application by Yeo and co‐workers. The crosslinked structures confer robustness to the conductive elastomer with high stretchability and sensitivity. The sensorized actuator can be worn in a glove to measure finger bending and assess finger dexterity and joint stiffness.
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Miniature locomotion robots with the ability to navigate confined environments show great promise for a wide range of tasks, including search and rescue operations. Soft miniature locomotion robots, ...as a burgeoning field, have attracted significant research interest due to their exceptional terrain adaptability and safety features. Here, a fully‐soft centimeter‐scale miniature crawling robot directly powered by fluid kinetic energy generated by an electrohydraulic actuator is introduced. Through optimization of the operating voltage and design parameters, the average crawling velocity of the robot is dramatically enhanced, reaching 16 mm s−1. The optimized robot weighs 6.3 g and measures 5 cm in length, 5 cm in width, and 6 mm in height. By combining two robots in parallel, the robot can achieve a turning rate of ≈3° s−1. Additionally, by reconfiguring the distribution of electrodes in the electrohydraulic actuator, the robot can achieve 2 degrees‐of‐freedom translational motion, improving its maneuverability in narrow spaces. Finally, the use of a soft water‐proof skin is demonstrated for underwater locomotion and actuation. In comparison with other soft miniature crawling robots, this robot with full softness can achieve relatively high crawling velocity as well as increased robustness and recovery.
This paper presents a breakthrough in miniature crawling robots designed for confined environments, particularly beneficial for search and rescue. The fully‐soft centimeter‐scale robot, powered by fluid‐kinetic‐energy, achieves a remarkable crawling velocity of 16 mm s−1. The robot's soft, water‐proof skin further enables underwater locomotion, distinguishing it for its high crawling speed, robustness, and recovery compared to other soft miniature crawling robots.
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This letter presents a fully fabric-based bidirectional soft robotic glove designed to assist hand impaired patients in rehabilitation exercises and performing activities of daily living. The glove ...provides both active finger flexion and extension for hand assistance and rehabilitative training, through its embedded fabric-based actuators that are fabricated by heat press and ultrasonic welding of flexible thermoplastic polyurethane-coated fabrics. Compared to previous developed elastomeric-based actuators, the actuators are able to achieve smaller bend radius and generate sufficient force and torque to assist in both finger flexion and extension at lower air pressure. In this letter, experiments were conducted to characterize the performances of the glove in terms of its kinematic and grip strength assistances on five healthy participants. Additionally, we present a graphical-user interface that allows user to choose the desired rehabilitation exercises and control modes, which include button-controlled-assistive mode, cyclic movement training, intention-driven task-specific training, and bilateral rehabilitation training.
Recent advancements in soft robotics have seen the rapid development of soft grippers for industrial pick-and-place applications. They are, however, ill-suited to bear heavy loads due to their ...compliant nature. Paradoxically, researchers have sought to increase the stiffness of soft grippers to improve load-bearing capabilities. Unfortunately, contemporary soft actuators with variable stiffness are fabricated using manual processes and their performance is subject to an individual's mastery. They are therefore not reliable for long-term industrial use. In this article, we present our work on a 3-D-printed metal-endoskeleton-reinforced actuator (MERA) for industrial pick-and-place applications. We also highlight the fabrication processes needed to recreate it repetitively. Using stainless steel splints (SSS), we demonstrate that MERA is able to modulate its stiffness at selective junctures for stable and effective grasping. We also describe our design rationale with a qualitative mathematical model and validate its performance quantitatively using a finite element model, which is further investigated in the following fatigue test. In our experiments, the MERA equipped with SSS is able to output a peak tip force of 8 N, which is a 291% increase compared to the one without metallic reinforcement. In addition, an increase of 76.5% in gripping load and a maximum holding force per actuator of 13.8 N are realized through the stiffness tuning of a MERA-Gripper. Despite significantly improving load-bearing capabilities, the actuator manages to retain an overall low profile with a weight of 82 g. Finally, we adapted the MERA into a reconfigurable gripper and tested its grasping capabilities on objects of various shapes, sizes, and weights.
Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital‐twin‐based remote interactive system for advanced robotic‐enabled industrial automation ...and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T‐TENG) and length (L‐TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T‐TENG and L‐TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital‐twin‐based virtual shop is successfully implemented to provide the users with real‐time feedback about the details of the product. In general, by offering a more immersive experience in human–machine interactions, the proposed remote interactive system shows the great potential of being the advanced human–machine interface for the applications of the unmanned working space.
A smart soft robotic manipulator is developed with a self‐powered multifunctional sensory system for simultaneously deformation, tactile, and temperature perception. With machine learning analysis, automatic recognition of grasped objects can be realized with high accuracy. By leveraging the artificial intelligence of things (AIoT) technology, a digital‐twin‐based virtual shop is successfully implemented to provide users with a more immersive shopping experience.
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