Soft Robotic Glove Research Articles

Let Me Give You a Hand: Enhancing Human Grasp Force With a Soft Robotic Assistive Glove

Let Me Give You a Hand: Enhancing Human Grasp Force With a Soft Robotic Assistive Glove

A magnetically controlled soft robotic glove for hand rehabilitation

A magnetically controlled soft robotic glove for hand rehabilitation

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Integrated design and manufacturing of a soft robotic glove with piezoresistive sensors

Soft robotic gloves that assist people in acquiring hand movement and gesture for various applications have been developed by various design and manufacturing methods over the decades. Major concerns in the recent development of gloves involve finger-related sensibility and effectiveness of movement acquisition as applied by various means for different applications. Also, remarkable progress in developing advanced materials and structures has resulted in promising design and fabrication using different methods. This paper reports a hybrid 3D printing approach that can be realized by an integrated design and manufacturing (IDM) scheme with direct ink writing (DIW) and fused deposition modeling (FDM) sequentially. Preparing a conductive polymer matrix composite (PMC) incorporating graphene and graphite with silicone rubber, the DIW sensor of the PMC embedded onto an FDM robotic glove of thermoplastic polyurethane was illustrated to demonstrate the piezoresistive effect on deformation of the structure in which an essential change in the electrical resistance was detected during bending and recovering. It showed remarkable sensibility over the deformation of the resistors and the fingers’ bending with various degrees. In the future, innovative composites and structures of robotic gloves based on DIW and FDM of the present IDM scheme could be further explored in applications such as human-machine interfaces.

Soft Robotic Glove Modulating Joint Torque Through Novel Passive Extensor Mechanisms

Soft Robotic Glove Modulating Joint Torque Through Novel Passive Extensor Mechanisms

Design Optimization of a Soft Robotic Rehabilitation Glove Based on Finger Workspace Analysis.

The finger workspace is crucial for performing various grasping tasks. Thus, various soft rehabilitation gloves have been developed to assist individuals with paralyzed hands in activities of daily living (ADLs) or rehabilitation training. However, most soft robotic glove designs are insufficient to assist with various hand postures because most of them use an underactuated mechanism for design simplicity. Therefore, this paper presents a methodology for optimizing the design of a high-degree-of-freedom soft robotic glove while not increasing the design complexity. We defined the required functional workspace of the index finger based on ten frequently used grasping postures in ADLs. The design optimization was achieved by simulating the proposed finger-robot model to obtain a comparable workspace to the functional workspace. In particular, the moment arm length for extension was optimized to facilitate the grasping of large objects (precision disk and power sphere), whereas a torque-amplifying routing design was implemented to aid the grasping of small objects (lateral pinch and thumb-two-finger pinch). The effectiveness of the optimized design was validated through testing with a stroke survivor and comparing the assistive workspace. The observed workspace demonstrated that the optimized glove design could assist with nine out of the ten targeted grasping posture functional workspaces. Furthermore, the assessment of the grasping speed and force highlighted the glove's usability for various rehabilitation activities. We also present and discuss a generalized methodology to optimize the design parameters of a soft robotic glove that uses an underactuated mechanism to assist the targeted workspace. Overall, the proposed design optimization methodology serves as a tool for developing advanced hand rehabilitation robots, as it offers insight regarding the importance of routing optimization in terms of the workspace.

3D-printed passive bellow actuator for portable soft wearable robots

The compliance of soft wearable robots driven by fluids is high, but their portability and controllability are limited due to complex fluidic systems. On the other hand, tendon-driven soft wearable robots are compact and easy to control, but they have lower compliance when actively interacting with unknown environments. To address this trade-off between compliance and controllability, we propose a novel actuator design for soft wearable robots, named the passive bellow actuator (PBA). The PBA is 3D-printed using elastic materials, which enables it to be easily customized into various shapes and sizes. When tendons running through the PBA are pulled, it contracts and preserves elastic potential energy. When the tendons are released, the PBA extends like a spring and exerts the stored elastic energy to drive the human body. Additionally, programmable deformation can be easily achieved by adjusting the thickness of the PBA chamber. By utilizing these effects, the PBA can be used to assist human flexion and extension movements. We developed a portable soft robotic glove to demonstrate the feasibility of the PBA. The glove is light weight, power safe, and is inherently compliant when grasping irregular objects. Theoretical modeling and experimental tests were conducted to characterize the PBA, and experimental tests were conducted to demonstrate the performance of the soft robotic gloves.

Therapy effect on hand function after home use of a wearable assistive soft-robotic glove supporting grip strength.

Soft-robotic gloves with an assist-as-needed control have the ability to assist daily activities where needed, while stimulating active and highly functional movements within the user's possibilities. Employment of hand activities with glove support might act as training for unsupported hand function. To evaluate the therapeutic effect of a grip-supporting soft-robotic glove as an assistive device at home during daily activities. This multicentre intervention trial consisted of 3 pre-assessments (averaged if steady state = PRE), one post-assessment (POST), and one follow-up assessment (FU). Participants with chronic hand function limitations were included. Participants used the Carbonhand glove during six weeks in their home environment on their most affected hand. They were free to choose which activities to use the glove with and for how long. The primary outcome measure was grip strength, secondary outcome measures were pinch strength, hand function and glove use time. 63 patients with limitations in hand function resulting from various disorders were included. Significant improvements (difference PRE-POST) were found for grip strength (+1.9 kg, CI 0.8 to 3.1; p = 0.002) and hand function, as measured by Jebson-Taylor Hand Function Test (-7.7 s, CI -13.4 to -1.9; p = 0.002) and Action Research Arm Test (+1.0 point, IQR 2.0; p≤0.001). Improvements persisted at FU. Pinch strength improved slightly in all fingers over six-week glove use, however these differences didn't achieve significance. Participants used the soft-robotic glove for a total average of 33.0 hours (SD 35.3), equivalent to 330 min/week (SD 354) or 47 min/day (SD 51). No serious adverse events occurred. The present findings showed that six weeks use of a grip-supporting soft-robotic glove as an assistive device at home resulted in a therapeutic effect on unsupported grip strength and hand function. The glove use time also showed that this wearable, lightweight glove was able to assist participants with the performance of daily tasks for prolonged periods.

Combining soft robotics and telerehabilitation for improving motor function after stroke.

Telerehabilitation and robotics, either traditional rigid or soft, have been extensively studied and used to improve hand functionality after a stroke. However, a limited number of devices combined these two technologies to such a level of maturity that was possible to use them at the patients' home, unsupervised. Here we present a novel investigation that demonstrates the feasibility of a system that integrates a soft inflatable robotic glove, a cloud-connected software interface, and a telerehabilitation therapy. Ten chronic moderate-to-severe stroke survivors independently used the system at their home for 4weeks, following a software-led therapy and being in touch with occupational therapists. Data from the therapy, including automatic assessments by the robot, were available to the occupational therapists in real-time, thanks to the cloud-connected capability of the system. The participants used the system intensively (about five times more movements per session than the standard care) for a total of more than 8hr of therapy on average. We were able to observe improvements in standard clinical metrics (FMA +3.9±4.0, p<.05, COPM-P+2.5±1.3, p<.05, COPM-S+2.6±1.9, p<.05, MAL-AOU +6.6±6.5, p<.05) and range of motion (+88%) at the end of the intervention. Despite being small, these improvements sustained at follow-up, 2weeks after the end of the therapy. These promising results pave the way toward further investigation for the deployment of combined soft robotic/telerehabilitive systems at-home for autonomous usage for stroke rehabilitation.

Assist-as-needed control with a soft robotic glove based on human-object contact estimation

Assist-as-needed control with a soft robotic hand glove for active rehabilitation is studied in this work. There are two resources of the grasping force, the robotic glove and the subject. Compared with traditional passive rehabilitation where the grasping force is merely provided by a robotic hand rehabilitation device (such as hand exoskeleton, robotic glove), assist-as-needed control accounts for the user contribute to performing grasping tasks collaboratively. In this control method, the human muscle strength for grasping is estimated through the myoelectrical signals of the human forearm collected by the MYO armband. A neural network is used for the recognition of human-object contact estimation. The assist-as-needed control is finally implemented to assist humans in grasping tasks. Experiment results on a soft robotic glove show the effectiveness of the proposed assistive control method.

A bidirectional fabric-based soft robotic glove for hand function assistance in patients with chronic stroke

BackgroundChronic stroke patients usually experience reduced hand functions, impeding their ability to perform activities of daily living (ADLs) independently. Additionally, improvements in hand functions by physical therapy beyond six months after the initial onset of stroke are much slower than in the earlier months. As such, chronic stroke patients could benefit from an assistive device to enhance their hand functions, allowing them to perform ADLs independently daily. In recent years, soft robotics has provided a novel approach to assistive devices for motor impaired individuals, offering more compliant and lightweight alternatives to traditional robotic devices. The scope of this study is to demonstrate the viability of a fabric-based soft robotic (SR) glove with bidirectional actuators in assisting chronic stroke study participants with hand impairments in performing ADLs.MethodsForce and torque measurement tests were conducted to characterize the SR Glove, and hand functional tasks were given to eight chronic stroke patients to assess the efficacy of the SR Glove as an assistive device. The tasks involved object manipulation tasks that simulate ADLs, and the series of tasks was done by the participants once without assistance for baseline data, and once while using the SR Glove. A usability questionnaire was also given to each participant after the tasks were done to gain insight into how the SR Glove impacts their confidence and reliance on support while performing ADLs.ResultsThe SR Glove improved the participants’ manipulation of objects in ADL tasks. The difference in mean scores between the unassisted and assisted conditions was significant across all participants. Additionally, the usability questionnaire showed the participants felt more confident and less reliant on support while using the SR Glove to perform ADLs than without the SR Glove.ConclusionsThe results from this study demonstrated that the SR Glove is a viable option to assist hand function in chronic stroke patients who suffer from hand motor impairments.

Lightweight Soft Robotic Glove with Whole-Hand Finger Motion Tracking for Hand Rehabilitation in Virtual Reality.

Soft robotic gloves have attracted significant interest in hand rehabilitation in the past decade. However, current solutions are still heavy and lack finger-state monitoring and versatile treatment options. To address this, we present a lightweight soft robotic glove actuated by twisted string actuators (TSA) that provides whole-hand finger motion tracking. We have developed a virtual reality environment for hand rehabilitation training, allowing users to interact with various virtual objects. Fifteen small inertial measurement units are placed on the glove to predict finger joint angles and track whole-hand finger motion. We performed TSA experiments to identify design and control rules, by understanding how their response varies with input load and voltages. Grasping experiments were conducted to determine the grasping force and range of motion. Finally, we showcase an application of the rehabilitation glove in a Unity-based VR interface, which can actuate the operator's fingers to grasp different virtual objects.

Training Induced Improvement on Human Perception of the Force Feedback Provided by A Soft Robotic Glove.

Creating haptic interface by glove-based wearable robotic system has become an increasingly interested topic in the area of human robotic interaction. Many force feedback gloves are constructed based on soft actuators. However, the recent development of haptic and force feedback technology mainly focused on the advancement of the actuating components and mechanism, and innovation of the force feedback rendering algorithms. It seems that another important part of this human-robot-interaction loop, i.e. the human factors, were understudied. Here, this study focused on the learning effect in haptic perception. We designed a pneumatic muscle-based force feedback robotic glove which can provide customized force feedback to the dorsal surface of each finger. An experiment was carried out with a specifically training procedure and evaluation on the force feedback perceptions. The results show that practice-induced improvement can be achieved by this training, allowing people have a better perception of the force feedback provided by this glove.

Design, Modeling and Experiments of a Variable Stiffness Soft Robotic Glove for Stroke Patients With Clenched Fist Deformity

Soft wearable devices for hand rehabilitation can assist activities of daily living. While existing soft robotic gloves can help with finger flexion, they are less suitable for patients who cannot extend their fingers due to poststroke increased muscular tension. In this letter, we proposed a variable stiffness pneumatic actuator that effectively increases the elasticity of the actuator to facilitate finger extension. The actuator consists of a fiber-reinforced actuator and a multi-stage articulated elastomer. It bends and extends by pneumatic actuation, with multi-stage articulated elastomer providing stiffness adjustment to accommodate varying joint resistance. The variable stiffness mechanism was achieved by the multi layered discrete metal plates which can gracefully fit the unique structure of human fingers. Analysis models were established to quantify the actuator's air pressure, vacuum pressure, and bending angle, also to predict the actuator's stiffness under different vacuum pressures. Experiments and finite element simulation were carried out to evaluate the bending performance of the actuator. Meanwhile, the output force and variable stiffness characteristic of the actuator were tested. The results show that the actuator can achieve 1.54 N fingertip output force and 2.4 times stiffness change, which satisfy the requirement of stroke patient. In addition, a clenched artificial hand with similar damper forces to human hand was applied to further verify the extending effect of the variable stiffness actuator. Finally, a rehabilitation glove integrated with multiple actuators was tested to assess typical rehabilitation gestures and the ability to grasp daily objects.

A Review of Rehabilitative and Assistive Technologies for Upper-Body Exoskeletal Devices

This journal review article focuses on the use of assistive and rehabilitative exoskeletons as a new opportunity for individuals with diminished mobility. The article aims to identify gaps and inconsistencies in state-of-the-art assistive and rehabilitative devices, with the overall goal of promoting innovation and improvement in this field. The literature review explores the mechanisms, actuators, and sensing procedures employed in each application, specifically focusing on passive shoulder supports and active soft robotic actuator gloves. Passive shoulder supports are an excellent option for bearing heavy loads, as they enable the load to be evenly distributed across the shoulder joint. This, in turn, reduces stress and strain around the surrounding muscles. On the other hand, the active soft robotic actuator glove is well suited for providing support and assistance by mimicking the characteristics of human muscle. This review reveals that these devices improve the overall standard of living for those who experience various impairments but also encounter limitations requiring redress. Overall, this article serves as a valuable resource for individuals working in the field of assistive and rehabilitative exoskeletons, providing insight into the state of the art and potential areas for improvement.

Soft Robotic Glove with Sensing and Force Feedback for Rehabilitation in Virtual Reality.

Many diseases, such as stroke, arthritis, and spinal cord injury, can cause severe hand impairment. Treatment options for these patients are limited by expensive hand rehabilitation devices and dull treatment procedures. In this study, we present an inexpensive soft robotic glove for hand rehabilitation in virtual reality (VR). Fifteen inertial measurement units are placed on the glove for finger motion tracking, and a motor-tendon actuation system is mounted onto the arm and exerts forces on fingertips via finger-anchoring points, providing force feedback to fingers so that the users can feel the force of a virtual object. A static threshold correction and complementary filter are used to calculate the finger attitude angles, hence computing the postures of five fingers simultaneously. Both static and dynamic tests are performed to validate the accuracy of the finger-motion-tracking algorithm. A field-oriented-control-based angular closed-loop torque control algorithm is adopted to control the force applied to the fingers. It is found that each motor can provide a maximum force of 3.14 N within the tested current limit. Finally, we present an application of the haptic glove in a Unity-based VR interface to provide the operator with haptic feedback while squeezing a soft virtual ball.

Finger Flexion and Extension Driven by a Single Motor in Robotic Glove Design

Pneumatic and tendon‐driven actuators are widely used in soft robotic glove design. Tendon‐driven robotic gloves are generally better in controllability, dexterity, and force output, but they are less comfortable than pneumatic ones. Most soft gloves focus on only one actuation mode where either motor‐driven tendon or pump‐driven pneumatic transmission is used. Herein, a double‐acting soft actuator (DASA) that provides both tendon‐driven flexion and pneumatic extension of fingers by a single motor is presented. This is achieved by a smart pulley and bellow system. The kinematic model of the tendon‐driven flexion and the torque model of the fabric‐based pneumatic extension actuator (FPEA) are developed to analyze the DASA performance. The bending angle of the index finger actuated by the tendon and the FPEA extension torque of a joint are characterized by experimental studies. A cycle test of the DASA is conducted 3000 times, demonstrating its high repeatability. A prototype soft glove (68 g) based on the proposed DASA with a control box (835 g) is fabricated to demonstrate finger flexion and extension assistance. Based on electromyography signals, the performance of the robotic glove is evaluated by a squeezing sponge test.

Effectiveness of soft robotic glove versus repetitive transcranial magnetic stimulation in post-stroke patients with severe upper limb dysfunction: A randomised controlled trial.

To explore the difference in rehabilitation effect between soft robot gloves and repetitive transcranial magnetic stimulation (rTMS) in patients with severe upper limb motor dysfunction after a stroke. A total of 69 post-stroke patients with severe upper limb dysfunction were randomly assigned to a repetitive transcranial magnetic group, a soft robotic glove group, and a conventional treatment group. The primary outcomes were the Fugl-Meyer Upper Extremity Assessment (FMA-UE) and the Modified Barthel Index (MBI). The secondary endpoints were the amplitude surface electromyogram of the extensor wrist muscle (sEMG) and the cerebral hemispheric resting motor threshold (RMT). The change of FMA-UE score in the soft robotic glove group was significantly better than that in the conventional treatment group (median difference: 2 points; 95% confidence interval [1, 3]; P < 0.05), but there was no significant difference compared with the repetitive transcranial magnetic stimulation group (median difference: 0 points; 95% confidence interval [-1, 2]; P [0.547] > 0.05). There was no significant difference in the change of MBI score between the soft robotic glove group and the conventional treatment and repetitive transcranial magnetic treatment groups [F = 2.458, P [0.093] > 0.05]. There was no significant difference in the change of sEMG score between the soft robotic glove group and the conventional treatment and repetitive transcranial magnetic treatment groups [H = 0.042, P [0.980] > 0.05]. Additionally, the change of RMT score in the soft robotic glove group was significantly inferior to that in the repetitive transcranial magnetic treatment group [difference: -1.09; 95% confidence interval [-2.048, 0.048]; P < 0.05], but there was no significant difference compared with the conventional treatment group [difference: 0.31 points; 95% confidence interval [-0.879, 0.358]; P [0.495] > 0.05]. For patients with severe dyskinesia after a stroke, soft robotic gloves are as effective as repetitive transcranial magnetic stimulation and may be a good choice for home rehabilitation. In addition, conventional treatment combined with repetitive transcranial magnetic stimulation (rTMS) or a soft robotic glove produced better rehabilitation outcomes than conventional treatment alone.

Cooperative Hand Therapy via a Soft, Wearable, and Unilateral Telerobotic System.

Functional rehabilitation of the hand is a complex and difficult process involving a large number of degrees of freedom (DOFs). Soft wearable hand-rehabilitation robots have assisted hand movements with a compact structural design, but effective rehabilitation requires an intuitive control scheme that can manage many DOFs and incorporate interaction with an occupational therapist, which has yet to be developed for this type of device. Thus, we present a soft wearable unilateral telerobotic system that enables various grasping tasks and cooperative interaction between the patient and therapist. The presented system consists of a sensor glove that measures the hand postures of the occupational therapist and a soft robotic glove that assists 4-DOF movements of the patient's hand, including adjustments of the interjoint coordination of the finger and 2-DOF movements of the thumb (flexion/extension and opposition/reposition). The soft robotic glove effectuates hand movements based on the measurements from the sensor glove. A telerobotic impedance-control scheme provides intuitive guidance of various hand postures, along with a fingertip-force vector. The feasible workspace and control performance of the system were evaluated on a healthy recruit and a poststroke patient. The presented system allowed the therapist to increase the patient's thumb workspace by 400% in palmar-dorsal direction and to control the fingertip-force direction at -30°∼10° range by enabling control of interjoint coordination of the proximal-interphalangeal and metacarpophalangeal joints. These features facilitate patients to perform various postures for stable object grasping. The presented rehabilitation system is suitable for noncontact telehealthcare that facilitates patient-therapist interactions.

Effect of Segment Types on Characterization of Soft Sensing Textile Actuators for Soft Wearable Robots.

The use of textiles in soft robotics is gaining popularity because of the advantages textiles offer over other materials in terms of weight, conformability, and ease of manufacture. The purpose of this research is to examine the stitching process used to construct fabric-based pneumatic bending actuators as well as the effect of segment types on the actuators' properties when used in soft robotic glove applications. To impart bending motion to actuators, two techniques have been used: asymmetry between weave and weft knit fabric layers and mechanical anisotropy between these two textiles. The impacts of various segment types on the actuators' grip force and bending angle were investigated further. According to experiments, segmenting the actuator with a sewing technique increases the bending angle. It was discovered that actuators with high anisotropy differences in their fabric combinations have high gripping forces. Textile-based capacitive strain sensors are also added to selected segmented actuator types, which possess desirable properties such as increased grip force, increased bending angle, and reduced radial expansion. The sensors were used to demonstrate the controllability of a soft robotic glove using a closed-loop system. Finally, we demonstrated that actuators integrated into a soft wearable glove are capable of grasping a variety of items and performing various grasp types.