Pneumatic Glove Research Articles

Design and application of pneumatic rehabilitation glove system based on brain-computer interface.

Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients.

Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints were obtained using the SURF and RANSAC algorithms. Furthermore, the trajectory of finger joint end and the correlation equation of finger joint motion were fitted, and a comprehensive finger kinematic model was established. Based on this finger kinematic model, a novel under-actuated exoskeleton mechanism was designed using a human-machine integration approach. The novel robotic glove fully aligns with the equivalent rotation centers and natural motion trajectories of the fingers, exerting minimal and evenly distributed dynamic pressure on the fingers, with a theoretical static pressure value of zero. Experiments involving gripping everyday objects demonstrated that the novel robotic glove significantly reduces the overall pressure on the fingers during grasping compared to the pneumatic glove and the traditional exoskeleton robotic glove. It is suitable for long-term use by stroke patients for rehabilitation training.

HaptGlove-Untethered Pneumatic Glove for Multimode Haptic Feedback in Reality-Virtuality Continuum.

Novel haptics technologies are urgently needed to bridge the gap between entirely physical world and fully digital environment to render a more realistic and immersive human-computer interaction. Current virtual reality (VR) haptic gloves either deliver limited haptic feedback or are bulky and heavy. The authors develop a haptic glove or HaptGlove, an untethered and lightweight pneumatic glove, that allows users to "physically" interact in a VR environment and enables both kinesthetic and cutaneous sensations naturally and realistically. Integrated with five pairs of haptic feedback modules and fiber sensors, HaptGlove provides variable stiffness force feedback and fingertip force and vibration feedback, allowing users to touch, press, grasp, squeeze, and pull various virtual objects and feel the dynamic haptic changes. Significant improvements in VR realism and immersion areobserved in a user study with participants achieving 78.9% accuracy in sorting six virtual balls of different stiffnesses. Importantly, HaptGlove facilitates VR training, education, entertainment, and socialization in a reality-virtuality continuum.

A Novel Soft Glove Utilizing Honeycomb Pneumatic Actuators (HPAs) for Assisting Activities of Daily Living.

Fabric-based pneumatic actuators (FPAs) are extensively employed in the design of lightweight and compliant soft wearable assistive gloves. However, conventional FPAs typically exhibit limited output force, thereby restricting the applications of such gloves. This paper presents the development of a novel honeycomb pneumatic actuator (HPA) constructed using flexible thermoplastic polyurethane (TPU) coating through hot pressing or ultrasonic welding techniques. Compared to the previously utilized double-layer fabric-based pneumatic actuators (DLFPAs), the HPAs yields a remarkable 862% increase in end output force. It can produce a tip force of 13.57 N at a pressure of 150 kPa. The integration of HPAs onto a soft pneumatic glove enables the facilitation of various activities of daily living. A series of trials involving nine patients were conducted to assess the effectiveness of the soft glove. The experimental results indicate that when assisted by the glove, the patients' finger metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints achieved angles of 87.67 ± 19.27° and 64.2 ± 30.66°, respectively. Additionally, the average fingertip force reached 10.16 ± 4.24 N, the average grip force reached 26.04 ± 15.08 N, and the completion rate of daily functions for the patients increased from 39% to 76%. These outcomes demonstrate that the soft glove effectively aids in finger movements and significantly enhances the patients' daily functioning.

Design of Guided Bending Bellows Actuators for Soft Hand Function Rehabilitation Gloves

This study developed a soft pneumatic glove actuated by elliptical cross-sectional guided bending bellows to augment finger-knuckle rehabilitation for patients with hand dysfunction. The guided bending bellows actuators (GBBAs) are made of thermoplastic elastomer (TPE) materials, demonstrating the necessary air tightness as a pneumatic actuator. The GBBAs could produce different moments of inertia when increasing internal air pressure drives the GBBAs bending along distinct symmetry planes and exhibits anisotropic kinematic bending performance. Actuated by GBBAs, wearable soft rehabilitation gloves can be used for daily rehabilitation training of hand dysfunction to enhance the range of motion of the finger joint. To control each finger of the gloves independently to achieve the function of manipulating gestures, a multi-channel pneumatic control system is designed, and each air circuit is equipped with an air-pressure sensor to make adjustments based on feedback. Compared with general soft robotic exoskeleton gloves currently used for hand dysfunction, the GBBAs actuated soft gloves have the advantage of enhancing the rehabilitation strength, finger movement range, and multi-action coordination applied with guided bending bellows actuators.

A soft pneumatic glove with multiple rehabilitation postures and assisted grasping modes

This article proposes a novel soft pneumatic glove with multiple rehabilitation postures and assisted grasping modes, which is composed of five segmented pneumatic networks (PneuNets) bidirectional bending actuators with a variable height structure. The metacarpophalangeal (MCP) joint chambers and the distal phalangeal (DIP) joint chambers are designed with a trapezoidal side profile, which enhances the reverse bending ability and can effectively expand the range of hand rehabilitation activities. In order to avoid hindering the forward bending, the chambers of the reverse bending module are designed as a triangular prism chamber. Experimental tests were performed and the results show that the soft actuator can produce a bending angle of 261° under an air pressure of 100 kPa, and can produce a fingertip force of 1.59 N under an air pressure of 130 kPa. In the rehabilitation mode, the designed soft rehabilitation gloves can help the hand to form four kinds of rehabilitation postures, namely full fist, straight fist, hook fist and table top. By ventilating different finger joints, and it can also help the fingers to stretch. Under the initial air pressure of 60 kPa, the design of the reverse bending module can increase the fingertip force of reverse bending by 40 %. In the assisted grasping mode, by adjusting the input air pressure, it can help the hand to grasp objects with cylindrical and large plane contour, which is very useful for the rehabilitation of patients with partial loss of hand function. • A novel soft pneumatic glove with multiple rehabilitation postures and assisted grasping modes is proposed. • The soft glove is composed of five PneuNets bidirectional bending actuators with a variable height structure. • The soft actuator can produce a angle of 261° under pressure of 100 kPa, and a force of 1.59 N under pressure of 130 kPa.

A Preliminary Study to Design and Evaluate Pneumatically Controlled Soft Robotic Actuators for a Repetitive Hand Rehabilitation Task.

A stroke is an infarction in the cortical region of the brain that often leads to isolated hand paresis. This common side effect renders individuals compromised in their ability to actively flex or extend the fingers of the affected hand. While there are currently published soft robotic glove designs, this article proposed a unique design that allows users to self-actuate their therapy due to the ability to re-extend the hand using a layer of resistive flexible steel. The results showed a consistently achieved average peak of 75° or greater for each finger while the subjects’ hands were at rest during multiple trials of pneumatic assisted flexion. During passive assisted testing, human subject testing on 10 participants showed that these participants were able to accomplish 80.75% of their normal active finger flexion range with the steel-layer-lined pneumatic glove and 87.07% with the unlined pneumatic glove on average when neglecting outliers. An addition of the steel layer lowered the blocked tip force by an average of 18.13% for all five fingers. These data show strong evidence that this glove would be appropriate to advance to human subject testing on those who do have post stroke hand impairments.

The Soft Compiler: A Web-Based Tool for the Design of Modular Pneumatic Circuits for Soft Robots

Developing soft circuits from individual soft logic gates poses a unique challenge: with increasing numbers of logic gates, the design and implementation of circuits lead to inefficiencies due to mathematically unoptimized circuits and wiring mistakes during assembly. It is therefore practically important to introduce design tools that support the development of soft circuits. We developed a web-based graphical user interface, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the Soft Compiler</i> , that accepts a user-defined robot behavior as a truth table to generate a mathematically optimized circuit diagram that guides the assembly of a soft fluidic circuit. We describe the design and experimental verification of three soft circuits of increasing complexity, using the Soft Compiler as a design tool and a novel pneumatic glove as an input interface. In one example, we reduce the size of a soft circuit from the original 11 logic gates to 4 logic gates while maintaining circuit functionality. The Soft Compiler is a web-based design tool for fluidic, soft circuits and published under an open-source MIT License.

Probabilistic Model-Based Learning Control of a Soft Pneumatic Glove for Hand Rehabilitation.

Stroke survivors are usually unable to perform activities of daily living (ADL) independently due to loss of hand functions. Soft pneumatic gloves provide a promising assistance approach for stroke survivors to conduct ADL tasks. However, few studies have explored effective control strategies for the 'human-soft robot' integrated system due to challenges in the nonlinearities of soft robots and uncertainties of human intentions. Therefore, this work aims to develop control approaches for the system to improve stroke survivors' hand functions. Firstly, a soft pneumatic glove was utilized to aid with stroke-impaired hands. Secondly, a probabilistic model-based learning control approach was proposed to overcome the challenges. Then a task-oriented intention-driven training modality was designed. Finally, the control performance was evaluated on three able-bodied subjects and three stroke survivors who attended 20-session rehabilitation training. The proposed approach could enable the soft pneumatic glove to provide adaptive assistance for all participants to accomplish different tasks. The tracking error and muscle co-contraction index showed decreasing trends while the hand gesture index showed an increasing tendency over training sessions. All stroke survivors showed improved hand functions and better muscle coordinations after training. This work developed a learning-based soft robotic glove training system and demonstrated its potential in post-stroke hand rehabilitation. This work promotes the application of soft robotic training systems in stroke rehabilitation.

Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors.

The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

A Wearable Soft Robot for Stroke Patients’ Finger Occupational Therapy and Quantitative Measures on the Joint Paralysis

We suggest a wearable soft robot for post-stroke, hemiplegic patients’ finger rehabilitation and quantitative evaluations on the joint paralysis. The device consists of a pair of gloves. One measures finger positions of the normal side, and the other induces symmetric movements on the affected side by pneumatic force. Ten patients at Brunnstrom stage 3 and 4 from a local hospital participated in this study. They performed Rolyan Stacking Cones 10 times with and without the support of the soft robot. We measured subjects’ proximal interphalangeal angles and air pressures in the pneumatic glove during the exercise to monitor grab/release patterns. The soft robot helped open their paralyzed finger joints by more than 50 degrees on average regardless of Brunnstrom stages. We applied pattern recognition methods on the measurement to quantitatively evaluate the subjects. A support vector machine revealed a misclassification rate of 20%, implying that there were a considerable number of overlapping data sets near the boundary between Brunnstrom stages. K-means method with three clusters suggested a new subject group near the support vector machine border. Thus, we conclude that our wearable soft robot not only provides grab/release guides to post-stroke patients but also provides quantitative information on their finger paralysis supplementary to the existing qualitative assessments.

Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home

The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which is a motor recovery system with the intent of rehabilitation, focuses on the hand organs and utilizes a brain-computer interface (BCI) technology. The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%. Moreover, the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Arti- ficial Neural Networks (ANN). The overall model performance reveals an accuracy up to 96.56% with sensitivity of 94.22% and specificity of 98.8%. Therefore, the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.

Design, Modeling, and Testing of a Soft Pneumatic Glove With Segmented PneuNets Bending Actuators

This paper presents the design, modeling, and testing of a soft pneumatic glove with five segmented PneuNets bending actuators (SPBAs) made of elastomer, each actuator driving the corresponding finger to bend. The segmented structure of the actuator is designed according to the anatomy of human fingers, i.e., three flexible segments for finger joints and four inflexible segments for finger metacarpal and phalanges, which makes the actuator fit with finger better than those of homogenous structure. The number of inflatable chambers in the flexible segment can be designed according to range of motion (ROM) of the corresponding joint. To improve the air tightness of the actuator, an inner bladder is embedded into chambers. To predict the passive bending degree and grasping force of human fingers driven by the glove, a bottom–up model is constructed by using Yeoh's theory of hyperelasticity and Lagrange equations of serial robots. The model is verified by FEM simulations and experiments simultaneously. At last, some passive exercises are presented, which indicates its potential in hand rehabilitation.

Effect of Pneumatic Boxing Gloves on Impact Kinematics and Their Relationship to Impact Forces

This study was aimed at improved understanding of the mechanisms of previously reported protective effects of a pneumatic boxing glove. A Motion Capture System was used to obtain velocity data from four different boxing gloves dropped on to a force plate from nine heights ranging from 1 to 5 metres. Two gloves were of the conventional type but differed in mass. The other two were prototype pneumatic gloves. One of these (SBLI) had a sealed bladder while the other (ARLI) incorporated a port allowing air exchange with the external environment. The pneumatic gloves decelerated more slowly than the conventional gloves following impact and compressed through a greater absolute distance. Consequently, they took longer to reach zero velocity. As drop height increased, these trends became more pronounced for the ARLI glove than the SBLI glove. Increase in velocity during rebound was also slower for the pneumatic gloves. The ARLI glove had a lower coefficient of restitution than any of the other gloves at low to moderate drop heights but not at high drop heights. The SBLI glove had a higher coefficient of restitution than the other gloves at all drop heights from 2 metres upwards. This indicated that, overall, the ARLI glove was the most effective, and the SBLI glove the least effective, in dissipating the kinetic energy of impact through conversion to other energy forms. For all gloves at all drop heights, peak positive acceleration at the beginning of rebound was of lower absolute magnitude than peak negative acceleration at the end of compression. The influence of drop height on an index characterising this relationship differed between the conventional and pneumatic gloves, possibly reflecting structural changes to gloves as impact energy increased. The conventional and pneumatic gloves differed regarding temporal alignment between key kinematic and kinetic events, and there were some differences between the two pneumatic gloves in this respect. Nevertheless, peak glove deceleration correlated highly with peak impact force, not only for each glove individually but also when data for all gloves were combined. The findings confirmed the potential practical utility of the ARLI glove and identified air cushion thickness, glove compressibility and capacity for air release and subsequent reuptake as critical aspects of its design.

Pneumatic Boxing Glove Reduces Upward Drift in Peak Force and Loading Rate over a Long Series of Impacts

A conventional boxing glove and a prototype pneumatic glove were each fitted to a mechanical fist and dropped 253 times from a height of 3 metres on to a force plate covered by an ethylene vinyl acetate (EVA) mat. Impact dynamics were measured and modelled. From the outset, peak impact force and peak rate of force development (loading rate) were lower for the pneumatic glove. For both gloves, these variables displayed upward drift during the drop series, but the drift was smaller for the pneumatic glove. Consequently, the magnitude of the protective effect provided by the pneumatic glove increased with the number of impacts. For the conventional glove, change in peak force showed a close inverse relationship to force plate contact time (R2 > 0.96) and the time from first contact of the glove with the force plate to attainment of peak force (R2 = 0.85). These relationships were much weaker for the pneumatic glove (R2 = 0.09 and 0.59 respectively), suggesting the possibility of a more complex impact damping mechanism. Following the 253 drops of the pneumatic glove, the EVA mat covering the force plate was replaced, and another 10 drops then performed. Peak force readings were immediately reduced to an extent suggesting that 26% - 34% of the increase that had occurred over the 253 drops was attributable to impact-induced change in mat properties. This has implications for future experimental designs. Overall, the findings provided further evidence of the potential of pneumatic gloves to enable safer boxing.

Performance of Prototype Pneumatic Boxing Gloves under Two Different Conditions of Target Padding

The impact damping capabilities of four different boxing gloves were assessed under two different conditions of target padding to determine whether target characteristics might influence previous conclusions concerning potential for impact mitigation through novel glove design. A conventional 10 oz glove (Std 10 oz), a conventional 16 oz glove (Std 16 oz), a prototype pneumatic glove with a sealed bladder (SBLI) and a prototype pneumatic glove with a bladder allowing air exchange with the external environment (ARLI) were each dropped three times on to a force plate from six heights ranging from 2.5 to 5.0 metres. The force plate was covered by a 50 mm thick mat of EVA material and results obtained were compared with those of an earlier experiment involving use of a similar protocol but a 25 mm thick EVA force plate covering. The thicker mat greatly reduced peak impact forces for all gloves, with values for the Std 10 oz glove becoming much closer to those reported by other researchers for punches delivered by elite boxers to crash test manikins. Peak rates of force development were also substantially decreased. Protective effects provided by the ARLI glove relative to the Std 10 oz glove were diminished but still in the order of 17% - 22% for peak impact force and 27% - 49% for peak rate of force development across the range of drop heights. With the 50 mm mat thickness, the SBLI glove was as effective as the ARLI glove in reducing peak impact force, whereas this was not the case with the 25 mm mat. It was, however, always inferior to the ARLI glove in decreasing peak rate of force development. The ability of the ARLI glove to afford protection across a spectrum of impact conditions could yield important practical advantages.

A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation.

Commercially available devices for Brain-Computer Interface (BCI)-controlled robotic stroke rehabilitation are prohibitively expensive for many researchers who are interested in the topic and physicians who would utilize such a device. Additionally, they are cumbersome and require a technician to operate, increasing the inaccessibility of such devices for home-based robotic stroke rehabilitation therapy. Presented here is the design, implementation and test of an inexpensive, portable and adaptable BCI-controlled hand therapy device. The system utilizes a soft, flexible, pneumatic glove which can be used to deflect the subject's wrist and fingers. Operation is provided by a custom-designed pneumatic circuit. Air flow is controlled by an embedded system, which receives serial port instruction from a PC running real-time BCI software. System tests demonstrate that glove control can be successfully driven by a real-time BCI. A system such as the one described here may be used to explore closed loop neurofeedback rehabilitation in stroke relatively inexpensively and potentially in home environments.

Glove Tamponade for Atonic Postpartum Haemorrhage using Glove band

Introduction: Balloon tamponade is effective and recently included in armamentarium of management of PPH. Condom tamponade is also popular and widely used in low resource setting. We introduced in this study, tamponade with glove and glove band using the same principles. Methods: Tamponde with glove using both pneumatic and hydrostatic method was studied in 2 cases. Glove bands are used to tie the gloves to Ryles tube with stillette inside which was used for inflation and facilitate placement. The device appears similar to uterine thermal balloon. After inflation When tamponade test was positive, the vagina was packed and Ryles tube with stillete is folded and strapped at the introitus. Drainage channel was attached to glove end. Double glove was used in pneumatic tamponade. Stethoscope tubing was used as y-connector with dial pressure meter and BP apparatus inflator. Results: Hemostasis was achieved in all 2 cases. The device could be placed successfully and immediately after opening the prepared kit and there was no incidence of air or saline leak and no popping-out /prolapse or bursting of the glove. There were no complications or mortaliy. Conclusion: Hydrostatic tamponade needs longer time especially when double gloves are used. Single glove serves the purpose well. The action of pneumatic glove tamponade is effective and instantaneous. We recommend this when the blood loss is marked in atonic PPH and there is lack of time. The device is simple, cheap, made with easily available materials and can be safely applied. Glove band prevents leakage of saline as well as air effectively without occlusion of the ryles tube lumen.

A Pneumatic Glove and Immersive Virtual Reality Environment for Hand Rehabilitative Training After Stroke

While a number of devices have recently been developed to facilitate hand rehabilitation after stroke, most place some restrictions on movement of the digits or arm. Thus, a novel glove was developed which can provide independent extension assistance to each digit while still allowing full arm movement. This pneumatic glove, the PneuGlove, can be used for training grasp-and-release movements either with real objects or with virtual objects in a virtual reality environment. Two groups of stroke survivors, with seven subjects in each group, completed a six-week rehabilitation training protocol, consisting of three 1-h sessions held each week. One group wore the PneuGlove during training, performed both within a novel virtual reality environment and outside of it with physical objects, while the other group completed the same training without the device. Across subjects, significant improvements were observed in the Fugl-Meyer Assessment for the upper extremity (p < 0.001), the hand/wrist portion of the Fugl-Meyer Assessment (p < 0.001), the Box and Blocks test (p < 0.005), and palmar pinch strength (p < 0.005). While changes in the two groups were not statistically different, the group using the PneuGlove did show greater mean improvement on each of these measures, such as gains of 3.7 versus 2.4 points on the hand/wrist portion of the Fugl-Meyer Assessment and 14 N versus 5 N in palmar pinch.