Robotic Glove Research Articles

РАЗРАБОТКА МОДЕЛИ КИНЕМАТИКИ ПАЛЬЦЕВ ЧЕЛОВЕЧЕСКОЙ РУКИ, ПРИМЕНЯЕМЫХ В РОБОТИЗИРОВАННЫХ ПЕРЧАТКАХ

This paper considers the design of an anthropomorphic manipulator in the form of a human hand. The aim of the work is to describe the kinematics of a robotic hand, using the example of one finger. Based on the Denavit–Hartenberg algorithm, a kinematic model is developed for one finger of the robotic hand. The working area of the robotic finger is determined, taking into account the limitations on the rotation angles of the finger phalanges. These kinematic equations are used to solve the problem of the speeds of one fingertip. According to the results of modeling for given speeds of the fingertip, the dependencies of the rotation angles of the robotic hand links were obtained. Unlike the previously used models, the developed one makes it possible to take into account the presence of a geometric constraint between the proximal and distal finger phalanges, which is structurally made with an inextensible thread. The results of the work can be used in the design and manufacture of new robotic gloves.

Finger joint aligned flat tube folding structure for robotic glove design

Pneumatic soft actuators have been widely considered the safest actuation technology for use in wearable rehabilitation robots. For soft robotic gloves, researchers commonly put soft extending or bending actuators on dorsal fingers to assist hand flexion. In this research, we propose a novel pre-folded flat tube actuator (PFTA) to assist finger flex into a pre-set bending angle or contact force. The PFTA has three folds, aligned with each of the finger joint. Different from other soft actuators, the PFTA exerts bending torque directly on each finger joint without large actuator deformation. The PFTA made of heat shrink flat tube has a small profile, with low cost, easy fabrication, and high safety. When actuated, the PFTA has the tendency to unfold as well, we call this effect as unfolding flat tube actuation. This effect is characterized by a range of bending angles and input air pressures in which four distinct response regimes were observed. They are defined as shearing, collapsing, creasing, and flattening regimes. Similarly, experimental characterization of PFTAs is also conducted to evaluate the level of joint flexion assistance based on which design guidelines for robotic gloves are recommended. Finally, we build PFTAs on a soft wearable glove and demonstrate their capability in assisting the grasping operations of various object shapes.

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.

Task-oriented human-robot interaction control of a robotic glove utilizing forearm electromyography

Task-oriented myoelectric assistive control of a robotic glove based on forearm electromyography for practical grasping tasks is investigated in this work. Three grasping functions are considered: human intent detection of grasping tasks, grasping mode recognition, and grasping force control. Different combinations of forearm electromyographic signals are adopted for the three functions. Firstly, the overall electromyographic signal is used to trigger the whole grasping task. Secondly, a novel Long Short-Term Memory network is utilized to classify various grasping modes, including pinch grasping and palmar grasping, by analyzing eight-channel electromyographic signals. Thirdly, two-channel proportional control of grasping force using electromyographic flexor/extensor signals is adopted for the robotic glove such that the human hand can relax during the grasping task, while the robotic glove maintains the grasping force. To this end, finite state machine based hierarchical control architecture is proposed for the whole grasping task. Experiments are conducted to validate the proposed task-oriented assistive control method, and the results clearly demonstrates the potential of the proposed method in rehabilitation therapy.

Human-Centered Design: A Haptic Robotic-Based Leader-Follower Driving Training System to Improve Driving Skills and Enhance Safety

This study presents the design and implementation of a human-centered wearable robotic glove for driving training using a lead-follower control methodology and haptic stimuli. The glove captures and analyses biometric signals, including piezoelectric sensors on fingers, Electromyography (EMG) for muscle strength, and inertial measurement unit (IMU) for hand position, to monitor hand and forearm behaviour during operation. Real-time motor controllers generate guidance cues for leader-follower gloves based on feedback from sensors, which are wirelessly communicated through the ESP-Now WIFI protocol. An experimental test was conducted to evaluate the effectiveness of the haptic cues on hand pressure and motion. 2-way ANOVA statistical analysis is used to measure the significance of the relevance between haptic cues and driving training outcomes. While haptic cues did not affect hand trajectory, they have statistical significance to driving outcomes. The proposed system has the potential to enhance driving safety training and improve performance.

An advanced cost-efficient IoT method for stroke rehabilitation using smart gloves

Stroke represents a severe, widespread, and widely acknowledged health crisis on both national and international levels. It is one of the most prevalent life-threatening conditions. Despite impressive advances in treating stroke, in addition to a need for effective patient care services, many sufferers still rely solely on physical interventions. The present paper describes and explains the use of a newly designed gadget for stroke survivors who cannot move their fingers. This is a sophisticated mobile device that enables stroke patients to regain their muscle memory and thus their ability to perform repetitive actions by continuing to tighten and stretch their muscles without the intervention of a physiotherapist. Gamification methodology is used to encourage patients to become involved in the process of rehabilitation. The device also has sensors that take information and transmit it to an app through an ESP32 connection. This enables physicians to view glove usage information remotely and keep track of an individual patient’s health. Communication between app and glove is facilitated by a broker in the Amazon Web Service IoT. With the robotic glove presented here, the recovery rate is found to be 90.23% over four weeks’ duration, which represents a significant improvement compared with existing hospital-based rehabilitation techniques.

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.

The Application of Soft Robotic Gloves in Stroke Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Wearable robotic devices have been strongly put into use in both the clinical and research fields of stroke rehabilitation over the past decades. This study aimed to explore the effectiveness of soft robotic gloves (SRGs) towards improving the motor recovery and functional abilities in patients with post-stroke hemiparesis. Five major bibliographic databases, PubMed, Embase, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, were all reviewed for enrollment regarding comparative trials prior to 7 March 2023. We included adults with stroke and compared their rehabilitation using SRGs to conventional rehabilitation (CR) on hand function in terms of the Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE), Fugl-Meyer Distal Upper Extremity Motor Assessment (FMA-distal UE), box and blocks test score, grip strength test, and the Jebsen-Taylor hand function test (JTT). A total of 8 studies, comprising 309 participants, were included in the analysis. Compared to CR, rehabilitation involving SRGs achieved better FMA-UE (MD 6.52, 95% CI: 3.65~9.39), FMA-distal UE (MD 3.27, 95% CI: 1.50~5.04), and JJT (MD 13.34, CI: 5.16~21.53) results. Subgroup analysis showed that stroke latency of more than 6 months and training for more than 30 min offered a better effect as well. In conclusion, for patients with stroke, rehabilitation using SRGs is recommended to promote the functional abilities of the upper extremities.

Design and fabrication of a moving robotic glove system

<p><span lang="EN-US">This paper presents the research, design, and manufacture of a robotic hand to control movement with a glove. The moving glove-controlled robotic hand is based on two main parts: the hand mechanism and the control circuit. The control glove unit includes an Arduino nRF24l01 microcontroller module and five flex sensors for five fingers. These sensors are used to collect data about the curvature of each finger. Then those data will be received by the Arduino microcontroller and sent by the nRF24l01 module. The hand's microcontroller will process that information and control five servo motors so that the five fingers of the robotic hand are moved. The result of this research is to produce a robotic hand that accurately simulates the curvature of a user's finger and mimics the motion of a glove well. Moreover, the robot hand can grip objects of different sizes (from 0.1 to 1 kg) and shapes, from which this robot helps users easily manipulate objects.</span></p>

Biodegradable Shape‐Memory Ionogels as Green and Adaptive Wearable Electronics Toward Physical Rehabilitation

AbstractRehabilitation is necessary for the recovery of patients with paralysis caused by stroke and muscle atrophy. Wearable electronics can provide feedback on physical training and facilitate healthcare. However, most existing wearable electronics are difficult to maintain a conformal skin‐device interface. Additionally, the use of non‐degradable electronic materials is associated with environmental risks. Herein, ionogels with biodegradation and shape‐memory properties as eco‐friendly and geometry‐adaptive wearable electronics for rehabilitation are proposed. The biodegradation is enabled by incorporating polycaprolactone segments into the ionogel matrix. Moreover, the ionogel‐based wearable electronics can be conformal to certain joints by shape programming, and provide stable and reproducible real‐time signals reflecting joint movements during long‐term rehabilitation training assisted by a robotic glove, facilitating carers to assess rehabilitation efficacy and choose an appropriate scheme. This study demonstrates the potential of biodegradable shape‐memory ionogels as green and adaptive wearable electronics for robot‐assisted rehabilitation.

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.

User-centered development process of an operating interface to couple a robotic glove with a virtual environment to optimize hand rehabilitation following a stroke.

Task-specific neurorehabilitation is crucial to optimize hand recovery shortly after a stroke, but intensive neurorehabilitation remains limited in resource-constrained healthcare systems. This has led to a growing interest in the use of robotic gloves as an adjunct intervention to intensify hand-specific neurorehabilitation. This study aims to develop and assess the usability of an operating interface supporting such a technology coupled with a virtual environment through a user-centered design approach. Fourteen participants with hand hemiparesis following a stroke were invited to don the robotic glove before browsing through the operating interface and its functionalities, and perform two mobility exercises in a virtual environment. Feedback was collected for improving technology usability. Participants completed the System Usability Scale and ABILHAND questionnaires and their recommendations were gathered and prioritized in a Pugh Matrix. The System Usability Scale (SUS) score for the operating interface was excellent (M = 87.0 SD = 11.6). A total of 74 recommendations to improve the user interface, calibration process, and exercise usability were identified. The application of a full cycle of user-centred design approach confirms the high level of usability of the system which is perceived by end users as acceptable and useful for intensifying neurorehabilitation.

Design, Control, and Experimental Evaluation of A Novel Robotic Glove System for Patients with Brachial Plexus Injuries.

This paper presents the development of an exoskeleton glove system for people who suffer from brachial plexus injuries, aiming to assist their lost grasping functionality. The robotic system consists of a portable glove system and an embedded controller. The glove system consists of Linear Series Elastic Actuators (LSEA), Rotary Series Elastic Actuators (RSEA), and optimized finger linkages to provide imitated human motion to each finger and a coupled motion of the hand. The design principles and optimization strategies were investigated to balance functionality, portability, and stability. The model-based force control strategy compensated with a backlash model and model-free force control strategy are presented and compared. Results show that our proposed model-free control method achieves the goal of accurate force control. Finally, experiments were conducted with the prototype of the developed integrated exoskeleton glove system. Results from 3 subjects with 150 trials show that our proposed exoskeleton glove system has the potential to be used as a rehabilitation device for patients.

Dynamic assessment of the upper extremity: a review of available and emerging technologies.

The purpose of this review article is to provide an update on the realm of emerging technology available for the assessment of dynamic functional movement of the hand and upper limb. A critical overview of the literature and a conceptual framework for use of such technologies is proposed. The framework explores three broad purpose categories including customization of care, functional surveillance and interventions through biofeedback strategies. State-of-the-art technologies are described, from basic activity monitors to feedback-enabled robotic gloves, along with exemplar trials and clinical applications. The future of technologies innovation in hand pathology is proposed in the context of the current obstacles and opportunities for hand surgeons and therapists.

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.