The impact of a soft robotic glove on activity performance and body function for brachial plexus birth injury patients-A longitudinal case series.

BACKGROUND: A brachial plexus birth injury (BPBI) can cause reduced ability to use the arm and hand in daily activities due to reduced grip strength and endurance. A soft robotic glove can increase the number of activities performed and improve activity performance for patients with neurological disease. The use of a soft robotic glove for patients with BPBI has not been studied. PURPOSE: To investigate if a soft robotic glove can improve activity performance and body function for patients with BPBI. STUDY DESIGN: Longitudinal Case Series. METHODS: A convenience sample of patients with BPBI, treated by the Brachial plexus injury service in Umeå, Sweden were studied. Eight patients used a soft robotic glove, (Carbonhand®), at home for three months. Data on activity performance and satisfaction with activity performance, active range of motion and strength were collected at baseline, and at three and four months. A patient evaluation form was filled out at three months, all patients kept a diary for three out of 12 weeks. RESULTS: Six out of eight patients wanted to continue using the device and improved their self-perception of activity performance and satisfaction with the performance due to a more secure grip, compared to when not using the device. All patients had improved maximum strength and endurance in elbow flexion at three months. The device was useful as an assisting device and as a training tool. CONCLUSION: A soft robotic glove (Carbonhand) may improve activity performance and perceived satisfaction and increase the number of activities that a person with BPBI can perform in everyday life. It is possible to increase strength in elbow flexion after using such a device. Due to this limited material, more research is needed.

From Skin Movement to Wearable Robotics: The Case of Robotic Gloves.

Previous research on wearable robotics focused on developing actuation mechanisms while overlooking influences of skin movement. During finger flexion, skins on the opisthenar and finger back are stretched. Impeding such skin movement will obstruct normal finger motions. In this research, a statistical study on skin movement is proposed and conducted to quantify skin movement on human hands. Results of 30 subjects (15 men and 15 women) reveal that skin at the finger back extends by an average of 29.3 ± 7.2% in fist clenching. Based on this study, design guidelines for robotic gloves are proposed, and nominal strain values at different hand regions are tabulated for references in robotic glove design. To explore the influence of skin movement on wearable robotics, an elastomer-constrained flat tube actuator is proposed based on which two prototype robotic gloves are developed: one with an ergonomic strap interface that has small constraint to skin motion, and the other based on the commonly used fabric glove that is supposed to have large constraint to skin motion. With the same power input to the robotic gloves, the strap-based design achieves a finger motion range of 2.5 times and a gripping force of 4.3 times that of the conventional fabric glove.

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.

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.

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.

Occupational therapists' evaluation of the perceived usability and utility of wearable soft robotic exoskeleton gloves for hand function rehabilitation following a stroke.

PURPOSE: To evaluate the perceived usability and utility of using a soft robotic glove to rehabilitate hand function following a stroke. METHODS: A convergent parallel mixed-methods design was used to consult a convenience sample of 14 experienced occupational therapists (OTs) practicing within a specialised stroke rehabilitation program. All OTs participated in one 60-to-90-minute individual consultation during which the attributes of a recently-developed soft robotic glove (ExoGlove) were presented before they could test it on themselves. After this consultation, OTs completed the System Usability Scale (SUS) questionnaire and answered open-ended questions focussing on the usability and utility of soft robotic gloves framed according to the Unified Theory of Acceptance and Use of Technology (UTAUT). RESULTS: The OTs perceived the glove's usability as being moderate-to-good on the SUS (median score= 63.75 on a scale of 100). Thematic analysis revealed the importance of specifically considering elements such as ease of use (e.g. simplicity and speed), cost, movement precision, durability, and safety, when developing soft robotic gloves such as the ExoGlove. CONCLUSIONS: Engagement in a continuous improvement process is essential to maximise the perceived usability and utility of soft robotic gloves, particularly of the ExoGlove, through their final development phase before pilot testing their effects and effectiveness for post-stroke hand rehabilitation.Implications for rehabilitationAll occupational therapists anticipate that a soft robotic glove such as the ExoGlove will allow them to increase treatment intensity and best aligns with principles of neuroplasticity.The clinical judgement and guidance of OTs, developed through practice, experience, and knowledge, remain essential to safely and efficiently exercise with a soft robotic glove.Achieving a balance between effort and performance expectancies is essential in developing and improving the functionality of soft robotic gloves, as with each additional functionality comes new challenges that impact its successful transition to a clinical setting.

Therapeutic Effect of a Soft Robotic Glove for Activities of Daily Living In People With Impaired Hand Strength: Protocol for a Multicenter Clinical Trial (iHand).

BACKGROUND: Decline of hand function, especially reduced hand strength, is a common problem that can be caused by many disorders and results in difficulties performing activities of daily living. A wearable soft robotic glove may be a solution, enabling use of the affected arm and hand repeatedly during functional daily activities and providing intensive and task-specific training simultaneously with assistance of hand function. OBJECTIVE: We aim to investigate the therapeutic effect of an assistive soft robotic glove (Carbonhand). METHODS: This multicenter uncontrolled intervention study consists of 3 preassessments (T0, T1, and T2), a postassessment (T3), and a follow-up assessment (T4). Participants are patients who experience hand function limitations. For the intervention, participants will use the glove during activities of daily living at home for 6 weeks, with a recommended use of at least 180 minutes per week. The primary outcome measure is handgrip strength, and secondary outcome measures are related to functional arm and hand abilities, amount of glove use, and quality of life. RESULTS: The first participant was included on June 25, 2019. Currently, the study has been extended due to the COVID-19 pandemic; data collection and analysis are expected to be completed in 2022. CONCLUSIONS: The Carbonhand system is a wearable assistive device, allowing performance of functional activities to be enhanced directly during functional daily activities. At the same time, active movement of the user is encouraged as much as possible, which has potential to provide highly intensive and task-specific training. As such, it is one of the first assistive devices to incorporate assist-as-needed principles. This is the first powered clinical trial that investigates the unique application of an assistive grip-supporting soft robotic glove outside of clinical settings with the aim to have a therapeutic effect. TRIAL REGISTRATION: Netherlands Trial Register NTR NL7561; https://www.trialregister.nl/trial/7561. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34200.

Real time control and fabrication of a soft robotic glove by two parallel sensors with MBD approach.

Soft robotic gloves were designed to aid the rehabilitation process with hand pathologies and coordination of gripping exercises. The main issue in soft robotic actuators is to design a control strategy to overcome deformation in grasping exercises. In this paper, a new soft robotic actuator is developed to be protected against swell and deformation. This soft robotic glove is equipped with two sensors; these sensors make the robotic glove more intelligent. In the hardware, it was used two sensors in the new closed-loop method which include an air pressure sensor in the figure tip and a flex sensor to measure finger flexion rate. Two closed-loop control system is developed to regulate inlet air pressure and regulate the angle of the fingers for the soft robotic actuator. A Model-Based Design (MBD) method is presented as a very cost-effective, favorable, and robust method. PID programming on an embedded controller is applied by MBD approach. The soft actuator process contains a molded wooden chamber and fiber reinforcement. Experimental results show that the proposed soft robotic has a soft gripping mechanism, accurate gripping against various objects during daily activities.

Increasing motor cortex activation during grasping via novel robotic mirror hand therapy: a pilot fNIRS study.

BACKGROUND: Mirror therapy (MT) has been used for functional recovery of the affected hand by providing the mirrored image of the unaffected hand movement, which induces neural activation of the cortical hemisphere contralateral to the affected hand. Recently, many wearable robots assisting the movement of the hand have been developed, and several studies have proposed robotic mirror therapy (RMT) that uses a robot to provide mirrored movements of the unaffected hand to the affected hand with the robot controlled by measuring electromyography or posture of the unaffected hand. In some cases of RMT a mirror is placed to allow the person to observe only the unaffected hand but in others users simply observe the robotically assisted hand performing the mirrored movements, as was the case in this study. There have been limited evaluations of the cortical activity during RMT compared to MT and robotic therapy (RT) providing passive movements despite the difference in the modality of sensory feedback and the involvement of motor intention, respectively. METHODS: This paper analyzes bilateral motor cortex activation in nine healthy subjects and five chronic stroke survivors during a pinching task performed in MT, RT, and RMT conditions using functional near infrared spectroscopy (fNIRS). In the MT condition, the person moved the unaffected hand and observed it in a mirror while the affected hand remained still. In RT condition passive movements were provided to the affected hand with a cable-driven soft robotic glove, while, in RMT condition, the posture of the unaffected hand was measured by a sensing glove and the soft robotic glove mirrored its movement on the affected hand. RESULTS: For both groups, the RMT condition showed the greatest mean cortical activation on the motor cortex contralateral to the affected (non-dominant for the healthy group) hand compared to other conditions. Individual results indicate that RMT induces similar or greater neural activation on the motor cortex compared to MT and RT conditions. The interhemispheric activations of both groups were balanced in RMT condition. In MT condition, significantly greater activation was shown on the hemisphere ipsilateral to the affected (dominant for the healthy group) hand for both subject groups, while the contralateral side showed significantly greater activation for the healthy group in RT condition. CONCLUSION: The experimental results indicate that combining visual feedback, somatosensory feedback, and motor intention are important for greater stimulation on the contralateral motor cortex of the affected hand. RMT that includes these factors is hypothesized to achieve a more effective functional rehabilitation due to greater and more balanced cortical activation.

Bioinspired High-Degrees of Freedom Soft Robotic Glove for Restoring Versatile and Comfortable Manipulation.

The human hand is one of the most complex and compact grippers that has arisen as a product of natural genetic engineering; it is highly versatile, as it handles power and precision tasks. Since proper contact points and force directions are required to ensure versatility and secure a stable grip on an object, there must be a large workspace and controllable tip force directions for the digits. Although they are important, many individuals with neuromuscular diseases experience loss of these features. Thus, we propose a high-degree-of-freedom (DOF) soft robotic glove inspired by the anatomical features of human hands. The mechanism for adjusting the position and force direction of each tip is based on the structure of the extrinsic and intrinsic muscle-tendon units. The large thumb workspace was achieved by assisting opposition/reposition and flexion/extension to enable various grasping postures. A bidirectional actuation control mechanism with a cable-actuated agonist and an elastomer antagonist increased the assisted DOF and maintained compactness. The kinematic and kinetic performances of our device were evaluated by performing tests with eight stroke survivors. The thumb workspace increased by 43%, 207%, and 248% in the distal-proximal, dorsal-palmar, and radial-ulnar directions, respectively. The pinching shear force decreased by 54% and 45% for the nonthumb digits and thumb, respectively. These device-assisted improvements allowed objects to be stably grasped and manipulated in various postures. The novel device can assist individuals with impaired hand function to improve their grasping performance. Clinical Research Information Service (CRIS) Registration Number: KCT0004855.

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

Purpose: 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. Methods: 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). Results: 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]. Conclusion: 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.

Review of the effects of soft robotic gloves for activity-based rehabilitation in individuals with reduced hand function and manual dexterity following a neurological event.

Despite limited scientific evidence, there is an increasing interest in soft robotic gloves to optimize hand- and finger-related functional abilities following a neurological event. This review maps evidence on the effects and effectiveness of soft robotic gloves for hand rehabilitation and, whenever possible, patients' satisfaction. A systematized search of the literature was conducted using keywords structured around three areas: technology attributes, anatomy, and rehabilitation. A total of 272 titles, abstracts, and keywords were initially retrieved, and data were extracted out of 13 articles. Six articles investigated the effects of wearing a soft robotic glove and eight studied the effect or effectiveness of an intervention with it. Some statistically significant and meaningful beneficial effects were confirmed with the 29 outcome measures used. Finally, 11 articles also confirmed users' satisfaction with regard to the soft robotic glove, while some articles also noticed an increased engagement in the rehabilitation program with this technology. Despite the heterogeneity across studies, soft robotic gloves stand out as a safe and promising technology to improve hand- and finger-related dexterity and functional performance. However, strengthened evidence of the effects or effectiveness of such devices is needed before their transition from laboratory to clinical practice.

Robotic Glove with Soft-Elastic Composite Actuators for Assisting Activities of Daily Living.

Soft robotic hand/gloves for hand rehabilitation can aid the performance of activities of daily living (ADL). Although existing soft robotic hands can assist with finger flexion, few have addressed finger extension, which is a challenging task for stroke patients due to poststroke spasticity. In this article, we describe the design of a composite actuator, the soft-elastic composite actuator (SECA), to facilitate both finger flexion and extension. A double-segmented SECA comprising two serially connected fiber-reinforced actuators with two bottom torque-compensating layers was fabricated. The SECA bends and extends by pneumatic actuation, and the torque-compensating layers offer an assistive bending moment to configure the bending moment inside the SECA. The principles associated with selection of the torque-compensating layer are described. Analytical models were established to quantify the input pressure and the bending angle of SECA with free bending and when placed on a model compromised hand. The analytical models were validated experimentally and by the finite element method. Moreover, a stroke survivor was recruited to test the new robotic glove integrated with the multiple double-segmented SECA. The robotic glove facilitated hand opening and closing by the patient, and successfully assisted with grasp of a Chinese chess piece and twisting of a towel.

Quantifying upper extremity performance with and without assistance of a soft-robotic glove in elderly patients: A kinematic analysis.

OBJECTIVE: To explore the direct influence of a soft-robotic glove on movement duration and movement execution in elderly people with decreased hand function during a reach-and-grasp task. DESIGN: Cross-sectional study. PATIENTS: Eight subjects, aged 55+ years, with decreased hand function. METHODS: The direct effect of the glove was explored using kinematic analysis during a reach-and-grasp task with a light (100 g) and heavy (1,000/2,500 g) cylindrical object, performed with and without the soft-robotic glove. RESULTS: There was no difference in total movement time between performance with and without the glove. With the glove, the relative time needed to transport the heavy object was shorter, while the relative time needed to grasp the heavy object was longer. In addition, transporting light objects involved a lower peak velocity and larger elbow extension, and grasping the object involved a larger hand opening compared with without glove. CONCLUSION: As expected, no positive influence of the soft-robotic glove was found on total movement duration in elderly subjects. The influence of the glove on movement execution varied with movement phase. The positive and negative effects found may be due to a perceived confidence while carrying heavy objects with the glove, or compensation for loss of sensation, respectively. This information can be used to improve the glove design.

Exploiting Textile Mechanical Anisotropy for Fabric-Based Pneumatic Actuators.

Knit, woven, and nonwoven fabrics offer a diverse range of stretch and strain limiting mechanical properties that can be leveraged to produce tailored, whole-body deformation mechanics of soft robotic systems. This work presents new insights and methods for combining heterogeneous fabric material layers to create soft fabric-based actuators. This work demonstrates that a range of multi-degree-of-freedom motions can be generated by varying fabrics and their layered arrangements when a thin airtight bladder is inserted between them and inflated. Specifically, we present bending and straightening fabric-based actuators that are simple to manufacture, lightweight, require low operating pressures, display a high torque-to-weight ratio, and occupy a low volume in their unpressurized state. Their utility is demonstrated through their integration into a glove that actively assists hand opening and closing.

Assisting hand function after spinal cord injury with a fabric-based soft robotic glove.

BACKGROUND: Spinal cord injury is a devastating condition that can dramatically impact hand motor function. Passive and active assistive devices are becoming more commonly used to enhance lost hand strength and dexterity. Soft robotics is an emerging discipline that combines the classical principles of robotics with soft materials and could provide a new class of active assistive devices. Soft robotic assistive devices enable a human-robot interaction facilitated by compliant and light-weight structures. The scope of this work was to demonstrate that a fabric-based soft robotic glove can effectively assist participants affected by spinal cord injury in manipulating objects encountered in daily living. METHODS: The Toronto Rehabilitation Institute Hand Function Test was administered to 9 participants with C4-C7 spinal cord injuries to assess the functionality of the soft robotic glove. The test included object manipulation tasks commonly encountered during activities of daily living (ADL) and lift force measurements. The test was administered to each participant twice; once without the assistive glove to provide baseline data and once while wearing the assistive glove. The object manipulation subtests were evaluated using a linear mixed model, including interaction effects of variables such as time since injury. The lift force measures were separately evaluated using the Wilcoxon signed-rank test. RESULTS: The soft robotic glove improved object manipulation in ADL tasks. The difference in mean scores between baseline and assisted conditions was significant across all participants and for all manipulated objects. An improvement of 33.42 ± 15.43% relative to the maximal test score indicates that the glove sufficiently enhances hand function during ADL tasks. Moreover, lift force also increased when using the assistive soft robotic glove, further demonstrating the effectiveness of the device in assisting hand function. CONCLUSIONS: The results gathered in this study validate our fabric-based soft robotic glove as an effective device to assist hand function in individuals who have suffered upper limb paralysis following a spinal cord injury.

Detection of the intention to grasp during reach movements.

INTRODUCTION: Soft-robotic gloves have been developed to enhance grip to support stroke patients during daily life tasks. Studies showed that users perform tasks faster without the glove as compared to with the glove. It was investigated whether it is possible to detect grasp intention earlier than using force sensors to enhance the performance of the glove. METHODS: This was studied by distinguishing reach-to-grasp movements from reach movements without the intention to grasp, using minimal inertial sensing and machine learning. Both single-user and multi-user support vector machine classifiers were investigated. Data were gathered during an experiment with healthy subjects, in which they were asked to perform grasp and reach movements. RESULTS: Experimental results show a mean accuracy of 98.2% for single-user and of 91.4% for multi-user classification, both using only two sensors: one on the hand and one on the middle finger. Furthermore, it was found that using only 40% of the trial length, an accuracy of 85.3% was achieved, which would allow for an earlier prediction of grasp during the reach movement by 1200 ms. CONCLUSIONS: Based on these promising results, further research will be done to investigate the possibility to use classification of the movements in stroke patients.