Soft Rehabilitation Actuator With Integrated Post-stroke Finger Spasticity Evaluation

Task-Oriented Training by a Personalized Electromyography-Driven Soft Robotic Hand in Chronic Stroke: A Randomized Controlled Trial

BACKGROUND

Intensive task-oriented training has shown promise in enhancing distal motor function among patients with chronic stroke. A personalized electromyography (EMG)-driven soft robotic hand was developed to assist task-oriented object-manipulation training effectively. Objective. To compare the effectiveness of task-oriented training using the EMG-driven soft robotic hand.

METHODS

A single-blinded, randomized controlled trial was conducted with 34 chronic stroke survivors. The subjects were randomly assigned to the Hand Task (HT) group (n = 17) or the control (CON) group (n = 17). The HT group received 45 minutes of task-oriented training by manipulating small objects with the robotic hand for 20 sessions, while the CON group received 45 minutes of hand-functional exercises without objects using the same robot. Fugl-Meyer assessment (FMA-UE), Action Research Arm Test (ARAT), Modified Ashworth Score (MAS), Box and Block test (BBT), Maximum Grip Strength, and active range of motion (AROM) of fingers were assessed at baseline, after intervention, and 3 months follow-up. The muscle co-contraction index (CI) was analyzed to evaluate the session-by-session variation of upper limb EMG patterns.

RESULTS

The HT group showed more significant improvement in FMA-UE (wrist/hand, shoulder/elbow) compared to the CON group (P < .05). At 3-month follow-up, the HT group demonstrated significant improvements in FMA-UE, ARAT, BBT, MAS (finger), and AROMs (P < .05). The HT group exhibited a more significant decrease in muscle co-contractions compared to the CON group (P < .05).

CONCLUSIONS

EMG-driven task-oriented training with the personalized soft robotic hand was a practical approach to improving motor function and muscle coordination.

CLINICAL TRIAL REGISTRY NAME

Soft Robotic Hand System for Stroke Rehabilitation.

CLINICAL TRIAL REGISTRATION-URL

https://clinicaltrials.gov/.

UNIQUE IDENTIFIER

NCT03286309.

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators

Soft robots that incorporate elastomeric matrices and flexible materials have gained attention for their unique capabilities, surpassing those of rigid robots, with increased degrees of freedom and movement. Research has highlighted the adaptability, agility, and sensitivity of soft robotic actuators in various applications, including industrial grippers, locomotive robots, wearable assistive devices, and more. It has been demonstrated that bellow-shaped actuators exhibit greater efficiency compared to uniformly shaped fiber-reinforced actuators as they require less input pressure to achieve a comparable range of motion (ROM). Nevertheless, the mathematical quantification of the performance of bellow-based soft fluidic actuators is not well established due to their inherent non-uniform and complex structure, particularly when compared to fiber-reinforced actuators. Furthermore, the design of bellow dimensions is mostly based on intuition without standardized guidance and criteria. This article presents a comprehensive description of the quasi-static analytical modeling process used to analyze bellow-based soft actuators with linear extension. The results of the models are validated through finite element method (FEM) simulations and experimental testing, considering elongation in free space under fluidic pressurization. This study facilitates the determination of optimal geometrical parameters for bellow-based actuators, allowing for effective biomimetic robot design optimization and performance prediction.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Effects of chamber shapes on maneuverability and control property of endoscope-support soft actuators

Introduction: Minimally Invasive Surgery (MIS) offers targeted surgical access with reduced invasiveness; however, the maneuverability challenges of traditional instruments in this domain underscore the need for innovative solutions. Soft actuators activated by fluids or gases present a promising strategy for augmenting endoscopic capabilities, thereby enhancing the surgical precision in MIS. This study aimed to explore the intricate dynamics of the interactions between soft actuators and endoscopes, with an emphasis on the pivotal role of cross-sectional chamber shapes. While previous studies have touched on the influence of chamber shapes on bending properties, we provide a comprehensive exploration. We explore how these shapes modulate friction forces, which in turn influence the interactions governing bending, response, and stiffness adjustability, all of which are essential for enhancing endoscope maneuverability in MIS contexts. Methods: A novel bilateral symmetrical air chamber design was adopted to investigate various chamber shapes. We employed finite element analysis (FEA) simulations followed by prototype testing to evaluate the interactions driven by these chamber shapes and to discern their impact on actuator properties. Recognizing the pivotal role of friction in these interactions, we conducted dedicated friction experiments. These experiments further deepened our understanding of the relationship between chamber shape and friction, and how this synergy influences the properties of the actuator. Results: Our findings showed that actuators with wider chambers generate larger friction forces, thereby enhancing the interaction and improving the bending, response, and stiffness adjustability. Additionally, the soft actuator significantly improved the maneuverability and bending radius of the endoscope, demonstrating enhanced navigation capabilities in complex environments. Discussion: The shape of a cross-sectional chamber plays a pivotal role in designing soft actuators for MIS applications. Our research emphasizes the importance of this design component, offering key insights for the development of endoscope-supporting soft actuators that can effectively handle intricate actuator-endoscope interactions, thereby enhancing surgical outcomes.

Assistive robotic hand with bi-directional soft actuator for hand impaired patients

Soft wearable robotic hand can assist with hand function for the performance of activities of daily living (ADL). However, existing robotic hands lack a mathematical way to quantify the grip force generated for better controlling the grasp of objects during the performance of ADL. To address this issue, this article presents a soft wearable robotic hand with active control of finger flexion and extension through an elastomeric-based bi-directional soft actuator. This actuator bends and extends by pneumatic actuation at lower air pressure, and a flex sensor embedded inside the actuator measures the angles of the fingers in real-time. Analytical models are established to quantify the kinematic and tip force for gripping of the actuator in terms of the relationship between the input pressure and the bending angle, as well as the output force, and are validated experimentally and by the finite element method. Furthermore, the ability of the soft robotic hand to grasp objects is validated with and without being worn on a human hand. The robotic hand facilitates hand opening and closing by the wearer and successfully assists with grasping objects with sufficient force for ADL-related tasks, and the grip force provided by the actuator is further estimated by the analytical models on two healthy subjects. Results suggest the possibility of the soft robotic hand in providing controllable grip strength in rehabilitation and ADL assistance.

Towards an Extensive Thumb Assist: A Comparison between Whole-Finger and Modular Types of Soft Pneumatic Actuators

Soft pneumatic actuators used in robotic rehabilitation gloves are classified into two types: whole-finger actuators with air chambers that cover the entire finger and modular actuators with chambers only above the finger joints. Most existing prototypes provide enough finger flexion support, but insufficient independent thumb abduction or opposition support. Even the latest modular soft actuator realized thumb abduction with a sacrifice of range of motion (RoM). Moreover, the advantages and disadvantages of using the two types of soft actuators for thumb assistance have not been made clear. Without an efficient thumb assist, patients’ options for hand function rehabilitation are very limited. Therefore, the objective of this study was to design a modular actuator (M-ACT) that could support multiple degrees of freedom, compare it with a whole-finger type of thumb actuator with three inner chambers (3C-ACT) in terms of the RoM, force output of thumb flexion, and abduction, and use an enhanced Kapandji test to measure both the kinematic aspect of the thumb (Kapandji score) and thumb-tip pinch force. Our results indicated superior single-DoF support capability of the M-ACT and superior multi-DoF support capability of the 3C-ACT. The use of the 3C-ACT as the thumb actuator and the M-ACT as the four-finger actuator may be the optimal solution for the soft robotic glove. This study will aid in the progression of soft robotic gloves for hand rehabilitation towards real rehabilitation practice.

Modeling and Evaluation of a Novel Hybrid-Driven Compliant Hand Exoskeleton Based on Human-Machine Coupling Model

This paper presents the modeling design method for a novel hybrid-driven compliant hand exoskeleton based on the human-machine coupling model for the patients who have requirements on training and assisting. Firstly, the human-machine coupling model is established based on the kinematics characteristics of human fingers and the Bernoulli beam formula. On this basis, the variable stiffness flexible hinge (VSFH) is used to drive the finger extension and the cable-driven mechanism is used to implement the movement of the finger flexion. Here, a hand orthosis is designed in the proposed hand exoskeleton to act as the base and maintain the function position of the hand for patients with hand dysfunction. Then, a final design prototype is fabricated to evaluate the proposed modeling method. In the end, a series of experiments based on the prototype is proceeded to evaluate its capabilities on stretching force for extension, bio-imitability, finger flexion capability, and fingertip force. The results show that the prototype has a significant improvement in all aspects of the ability mentioned above, and has good bionics. The proposed design method can be utilized to implement the rapid design of the hybrid-driven compliant hand exoskeleton with the changed requirements. The novel modeling method can be easily applied in personalized design in rehabilitation engineering.

High Compliance Pneumatic Actuators to Promote Finger Extension in Stroke Survivors

Compliant pneumatic systems are well suited for wearable robotic applications. The actuators are lightweight, conformable to irregular shapes, and tolerant of uncontrolled degrees of freedom. These attributes are especially desirable for hand exoskeletons given their space and mass constraints. Creating active digit extension with these exoskeletons is especially critical for clinical populations such as stroke survivors who often have great difficulty opening their paretic hand. To achieve active digit extension with a soft actuator, we have created pneumatic chambers that lie along the palmar surface of the digits. These chambers can directly extend the digits when pressurized. We present a characterization of the extension force and passive flexion resistance generated by these pneumatic chambers across a range of joint angles as a function of cross-sectional shape, dimension, and wall thickness. The chambers were fabricated out of DragonSkin 20 using custom molds and were tested on a custom jig. Extension forces created at the end of the chamber (where fingertip contact would occur) exceeded 3.00 N at relatively low pressure (48.3 kPa). A rectangular cross-section generated higher extension force than a semi-obround cross-sectional shape. Extension force was significantly higher (p < 0.05) for actuators with the highest wall thickness compared to those with the thinnest walls. In comparison to previously used polyurethane actuators, the DragonSkin actuators had a much higher extension force for a similar passive bending resistance. Passive bending resistance of the chamber (simulating finger flexion) did not vary significantly with actuator shape, wall thickness, width, or depth. The flexion resistance, however, could be significantly reduced by applying a vacuum. These results provide guidance in designing pneumatic actuators for assisting finger extension and resisting unwanted flexion in the fingers.

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

Robotic devices for paediatric rehabilitation: a review of design features

Children with physical disabilities often have limited performance in daily activities, hindering their physical development, social development and mental health. Therefore, rehabilitation is essential to mitigate the adverse effects of the different causes of physical disabilities and improve independence and quality of life. In the last decade, robotic rehabilitation has shown the potential to augment traditional physical rehabilitation. However, to date, most robotic rehabilitation devices are designed for adult patients who differ in their needs compared to paediatric patients, limiting the devices' potential because the paediatric patients' needs are not adequately considered. With this in mind, the current work reviews the existing literature on robotic rehabilitation for children with physical disabilities, intending to summarise how the rehabilitation robots could fulfil children's needs and inspire researchers to develop new devices. A literature search was conducted utilising the Web of Science, PubMed and Scopus databases. Based on the inclusion-exclusion criteria, 206 publications were included, and 58 robotic devices used by children with a physical disability were identified. Different design factors and the treated conditions using robotic technology were compared. Through the analyses, it was identified that weight, safety, operability and motivation were crucial factors to the successful design of devices for children. The majority of the current devices were used for lower limb rehabilitation. Neurological disorders, in particular cerebral palsy, were the most common conditions for which devices were designed. By far, the most common actuator was the electric motor. Usually, the devices present more than one training strategy being the assistive strategy the most used. The admittance/impedance method is the most popular to interface the robot with the children. Currently, there is a trend on developing exoskeletons, as they can assist children with daily life activities outside of the rehabilitation setting, propitiating a wider adoption of the technology. With this shift in focus, it appears likely that new technologies to actuate the system (e.g. serial elastic actuators) and to detect the intention (e.g. physiological signals) of children as they go about their daily activities will be required.

Effects of a Soft Robotic Hand for Hand Rehabilitation in Chronic Stroke Survivors

OBJECTIVES

Soft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated the clinical efficacy of a soft robotic hand that could actively flex and extend the fingers in chronic stroke subjects with different levels of spasticity.

METHODS

Sixteen chronic stroke subjects were recruited into this single-group study. Subjects underwent 20 sessions of 1-hour EMG-driven soft robotic hand training. Training effect was evaluated by the pre-training and post-training assessments with the clinical scores: Action Research Arm Test(ARAT), Fugl-Meyer Assessment for Upper Extremity(FMA-UE), Box-and-Block test(BBT), Modified Ashworth Scale(MAS), and maximum voluntary grip strength.

RESULTS

For all the recruited subjects (n = 16), significant improvement of upper limb function was generally observed in ARAT (increased mean=2.44, P = 0.032), FMA-UE (increased mean=3.31, P = 0.003), BBT (increased mean=1.81, P = 0.024), and maximum voluntary grip strength (increased mean=2.14 kg, P < 0.001). No significant change was observed in terms of spasticity with the MAS (decreased mean=0.11, P = 0.423). Further analysis showed subjects with mild or no finger flexor spasticity (MAS<2, n = 9) at pre-training had significant improvement of upper limb function after 20 sessions of training. However, for subjects with moderate and severe finger flexor spasticity (MAS=2,3, n = 7) at pre-training, no significant change in clinical scores was shown and only maximum voluntary grip strength had significant increase.

CONCLUSION

EMG-driven rehabilitation training using the soft robotic hand with flexion and extension could be effective for the functional recovery of upper limb in chronic stroke subjects with mild or no spasticity.

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.

Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator

Stroke has been the leading cause of disability due to the induced spasticity in the upper extremity. The constant flexion of spastic fingers following stroke has not been well described. Accurate measurements for joint stiffness help clinicians have a better access to the level of impairment after stroke. Previously, we conducted a method for quantifying the passive finger joint stiffness based on the pressure-angle relationship between the spastic fingers and the soft-elastic composite actuator (SECA). However, it lacks a ground-truth to demonstrate the compatibility between the SECA-facilitated stiffness estimation and standard joint stiffness quantification procedure. In this study, we compare the passive metacarpophalangeal (MCP) joint stiffness measured using the SECA with the results from our designed standalone mechatronics device, which measures the passive metacarpophalangeal joint torque and angle during passive finger rotation. Results obtained from the fitting model that concludes the stiffness characteristic are further compared with the results obtained from SECA-Finger model, as well as the clinical score of Modified Ashworth Scale (MAS) for grading spasticity. These findings suggest the possibility of passive MCP joint stiffness quantification using the soft robotic actuator during the performance of different tasks in hand rehabilitation.

A domestic robotic rehabilitation device for assessment of wrist function for outpatients

Introduction

Available robot-assisted stroke rehabilitation systems are often limited in their utilization in the home environment, due to several barriers such as high cost, absence of therapists, tedious training tasks, or encumbering interfaces. This paper presents a low-cost robotic rehabilitation and assessment device for restoring wrist function, offering wrist exercises incorporating pronation-supination and flexion-extension movements. Furthermore, the device is designed for the assessment of joint stiffness of the wrist, and range of motion in two degrees of freedom. Methods: Mechanical/electrical design of the device as well as the control system is described. A preliminary evaluation focused on the measurement of the torsional stiffness of the limb is presented. It is evaluated by reconstructing the known stiffness values of torsional springs by measuring the motor current required to displace them.

Results

The device demonstrates the ability to determine the stiffness of an object with low-cost hardware. Use case scenarios of the device for training and assessment of the wrist are presented, allowing for a range of motion of ± 75 ° and ± 65 ° , for pronation-supination and flexion-extension respectively.

Conclusion

The device shows potential to help objectively quantify the stiffness of the wrist movement, which consecutively could be used to represent and quantify the degree of impairment of patients after stroke in a more objective manner. Further clinical study is necessary to examine this.

Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review

Spasticity is a motor disorder that causes stiffness or tightness of the muscles and can interfere with normal movement, speech, and gait. Traditionally, the spasticity assessment is carried out by clinicians using standardized procedures for objective evaluation. However, these procedures are manually performed and, thereby, they could be influenced by the clinician's subjectivity or expertise. The automation of such traditional methods for spasticity evaluation is an interesting and emerging field in neurorehabilitation. One of the most promising approaches is the use of robot-aided systems. In this paper, a systematic review of systems focused on the assessment of upper limb (UL) spasticity using robotic technology is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the morphology of devices, the data acquisition systems, the outcome generation method, and the focus of intervention (assessment and/or training). Finally, a series of guidelines and challenges that must be considered when designing and implementing fully-automated robot-aided systems for the assessment of UL spasticity are summarized.