Hand Rehabilitation Robotics on Poststroke Motor Recovery

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.

Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets

Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of handcrafted features was mined from windows of electromyogram (EMG) of the upper-limb muscles while reaching nine nearby targets like activities of daily living. The feature selection-based scoring method, neighborhood component analysis (NCA), selected the relevant feature subset. Finally, the target was recognized by the support vector machine (SVM) model. The classification performance was generalized by a nested cross-validation structure that selected the optimal feature subset in the inner loop. According to the low spatial resolution of the target location on display and following the slight discrimination of signals between targets, the best classification accuracy of 77.11 % was achieved for concatenating the features of two segments with a length of 2 and 0.25 s. Due to the lack of subtle variation in EMG, while reaching different targets, a wide range of features was applied to consider additional aspects of the knowledge contained in EMG signals. Furthermore, since NCA selected features that provided more discriminant power, it became achievable to employ various combinations of features and even concatenated features extracted from different movement parts to improve classification performance.

Effects of kinesio taping combined with upper extremity function training home program on upper limb function and self-efficacy in stroke patients: An experimental study

BACKGROUND

The purpose of this study is to investigate the effects of kinesio taping and an upper extremity function home program on the upper extremity function and self-efficacy of stroke patients, and to present therapeutic evidence for home program intervention to improve upper extremity function.

METHODS

First, 53 stroke patients were randomly assigned to 2 groups: 26 experimental subjects and 27 controls. The experimental group performed kinesio taping on the dorsal part of the hand along with upper extremity functional training home program and the control group performed only upper extremity functional training home program. The intervention was conducted for a total of 30 sessions over 6 weeks. To evaluate changes in upper extremity function, wrist extensor muscle activation via the Surface Electromyography, the Chedoke Arm and Hand Activity Inventory-9 (CAHAI-9), and the motor activity log (including amount of use and quality of movement) were evaluated. In addition, the Self-Efficacy Scale (SES) was evaluated to examine the change in the self- efficacy of the study subjects.

RESULTS

The experimental group participating in the kinesio taping and upper limb function home program showed a statistically significant improvement (P < .01) before and after the intervention in the Surface Electrography the Chedoke Arm and Hand Activity Inventory-9 evaluation item in the upper limb function change. The SES evaluation, a self-esteem evaluation, also showed a statistically significant improvement (P < .01) before and after the intervention. Chedoke Arm and Hand Activity Inventory-9, motor activity log (quality of movement), and SES evaluation showed statistically significant differences (P < .05) between the experimental and control groups.

CONCLUSION

It was confirmed that the upper extremity function training home program performed in parallel with the kinesio taping technique had a positive effect on the recovery of upper extremity function and self-esteem in stroke patients. The kinesio taping technique provides stability to the wrist while performing a home program that patients can perform on their own at home and appears to improve upper extremity function more effectively than when performing the upper extremity function home program alone.

An Overall Automated Architecture Based on the Tapping Test Measurement Protocol: Hand Dexterity Assessment through an Innovative Objective Method

The present work focuses on the tapping test, which is a method that is commonly used in the literature to assess dexterity, speed, and motor coordination by repeatedly moving fingers, performing a tapping action on a flat surface. During the test, the activation of specific brain regions enhances fine motor abilities, improving motor control. The research also explores neuromuscular and biomechanical factors related to finger dexterity, revealing neuroplastic adaptation to repetitive movements. To give an objective evaluation of all cited physiological aspects, this work proposes a measurement architecture consisting of the following: (i) a novel measurement protocol to assess the coordinative and conditional capabilities of a population of participants; (ii) a suitable measurement platform, consisting of synchronized and non-invasive inertial sensors to be worn at finger level; (iii) a data analysis processing stage, able to provide the final user (medical doctor or training coach) with a plethora of useful information about the carried-out tests, going far beyond state-of-the-art results from classical tapping test examinations. Particularly, the proposed study underscores the importance interdigital autonomy for complex finger motions, despite the challenges posed by anatomical connections; this deepens our understanding of upper limb coordination and the impact of neuroplasticity, holding significance for motor abilities assessment, improvement, and therapeutic strategies to enhance finger precision. The proof-of-concept test is performed by considering a population of college students. The obtained results allow us to consider the proposed architecture to be valuable for many application scenarios, such as the ones related to neurodegenerative disease evolution monitoring.

Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke

BACKGROUND

Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics.

METHODS

This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ).

RESULTS

At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (rSpearman=0.64) and to a lesser extent to changes in movement duration (rSpearman=0.51) while TDSM were very responsive to changes in movement duration (rSpearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (rSpearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations).

CONCLUSIONS

Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke.

TRIAL REGISTRATION

NCT01383512, https://clinicaltrials.gov/ , June 27, 2011.

Dexterity in the Acute Phase of Stroke: Impairments and Neural Substrates

BACKGROUND

Stroke can impair manual dexterity, leading to loss of independence following incomplete recovery. Enhancing our understanding of dexterity impairment may improve neurorehabilitation.

OBJECTIVES

The study aimed to measure dexterity components in acute stroke patients with and without hand motor deficits, compare them to those of healthy controls (HC), and to explore the neural substrates involved in specific components of dexterity.

METHODS

We used the Dextrain Manipulandum to quantify fine finger force control, finger selection accuracy, coactivation, and reaction time (RT). Dexterity was evaluated twice (2 days apart) in 74 patients and 14 HC. Voxel-Lesion-Symptom-Mapping (VLSM) was used to analyze the relationship between tissue damage and dexterity. Results. Due to severe paresis or fatigue, 24 patients could not perform these tasks. In 50 patients (included 4.6 ± 3.3 days post-stroke), finger force control improved (P < .001), as it did in HC (P = .03) who performed better than patients on both evaluations. Accuracy of finger selection did not improve significantly in any group, but the HC performed better on both evaluations. Unexpectedly, coactivation was better in patients than in HC at D3 (P = .03). There were no between-group differences in RT. VLSM showed that damage to the superior temporal gyrus (STG) impaired finger force control while damage to the posterior limb of the internal capsule (PLIC) impaired finger selectivity.

CONCLUSIONS

Acute stroke affecting the STG or PLIC impaired selective components of dexterity. Patients with mild to moderate impairment showed better finger force control and accuracy selection within 48 hours, suggesting the feasibility of detecting early dexterity improvements.

Three-Dimensional Magnetic Rehabilitation, Robot-Enhanced Hand-Motor Recovery after Subacute Stroke: A Randomized Controlled Trial

We developed an end-effector-type rehabilitation robot that can uses electro- and permanent magnets to generate a three-way magnetic field to assist hand movements and perform rehabilitation therapy. This study aimed to investigate the therapeutic effect of a rehabilitation program using a three-dimensional (3D) magnetic force-based hand rehabilitation robot on the motor function recovery of the paralyzed hands of patients with stroke. This was a double-blind randomized controlled trial in which 36 patients with subacute stroke were assigned to intervention and control groups of 18 patients each. The intervention group received 30 min of rehabilitation therapy per day for a month using a 3D magnetic force-driven hand rehabilitation robot, whereas the control group received 30 min of conventional occupational therapy to restore upper-limb function. The patients underwent three behavioral assessments at three time points: before starting treatment (T0), after 1 month of treatment (T1), and at the follow-up 1-month after treatment completion (T2). The primary outcome measure was the Wolf Motor Function Test (WMFT), and secondary outcome measures included the Fugl-Meyer Assessment of the Upper Limb (FMA_U), Modified Barthel Index (MBI), and European Quality of Life Five Dimensions (EQ-5D) questionnaire. No participant safety issues were reported during the intervention. Analysis using repeated measures analysis of variance showed significant interaction effects between time and group for both the WMFT score (p = 0.012) and time (p = 0.010). In post hoc analysis, the WMFT scores and time improved significantly more in the patients who received robotic rehabilitation at T1 than in the controls (p = 0.018 and p = 0.012). At T2, we also consistently found improvements in both the WMFT scores and times for the intervention group that were superior to those in the control group (p = 0.024 and p = 0.018, respectively). Similar results were observed for FMA_U, MBI, and EQ-5D. Rehabilitation using the 3D hand-rehabilitation robot effectively restored hand function in the patients with subacute stroke, contributing to improvement in daily independence and quality of life.

Development and Testing of a Soft Exoskeleton Robotic Hand Training Device

Hand-function recovery is often a goal for stroke survivors undergoing therapy. This work aimed to design, build, and verify a pneumatic hand training device for its eventual use in post-stroke rehabilitation. The system was built considering prior research in the field of robotic hand rehabilitation as well as specifications and design constraints developed with physiotherapists. The system contained pneumatic airbag actuators for the fingers and thumb of the hand, a set of flex, pressure, and flow sensors, and software and hardware controls. An experiment with the system was carried out on 30 healthy individuals. The sensor readings were analyzed for repeatability and reliability. Position sensors and an approximate biomechanical model of the index finger were used to estimate joint angles during operation. A survey was also issued to the users to evaluate their comfort levels with the device. It was found that the system was safe and comfortable when moving the fingers of the hand into an extension.

Initial Testing of Robotic Exoskeleton Hand Device for Stroke Rehabilitation

The preliminary test results of a novel robotic hand rehabilitation device aimed at treatment for the loss of motor abilities in the fingers and thumb due to stroke are presented. This device has been developed in collaboration with physiotherapists who regularly treat individuals who have suffered from a stroke. The device was tested on healthy adults to ensure comfort, user accessibility, and repeatability for various hand sizes in preparation for obtaining permission from regulatory bodies and implementing the design in a full clinical trial. Trials were conducted with 52 healthy individuals ranging in age from 19 to 93 with an average age of 58. A comfort survey and force data ANOVA were performed to measure hand motions and ensure the repeatability and accessibility of the system. Readings from the force sensor (p < 0.05) showed no significant difference between repetitions for each participant. All subjects considered the device comfortable. The device scored a mean comfort value of 8.5/10 on all comfort surveys and received the approval of all physiotherapists involved. The device has satisfied all design specifications, and the positive results of the participants suggest that it can be considered safe and reliable. It can therefore be moved forward for clinical trials with post-stroke users.

Design and Development of a Spherical 5-Bar Thumb Exoskeleton Mechanism for Poststroke Rehabilitation

This paper presents the kinematic design and development of a two degree-of-freedom (2DOF) spherical 5-bar thumb exoskeleton to augment the finger individuating grasp exercise robot (FINGER) rehabilitation robot, which assists the index and middle fingers individually in naturalistic grasping. The thumb module expands the capabilities of FINGER, allowing for broader proprioceptive training and assessment of hand function. The design process started by digitizing thumb-grasping motions to the index and the middle fingers separately, recorded from multiple healthy subjects utilizing a motion capture system. Fitting spheres to trajectory data of each subject allowed normalization of all subjects' data to a common center and radius. A two-revolute joint serial-chain mechanism was synthesized (intermediate optimization step) to reach the normalized trajectories. Next, the two resulting grasping trajectories were spatially sampled as targets for the 2DOF spherical 5-bar synthesis. Optimization of the spherical 5-bar included symmetry constraints and cost-function penalties for poor manipulability. The resulting exoskeleton assists both flexion/extension and abduction/adduction of the thumb enabling a wide range of motions. Consistent with FINGER, the parallel structure of the spherical 5-bar places the actuators at the base of the module, allowing for desirable characteristics, including high backdrivability, high controllable bandwidth, and low mechanical impedance. The mechanical design was developed from the kinematic solution, including an adjustable thumb cuff to accommodate different hand sizes. Fit and function of the device were tested on multiple subjects, including survivors of stroke. A proportional-derivative force controller with gravity and friction compensation was implemented to reduce resistance to motion during subject testing.

How Do Motor and Sensory Function Correlate with Daily Performance Recovery after Post-Stroke Robotic Intervention? A Secondary Analysis of a Non-Randomized Controlled Trial

New technologies have been developed to complement conventional interventions to better target the specific needs of people with stroke, and they have been shown to improve both function and performance. However, it is unknown whether the baseline levels of sensorimotor function and performance interrelate with the improvement in upper limb and daily performance. Thus, the aim of this study was to examine the relationship between baseline levels of sensorimotor function and daily performance and its impact on post-intervention improvement in people with stroke following a robotic intervention. A single-blind, non-randomized, controlled clinical trial was conducted. Participants in the experimental group (n = 9) received a robotic intervention in addition to conventional treatment. Sensorimotor function was measured with Semmes-Weinstein Monofilaments® and the Fugl-Meyer Assessment Upper Extremity Scale. Upper limb and daily performance were measured with the MAL and SIS-16 scales. The multivariate regression models showed that baseline levels of upper limb performance and motor function predicted >95% of the variance in upper limb performance (p < 0.001), while pre-intervention levels of daily performance explained >75% of the post-intervention variance (p < 0.05). These findings indicate that basal upper limb motor function is associated with improved performance following a combined intervention of conventional treatment and robotic intervention.

A CNN-LSTM model for six human ankle movements classification on different loads

This study aims to address three problems in current studies in decoding the ankle movement intention for robot-assisted bilateral rehabilitation using surface electromyogram (sEMG) signals: (1) only up to four ankle movements could be identified while six ankle movements should be classified to provide better training; (2) feeding the raw sEMG signals directly into the neural network leads to high computational cost; and (3) load variation has large influence on classification accuracy. To achieve this, a convolutional neural network (CNN)-long short-term memory (LSTM) model, a time-domain feature selection method of the sEMG, and a two-step method are proposed. For the first time, the Boruta algorithm is used to select time-domain features of sEMG. The selected features, rather than raw sEMG signals are fed into the CNN-LSTM model. Hence, the number of model's parameters is reduced from 331,938 to 155,042, by half. Experiments are conducted to validate the proposed method. The results show that our method could classify six ankle movements with relatively good accuracy (95.73%). The accuracy of CNN-LSTM, CNN, and LSTM models with sEMG features as input are all higher than that of corresponding models with raw sEMG as input. The overall accuracy is improved from 73.23% to 93.50% using our two-step method for identifying the ankle movements with different loads. Our proposed CNN-LSTM model have the highest accuracy for ankle movements classification compared with CNN, LSTM, and Support Vector Machine (SVM).

Development and evaluation of an efficient training program to facilitate the adoption of a novel neurorehabilitation device

Many rehabilitation devices are not adopted by therapists in practice. One major barrier is therapists' limited time and resources to get training. The objective of this study was to develop/evaluate an efficient training program for a novel rehabilitation device. The program was developed based on structured interviews with seven therapists for training preference and composed of asynchronous and in-person trainings following efficient teaching methods. The training program was evaluated for six occupational therapy doctoral students and six licensed therapists in neurorehabilitation practice. Training effectiveness was evaluated in a simulated treatment session in which 3 trainees shifted their roles among therapist applying the device, client, and peer assessor. In results, 11 of the 12 trainees passed the assessment of using the device in simulated treatment sessions. One trainee did not pass because s/he did not plug in the device to charge at the end. The in-person training fit within 1-h lunch break. All trainees perceived that they could effectively use the device in their practice and both asynchronous and in-person training easily fit into their schedule. This project serves as an example for development of an efficient and effective training program for a novel rehabilitation device to facilitate clinical adoption.

Interaction with a Hand Rehabilitation Exoskeleton in EMG-Driven Bilateral Therapy: Influence of Visual Biofeedback on the Users' Performance

The effectiveness of EMG biofeedback with neurorehabilitation robotic platforms has not been previously addressed. The present work evaluates the influence of an EMG-based visual biofeedback on the user performance when performing EMG-driven bilateral exercises with a robotic hand exoskeleton. Eighteen healthy subjects were asked to perform 1-min randomly generated sequences of hand gestures (rest, open and close) in four different conditions resulting from the combination of using or not (1) EMG-based visual biofeedback and (2) kinesthetic feedback from the exoskeleton movement. The user performance in each test was measured by computing similarity between the target gestures and the recognized user gestures using the L2 distance. Statistically significant differences in the subject performance were found in the type of provided feedback (p-value 0.0124). Pairwise comparisons showed that the L2 distance was statistically significantly lower when only EMG-based visual feedback was present (2.89 ± 0.71) than with the presence of the kinesthetic feedback alone (3.43 ± 0.75, p-value = 0.0412) or the combination of both (3.39 ± 0.70, p-value = 0.0497). Hence, EMG-based visual feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real time. This type of feedback could benefit patients in learning more quickly how to activate robot functions, increasing their motivation towards rehabilitation.

A Tenodesis-Induced-Grip exoskeleton robot (TIGER) for assisting upper extremity functions in stroke patients: a randomized control study

PURPOSE

This study was aimed toward developing a lightweight assisting tenodesis-induced-grip exoskeleton robot (TIGER) and to examine the performance of the TIGER in stroke patients with hemiplegia.

METHODS

This was a single-blinded, randomized control trial with pre-treatment, immediate post-treatment, and 12-week follow-up assessments. Thirty-four stroke patients were recruited and randomized to either an experimental or control group, where each participant in both groups underwent 40 min of training. In addition to a 20-min bout of regular task-specific motor training, each participant in the experimental group received 20 min of TIGER training, and the controls received 20 min of traditional occupational therapy in each treatment session. Primary outcomes based on the Fugl-Meyer Motor Assessment of Upper Extremity (FMA-UE) were recorded.

RESULTS

Thirty-two patients (94.1%) completed the study: 17 and 15 patients in the experimental and control groups, respectively. Significant beneficial effects were found on the total score (ANCOVA, p = 0.006), the wrist score (ANCOVA, p = 0.037), and the hand score (ANCOVA, p = 0.006) for the FMA-UE in the immediate post-treatment assessment of the participants receiving the TIGER training.

CONCLUSION

The TIGER has beneficial effects on remediating upper limb impairments in chronic stroke patients. Clinical trial registration: ClinicalTrials.gov; identifier NCT03713476Implications for rehabilitationBased on use-dependent plasticity concepts, robot training with the more distal segments of the upper extremities has a beneficial effect in patients with chronic stroke.A novel lightweight assisting tenodesis-induced-grip exoskeleton robot (TIGER) system using a mechanism involving musculotendinous coordination of the wrist and hand was proposed in this study.Between-group differences in changes in the upper limb motor performance were observed in the experimental group as compared to patients in the control group. For patients with chronic stroke, receiving 20 min of TIGER training in conjunction with 20 min of task-specific motor training led to clinically important changes in motor control and functioning of the affected upper limb.

Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification

This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the controller while performing the controller design. These features of the control algorithm eliminate an essential problem for rehabilitation robots in terms of easy customization and safe repetitive rehabilitation tasks that can be planned within certain constraints. Experiments were carried out with a designed hand rehabilitation system under repetitive and various therapy tasks. Real-time experiment results demonstrate the feasibility and efficiency of the proposed control approach to rehabilitation systems.

A five-bar mechanism to assist finger flexion-extension movement: system implementation

The lack of specialized personnel and assistive technology to assist in rehabilitation therapies is one of the challenges facing the health sector today, and it is projected to increase. For researchers and engineers, it represents an opportunity to innovate and develop devices that improve and optimize rehabilitation services for the benefit of society. Among the different types of injuries, hand injuries occur most frequently. These injuries require a rehabilitation process in order for the hand to regain its functionality. This article presents the fabrication and instrumentation of an end-effector prototype, based on a five-bar configuration, for finger rehabilitation that executes a natural flexion-extension movement. The dimensions were obtained through the gradient method optimization and evaluated through Matlab. Experimental tests were carried out to demonstrate the prototype’s functionality and the effectiveness of a five-bar mechanism acting in a vertical plane, where gravity influences the mechanism’s performance. Position control using fifth-order polynomials with via points was implemented in the joint space. The design of the end-effector was also evaluated by performing a theoretical comparison, calculated as a function of a real flexion-extension trajectory of the fingers and the angle of rotation obtained through an IMU. As a result, controlling the two degrees of freedom of the mechanism at several points of the trajectory assures the end-effector trajectory and therefore the fingers’ range of motion, which helps for full patient recovery.

EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients

Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user's intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients' four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.

Hand Rehabilitation Devices: A Comprehensive Systematic Review

A cerebrovascular accident, or a stroke, can cause significant neurological damage, inflicting the patient with loss of motor function in their hands. Standard rehabilitation therapy for the hand increases demands on clinics, creating an avenue for powered hand rehabilitation devices. Hand rehabilitation devices (HRDs) are devices designed to provide the hand with passive, active, and active-assisted rehabilitation therapy; however, HRDs do not have any standards in terms of development or design. Although the categorization of an injury's severity can guide a patient into seeking proper assistance, rehabilitation devices do not have a set standard to provide a solution from the beginning to the end stages of recovery. In this paper, HRDs are defined and compared by their mechanical designs, actuation mechanisms, control systems, and therapeutic strategies. Furthermore, devices with conducted clinical trials are used to determine the future development of HRDs. After evaluating the abilities of 35 devices, it is inferred that standard characteristics for HRDs should include an exoskeleton design, the incorporation of challenge-based and coaching therapeutic strategies, and the implementation of surface electromyogram signals (sEMG) based control.

Development of an Exoskeleton Platform of the Finger for Objective Patient Monitoring in Rehabilitation

This paper presents the application of an adaptive exoskeleton for finger rehabilitation. The system consists of a force-controlled exoskeleton of the finger and wireless coupling to a mobile application for the rehabilitation of complex regional pain syndrome (CRPS) patients. The exoskeleton has sensors for motion detection and force control as well as a wireless communication module. The proposed mobile application allows to interactively control the exoskeleton, store collected patient-specific data, and motivate the patient for therapy by means of gamification. The exoskeleton was applied to three CRPS patients over a period of six weeks. We present the design of the exoskeleton, the mobile application with its game content, and the results of the performed preliminary patient study. The exoskeleton system showed good applicability; recorded data can be used for objective therapy evaluation.

Effects of Intensive Vibratory Treatment with a Robotic System on the Recovery of Sensation and Function in Patients with Subacute and Chronic Stroke: A Non-Randomized Clinical Trial

Background: Sensory–motor deficits are frequent and affect the functionality after stroke. The use of robotic systems to improve functionality and motor performance is advisable; therefore, the aim of the present study was to evaluate the effects of intensive, high-frequency vibration treatment administered with a robotic system in subacute and chronic stroke patients in terms of upper limb sensitivity, motor function, quantity and quality of movement, and quality of life. Methods: A simple-blind, non-randomized controlled trial was conducted. The control group received conventional rehabilitation treatment and the experimental group received robotic treatment with an Amadeo^® robot in addition to their conventional rehabilitation sessions. Results: Intragroup analysis identified significant improvements in the experimental group in hand (p = 0.012), arm (p = 0.018), and shoulder (p = 0.027) sensitivity, as well as in motor function (FMA-UEmotor function, p = 0.028), integration of the affected limb (MAL-14amount scale, p = 0.011; MAL-14How well scale, p = 0.008), and perceived quality of life (SIS-16, p = 0.008). The measures between the control and experimental groups showed statistically significant differences in motor performance and spontaneous use of the affected limb (MAL-14amount scale, p = 0.021; MAL-14How well scale, p = 0.037). Conclusions: Intensive, high-frequency vibration with a robotic system, in combination with conventional intervention, improves the recovery of upper limb function in terms of quantity and quality of movement in patients with subacute and chronic stroke.

sEMG-Based Gesture Classifier for a Rehabilitation Glove

Human hand gesture recognition from surface electromyography (sEMG) signals is one of the main paradigms for prosthetic and rehabilitation device control. The accuracy of gesture recognition is correlated with the control mechanism. In this work, a new classifier based on the Bayesian neural network, pattern recognition networks, and layer recurrent network is presented. The online results obtained with this architecture represent a promising solution for hand gesture recognition (98.7% accuracy) in sEMG signal classification. For real time classification performance with rehabilitation devices, a new simple and efficient interface is developed in which users can re-train the classification algorithm with their own sEMG gesture data in a few minutes while enables shape memory alloy-based rehabilitation device connection and control. The position of reference for the rehabilitation device is generated by the algorithm based on the classifier, which is capable of detecting user movement intention in real time. The main aim of this study is to prove that the device control algorithm is adapted to the characteristics and necessities of the user through the proposed classifier with high accuracy in hand gesture recognition.

Experimental Characterization of A-AFiM, an Adaptable Assistive Device for Finger Motions

Robot rehabilitation devices are attracting significant research interest, aiming at developing viable solutions for increasing the patient’s quality of life and enhancing clinician’s therapies. This paper outlines the design and implementation of a low-cost robotic system that can assist finger motion rehabilitation by controlling and adapting both the position and velocity of fingers to the users′ needs. The proposed device consists of four slider-crank mechanisms. Each slider-crank is fixed and moves one finger (from the index to the little finger). The finger motion is adjusted through the regulation of a single link length of the mechanism. The trajectory that is generated corresponds to the natural flexion and extension trajectory of each finger. The functionality of this mechanism is validated by experimental image processing. Experimental validation is performed through tests on healthy subjects to demonstrate the feasibility and user-friendliness of the proposed solution.

Use of Functional Data to Model the Trajectory of an IMU and Classify Levels of Motor Impairment for Stroke Patients

Motor impairment evaluations are key rehabilitation-related assessments for patients with stroke. Currently, such evaluations are subjective; they are based on physicians' judgements regarding the actions performed by patients. This leads to inconsistent clinical results. Many inertial sensing elements for motion detection have been designed. However, to more easily and rapidly evaluate motor impairment, we require a system that can collect data effectively to predict the degree of motor impairment. Lin et al. used data gloves equipped with an inertial measurement unit (IMU) to collect movement trajectories for motor impairment evaluations in patients with stroke. The present study used functional data analysis to model data trajectories to reduce the influence of noise from IMU data and proposed using coefficients of function as features for classifying motor impairment. To verify the appropriateness of feature construction, five classification methods were used to evaluate the extracted features in terms of the overall and sensor-specific ability to classify levels of motor impairment. The results indicated that the features derived from cubic smoothing splines could effectively reflect key data characteristics, and a support vector machine yielded relatively high overall and sensor-specific accuracy for distinguishing between levels of motion impairment in patients with stroke. Future data glove systems can contain cubic smoothing splines to extract hand function features and then classify motion impairment for appropriate rehabilitation programs to be prescribed.

Robot-assisted distal training improves upper limb dexterity and function after stroke: a systematic review and meta-regression

INTRODUCTION

Stroke is one of the top 10 causes of death worldwide, and more than half of stroke patients face distal upper extremity dysfunction. Considering that robot-assisted training may be effective in improving distal upper extremity function, the review evaluated the effect of robot-assisted distal training on motor function, hand dexterity, and spasticity after stroke.

METHODS

Eleven databases were systematically searched for randomised controlled trials (RCTs) from inception until Aug 28, 2021. Meta-analysis and meta-regression were performed to investigate the overall effect and source of heterogeneity, respectively.

RESULTS

Twenty-two trials involving 758 participants were included in this systematic review. The overall effect of robot-assisted distal training on the motor function of the wrists and hands was significant improvement (MD = 3.92; 95% CI, 3.04-4.80; P < 0.001). The robot-assisted training had a significantly beneficial effect on other motor functions (MD = 2.84; 95% CI, 1.54-4.14; P < 0.001); dexterity (MD = 9.01; 95% CI, -12.07--5.95; P < 0.001), spasticity, upper extremity strength (SMD = 0.42; 95% CI, 0.07-0.78; P = 0.02) and activities of daily living (SMD = 0.70; 95% CI, 0.29-1.23; P < 0.001). A series of subgroup analyses showed preferable design and effective regime of training. Meta-regression indicated the statistically significant effect of the year of trial, country, and duration on the effectiveness of training.

CONCLUSION

Robot-assisted distal training has a significant effect on motor function, dexterity and spasticity of the upper extremity, compared to conventional therapy.

Somesthetic, Visual, and Auditory Feedback and Their Interactions Applied to Upper Limb Neurorehabilitation Technology: A Narrative Review to Facilitate Contextualization of Knowledge

Reduced hand dexterity is a common component of sensorimotor impairments for individuals after stroke. To improve hand function, innovative rehabilitation interventions are constantly developed and tested. In this context, technology-based interventions for hand rehabilitation have been emerging rapidly. This paper offers an overview of basic knowledge on post lesion plasticity and sensorimotor integration processes in the context of augmented feedback and new rehabilitation technologies, in particular virtual reality and soft robotic gloves. We also discuss some factors to consider related to the incorporation of augmented feedback in the development of technology-based interventions in rehabilitation. This includes factors related to feedback delivery parameter design, task complexity and heterogeneity of sensory deficits in individuals affected by a stroke. In spite of the current limitations in our understanding of the mechanisms involved when using new rehabilitation technologies, the multimodal augmented feedback approach appears promising and may provide meaningful ways to optimize recovery after stroke. Moving forward, we argue that comparative studies allowing stratification of the augmented feedback delivery parameters based upon different biomarkers, lesion characteristics or impairments should be advocated (e.g., injured hemisphere, lesion location, lesion volume, sensorimotor impairments). Ultimately, we envision that treatment design should combine augmented feedback of multiple modalities, carefully adapted to the specific condition of the individuals affected by a stroke and that evolves along with recovery. This would better align with the new trend in stroke rehabilitation which challenges the popular idea of the existence of an ultimate good-for-all intervention.

Healing Hands: The Tactile Internet in Future Tele-Healthcare

In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today’s communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings.

Comparative effects of EMG-driven robot-assisted therapy versus task-oriented training on motor and daily function in patients with stroke: a randomized cross-over trial

Background

Robot-assisted hand training has shown positive effects on promoting neuromuscular control. Since both robot-assisted therapy and task-oriented training are often used in post-stroke rehabilitation, we raised the question of whether two interventions engender differential effects in different domains.

Methods

The study was conducted using a randomized, two-period crossover design. Twenty-four chronic stroke survivors received a 12-session robot-assisted intervention followed by a 12-session task-oriented intervention or vice versa. A 1-month washout period between each intervention was implemented. Outcome measures were evaluated before the intervention, after the first 12-session intervention, and after the second 12-session intervention. Clinical assessments included Fugl-Meyer Assessment for Upper Extremity, Wolf Motor Function Test, Action Research Arm Test and Motor Activity Log.

Results

Our findings suggested that EMG-driven robot-assisted therapy was as effective as task-oriented training in terms of improving upper limbs functional performance in activity domain, and robot-assisted therapy was more effective in improving movement duration during functional tasks. Task-oriented training showed better improvement in body function domain and activity and participation domain, especially in improving spontaneous use of affected arm during daily activities.

Conclusions

Both intervention protocol had their own advantages in different domains, and robot-assisted therapy may save manpower and be considered as an alternative intervention to task-oriented training. Combining the two approaches could yield results greater than either alone, which awaits further study.

Trial registration: ClinicalTrials.gov Identifier: NCT03624153. Registered on 9th August 2018, https://clinicaltrials.gov/ct2/show/NCT03624153.

Therapeutic Effects of a Newly Developed 3D Magnetic Finger Rehabilitation Device in Subacute Stroke Patients: A Pilot Study

We developed a magnetic-force-based three-dimensional (3D) rehabilitation device that can perform motor rehabilitation treatment for paralyzed fingers, regardless of upper extremity movement and position, and investigated the therapeutic effects of the device. An end-effector type rehabilitation device that can generate magnetic fields in three directions was developed using electromagnets and permanent magnetics. A double-blinded randomized controlled pilot study was conducted with a total of 12 patients. The intervention group had rehabilitation treatment using the developed magnetic finger rehabilitation device for 30 min a day for four weeks. The control group underwent exercise rehabilitation treatment. The control group received conventional occupational therapy on the upper limbs, including hands, from an occupational therapist, for the same amount of time. Adverse effects were monitored, and the patient’s sensory or proprioceptive deficits were examined before the intervention. No participants reported safety concerns while the intervention was conducted. The Wolf Motor Function Test (WMFT) scores were significantly improved in the intervention group (from 13.4 ± 3.6 to 20.9 ± 4.0 points) compared to the control group (from 13.1 ± 4.0 to 15.2 ± 3.8 points) (p = 0.016). The patients in the intervention group (from 88 ± 12 to 67 ± 13 s) showed greater improvement of WMFT times compared to the control group (from 89 ± 10 to 73 ± 11 s) (p = 0.042). The Manual Function Test and the upper limb score of the Fugl-Meyer Assessment were significantly improved in the intervention group compared with the control group (p = 0.038 and p = 0.042). The patients in the intervention group also showed significantly greater enhancement of the Korean version of the modified Barthel Index than the control group (p = 0.042). Rehabilitation treatment using the 3D magnetic-force-driven finger rehabilitation device helped improve finger motor function and activities of daily living in subacute stroke patients.

An Adaptive Mechatronic Exoskeleton for Force-Controlled Finger Rehabilitation

This paper presents a novel mechatronic exoskeleton architecture for finger rehabilitation. The system consists of an underactuated kinematic structure that enables the exoskeleton to act as an adaptive finger stimulator. The exoskeleton has sensors for motion detection and control. The proposed architecture offers three main advantages. First, the exoskeleton enables accurate quantification of subject-specific finger dynamics. The configuration of the exoskeleton can be fully reconstructed using measurements from three angular position sensors placed on the kinematic structure. In addition, the actuation force acting on the exoskeleton is recorded. Thus, the range of motion (ROM) and the force and torque trajectories of each finger joint can be determined. Second, the adaptive kinematic structure allows the patient to perform various functional tasks. The force control of the exoskeleton acts like a safeguard and limits the maximum possible joint torques during finger movement. Last, the system is compact, lightweight and does not require extensive peripherals. Due to its safety features, it is easy to use in the home. Applicability was tested in three healthy subjects.

Classification of error-related potentials evoked during stroke rehabilitation training

Objective.Error-related potentials (ErrPs) are elicited in the human brain following an error's perception. Recently, ErrPs have been observed in a novel task situation, i.e. when stroke patients perform upper-limb rehabilitation exercises. These ErrPs can be used to developassist-as-needed(AAN) robotic stroke rehabilitation systems. However, to date, there is no reported research on assessing the feasibility of using the ErrPs to implement the AAN approach. Hence, in this study, we evaluated and compared the single-trial classification of novel ErrPs using various classical machine learning and deep learning approaches.Approach.Electroencephalogram data of 13 stroke patients recorded while performing an upper-limb physical rehabilitation exercise were used. Two classification approaches, one combining the xDAWN spatial filtering and support vector machines, and the other using a convolutional neural network-based double transfer learning, were utilized.Main results.Results showed that the ErrPs could be detected with a mean area under the receiver operating characteristics curve of 0.838, and a mean accuracy of 0.842, 0.257 above the chance level (p< 0.05), for a within-subject classification. The results indicated the feasibility of using ErrP signals in real-time AAN robot therapy with evidence from the conducted latency analysis, cross-subject classification, and three-class asynchronous classification.Significance.The findings presented support our proposed approach of using ErrPs as a measure to trigger and/or modulate as required the robotic assistance in a real-timehuman-in-the-looprobotic stroke rehabilitation system.

Exoskeleton versus end-effector robot-assisted therapy for finger-hand motor recovery in stroke survivors: systematic review and meta-analysis

INTRODUCTION

The growing number of stroke survivors with residual hand disabilities requires the development of efficient recovery therapy, and robotic rehabilitation can play an important role.

OBJECTIVE

The study aims to compare the relative effects of end-effector (EE) and exoskeleton (EXO) hand devices in motor recovery of patients with finger-hand motor impairment stroke.

METHODS

We identified randomized controlled trials (RCTs) through search in database on PubMed, Embase, MEDLINE, Cochrane library until October 2020. We included as outcomes: motricity index (MI), quick version of disabilities of the arm, shoulder, and hand (QuickDASH) questionnaire, and Fugl-Meyer assessment for upper extremity (FMAUE). We performed a systematic review, a meta-analysis, and a surface under the cumulative ranking analysis (SUCRA).

RESULTS

We included five RTCs and 149 subjects. MI showed a signifìcant improvement (p < .05) in robotic intervention group compared to control group (effect size, ES: 9.47; confidence interval, CI: 3.91, 15.03). QuickDASH reported a significant reduction (p < .05) in EXO group (ES: -6.71; CI: -9.17, -4.25). FMAUE showed a significant improvement (p < .05) in the EE group (ES:3; CI:1.97, 4.04). SUCRA analysis of MI demonstrated that robotic interventions are more likely to be the best option for motor recovery (97.3% of probability EXO; 48.3% EE; 4.4% control).

CONCLUSION

Despite the limited number of studies included, exoskeleton robotic devices might be a better option than end-effector devices in the treatment of fingers motor impairment in stroke patients. Further studies are still needed to confirm the findings and should focus on a direct comparison of the two devices.

Mechatronic Design of a Robot for Upper Limb Rehabilitation at Home

This paper addresses the design of a novel bionic robotic device for upper limb rehabilitation tasks at home. The main goal of the design process has been to obtain a rehabilitation device, which can be easily portable and can be managed remotely by a professional therapist. This allows to treat people also in regions that are not easily reachable with a significant cost reduction. Other potential benefits can be envisaged, for instance, in the possibility to keep social distancing while allowing rehabilitation treatments even during a pandemic spread. Specific attention has been devoted to design the main mechatronic components by developing specific kinematics and dynamics models. The design process includes the implementation of a specific control hardware and software. Preliminary experimental tests are reported to show the effectiveness and feasibility of the proposed design solution.

Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection

Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient's progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.

Repurposing an EMG Biofeedback Device for Gait Rehabilitation: Development, Validity and Reliability

Gait impairment often limits physical activity and negatively impacts quality of life. EMG-Biofeedback (EMG-BFB), one of the more effective interventions for improving gait impairment, has been limited to laboratory use due to system costs and technical requirements, and has therefore not been tested on a larger scale. In our research, we aimed to develop and validate a cost-effective, commercially available EMG-BFB device for home- and community-based use. We began by repurposing mTrigger® (mTrigger LLC, Newark, DE, USA), a cost-effective, portable EMG-BFB device, for gait application. This included developing features in the cellphone app such as step feedback, success rate, muscle activity calibration, and cloud integration. Next, we tested the validity and reliability of the mTrigger device in healthy adults by comparing it to a laboratory-grade EMG system. While wearing both devices, 32 adults walked overground and on a treadmill at four speeds (0.3, 0.6, 0.9, and 1.2 m/s). Statistical analysis revealed good to excellent test-retest reliability (r > 0.89) and good to excellent agreement in the detection of steps (ICC > 0.85) at all speeds between two systems for treadmill walking. Our results indicated that mTrigger compared favorably to a laboratory-grade EMG system in the ability to assess muscular activity and to provide biofeedback during walking in healthy adults.

Attention Enhancement for Exoskeleton-Assisted Hand Rehabilitation Using Fingertip Haptic Stimulation

Active enrollment in rehabilitation training yields better treatment outcomes. This paper introduces an exoskeleton-assisted hand rehabilitation system. It is the first attempt to combine fingertip cutaneous haptic stimulation with exoskeleton-assisted hand rehabilitation for training participation enhancement. For the first time, soft material 3D printing techniques are adopted to make soft pneumatic fingertip haptic feedback actuators to achieve cheaper and faster iterations of prototype designs with consistent quality. The fingertip haptic stimulation is synchronized with the motion of our hand exoskeleton. The contact force of the fingertips resulted from a virtual interaction with a glass of water was based on data collected from normal hand motions to grasp a glass of water. System characterization experiments were conducted and exoskeleton-assisted hand motion with and without the fingertip cutaneous haptic stimulation were compared in an experiment involving healthy human subjects. Users' attention levels were monitored in the motion control process using a Brainlink EEG-recording device and software. The results of characterization experiments show that our created haptic actuators are lightweight (6.8 ± 0.23 g each with a PLA fixture and Velcro) and their performance is consistent and stable with small hysteresis. The user study experimental results show that participants had significantly higher attention levels with additional haptic stimulations compared to when only the exoskeleton was deployed; heavier stimulated grasping weight (a 300 g glass) was associated with significantly higher attention levels of the participants compared to when lighter stimulated grasping weight (a 150 g glass) was applied. We conclude that haptic stimulations increase the involvement level of human subjects during exoskeleton-assisted hand exercises. Potentially, the proposed exoskeleton-assisted hand rehabilitation with fingertip stimulation may better attract user's attention during treatment.

Explicit motor sequence learning after stroke: a neuropsychological study

Motor learning interacts with and shapes experience-dependent cerebral plasticity. In stroke patients with paresis of the upper limb, motor recovery was proposed to reflect a process of re-learning the lost/impaired skill, which interacts with rehabilitation. However, to what extent stroke patients with hemiparesis may retain the ability of learning with their affected limb remains an unsolved issue, that was addressed by this study. Nineteen patients, with a cerebrovascular lesion affecting the right or the left hemisphere, underwent an explicit motor learning task (finger tapping task, FTT), which was performed with the paretic hand. Eighteen age-matched healthy participants served as controls. Motor performance was assessed during the learning phase (i.e., online learning), as well as immediately at the end of practice, and after 90 min and 24 h (i.e., retention). Results show that overall, as compared to the control group, stroke patients, regardless of the side (left/right) of the hemispheric lesion, do not show a reliable practice-dependent improvement; consequently, no retention could be detected in the long-term (after 90 min and 24 h). The motor learning impairment was associated with subcortical damage, predominantly affecting the basal ganglia; conversely, it was not associated with age, time elapsed from stroke, severity of upper-limb motor and sensory deficits, and the general neurological condition. This evidence expands our understanding regarding the potential of post-stroke motor recovery through motor practice, suggesting a potential key role of basal ganglia, not only in implicit motor learning as previously pointed out, but also in explicit finger tapping motor tasks.

Determining the Effectiveness of a New Device for Hand Therapy (The FEPSim Device): Feasibility Protocol for a Randomized Controlled Trial Study

Background

Impairments of the forearm, wrist, and hand affect a sizable proportion of individuals and impose a significant economic burden on health care systems. FEPSim is a medical device for hand and wrist rehabilitation. The FEPSim device could be part of the standard of care for upper extremity rehabilitation during therapeutic activities to increase range of motion, dexterity, and strength. FEPSim has not yet been tested in a health care setting; therefore, a trial of the effectiveness of FEPSim in upper extremity rehabilitation is warranted.

Objective

This study aims to assess the feasibility of conducting a definitive trial in terms of recruitment, eligibility criteria, the type and number of diagnoses included, the length and dosage of the intervention, and data collection methods. This study also aims to gather clinical and statistical information as well as information related to the cost and usability, which allows for an economic evaluation of the device.

Methods

The trial will use a randomized controlled design comprising 47 intervention participants and 47 control group participants. Participants will be adults (age≥18 years) attending outpatient rehabilitation with limitations in their forearm, wrist, or hand function due to distal radial or ulnar fractures, stroke, or osteoarthritis. This study’s primary outcome variables are related to patients’ range of motion and strength, specifically active and passive wrist flexion and extension range of motion; active and passive forearm pronation and supination range of motion; grip strength; and pinch strength. The secondary outcome variables are related to patients’ perceived wrist pain and disability in activities of daily living. The patients’ perceived wrist pain and disability in activities of daily living will be measured using the patient-rated wrist evaluation questionnaire. The control group will receive the standard of care at each of the 2 hospital facilities (Glenrose Rehabilitation and Royal Alexandra Hospitals). The intervention group will receive the same standard of care as the control group at each facility and will use the FEPSim device for therapeutic activities to increase strength, range of motion, resistance, and dexterity. All the participants will be assessed at baseline (week 0); weeks 2, 4, and 8; and postintervention (week 10).

Results

The FEPSim study was launched in April 2020. This study is currently on hold because of the global COVID-19 pandemic. The recruitment process is expected to resume by September 2020, and the primary impact analysis is expected to be conducted by December 2020.

Conclusions

This study will provide valuable information on the measurement of comparative intervention effects, technology acceptance by hand therapists, and how associated treatment and product costs will contribute to the evidence planning process, which will be crucial for the future adoption of FEPSim.

Trial Registration

International Standard Randomized Controlled Trial Number Registry ISRCTN13656014; https://www.isrctn.com/ISRCTN13656014

International Registered Report Identifier (IRRID)

PRR1-10.2196/22145

Digital Technology in Clinical Trials for Multiple Sclerosis: Systematic Review

Clinical trials in multiple sclerosis (MS) have been including digital technology tools to overcome limitations in treatment delivery and disease monitoring. In March 2020, we conducted a systematic search on pubmed.gov and clinicaltrials.gov databases (with no restrictions) to identify all relevant published and unpublished clinical trials, in English language, including MS patients, in which digital technology was applied. We used “multiple sclerosis” and “clinical trial” as the main search words, and “app”, “digital”, “electronic”, “internet” and “mobile” as additional search words, separately. Digital technology is part of clinical trial interventions to deliver psychotherapy and motor rehabilitation, with exergames, e-training, and robot-assisted exercises. Digital technology has been used to standardise previously existing outcome measures, with automatic acquisitions, reduced inconsistencies, and improved detection of symptoms (e.g., electronic recording of motor performance). Other clinical trials have been using digital technology for monitoring symptoms that would be otherwise difficult to detect (e.g., fatigue, balance), for measuring treatment adherence and side effects, and for self-assessment purposes. Collection of outcome measures is progressively shifting from paper-based on site, to internet-based on site, and, in the future, to internet-based at home, with the detection of clinical and treatment features that would have remained otherwise invisible. Similarly, remote interventions provide new possibilities of motor and cognitive rehabilitation.

Reversed Polarity bi-tDCS over M1 during a Five Days Motor Task Training Did Not Influence Motor Learning. A Triple-Blind Clinical Trial

Transcranial direct current stimulation (tDCS) has been investigated as a way of improving motor learning. Our purpose was to explore the reversal bilateral tDCS effects on manual dexterity training, during five days, with the retention component measured after 5 days to determine whether somatosensory effects were produced. In this randomized, triple-blind clinical trial, 28 healthy subjects (14 women) were recruited and randomized into tDCS and placebo groups, although only 23 participants (13 women) finished the complete protocol. Participants received the real or placebo treatment during five consecutive days, while performing a motor dexterity training program of 20 min. The motor dexterity and the sensitivity of the hand were assessed pre- and post-day 1, post 5 days of training, and 5 days after training concluded. Training improved motor dexterity, but tDCS only produced a tendency to improve retention. The intervention did not produce changes in the somatosensory variables assessed. Thus, reversal bi-tDCS had no effects during motor learning on healthy subjects, but it could favor the retention of the motor skills acquired. These results do not support the cooperative inter-hemispheric model.

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.

Hand rehabilitation with sonification techniques in the subacute stage of stroke

After a stroke event, most survivors suffer from arm paresis, poor motor control and other disabilities that make activities of daily living difficult, severely affecting quality of life and personal independence. This randomized controlled trial aimed at evaluating the efficacy of a music-based sonification approach on upper limbs motor functions, quality of life and pain perceived during rehabilitation. The study involved 65 subacute stroke individuals during inpatient rehabilitation allocated into 2 groups which underwent usual care dayweek) respectively of standard upper extremity motor rehabilitation or upper extremity treatment with sonification techniques. The Fugl-Meyer Upper Extremity Scale, Box and Block Test and the Modified Ashworth Scale were used to perform motor assessment and the McGill Quality of Life-it and the Numerical Pain Rating Scale to assess quality of life and pain. The assessment was performed at baseline, after 2 weeks, at the end of treatment and at follow-up (1 month after the end of treatment). Total scores of the Fugl-Meyer Upper Extremity Scale (primary outcome measure) and hand and wrist sub scores, manual dexterity scores of the affected and unaffected limb in the Box and Block Test, pain scores of the Numerical Pain Rating Scale (secondary outcomes measures) significantly improved in the sonification group compared to the standard of care group (time*group interaction < 0.05). Our findings suggest that music-based sonification sessions can be considered an effective standardized intervention for the upper limb in subacute stroke rehabilitation.

The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

BACKGROUND

Recently, error-related negativity (ERN) signals are proposed to develop an assist-as-needed robotic stroke rehabilitation program. Stroke patients' state-of-mind, such as motivation to participate and active involvement in the rehabilitation program, affects their rate of recovery from motor disability. If the characteristics of the robotic stroke rehabilitation program can be altered based on the state-of-mind of the patients, such that the patients remain engaged in the program, the rate of recovery from their motor disability can be improved. However, before that, it is imperative to understand how the states-of-mind of a participant affect their ERN signal.

METHODS

This study aimed to determine the association between the ERN signal and the psychological and cognitive states of the participants. Experiments were conducted on stroke patients, which involved performing a physical rehabilitation exercise and a questionnaire to measure participants' subjective experience on four factors: motivation in participating in the experiment, perceived effort, perceived pressure, awareness of uncompleted exercise trials while performing the rehabilitation exercise. Statistical correlation analysis, EEG time-series and topographical analysis were used to assess the association between the ERN signals and the psychological and cognitive states of the participants.

RESULTS

A strong correlation between the amplitude of the ERN signal and the psychological and cognitive states of the participants was observed, which indicate the possibility of estimating the said states using the amplitudes of the novel ERN signal.

CONCLUSIONS

The findings pave the way for the development of an ERN based dynamically adaptive assist-as-needed robotic stroke rehabilitation program of which characteristics can be altered to keep the participants' motivation, effort, engagement in the rehabilitation program high. In future, the single-trial prediction ability of the novel ERN signals to predict the state-of-mind of stroke patients will be evaluated.

Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review

BACKGROUND

Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective.

METHODS

A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures.

RESULTS

30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery.

CONCLUSION

19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

On the Optimal Synthesis of a Finger Rehabilitation Slider-Crank-Based Device with a Prescribed Real Trajectory: Motion Specifications and Design Process

This article discusses the mechanical redesign of a finger rehabilitation device based on a slider-crank mechanism. The redesign proposal is to obtain a smaller and more portable device that can recreate the motion trajectories of a finger. The real finger motion trajectories were recorded using a motion capture system. The article focused on the optimal synthesis of the rehabilitation device mechanism formulated as a classic trajectory generation problem. The proposed approach was combined with the recorded finger movements and solved using the genetic algorithm (GA) method. Optimization criteria and constraints were successively formulated and solved using a mono-objective function.

Determining Factors that Influence Adoption of New Post-Stroke Sensorimotor Rehabilitation Devices in the USA

Rehabilitation device efficacy alone does not lead to clinical practice adoption. Previous literature identifies drivers for device adoption by therapists but does not identify the best settings to introduce devices, the roles of different stakeholders including rehabilitation directors, or specific criteria to be met during device development. The objective of this work was to provide insights into these areas to increase clinical adoption of post-stroke restorative rehabilitation devices. We interviewed 107 persons including physical/occupational therapists, rehabilitation directors, and stroke survivors and performed content analysis. Unique to this work, care settings in which therapy goals are best aligned for restorative devices were found to be outpatient rehabilitation, followed by inpatient rehabilitation. Therapists are the major influencers for adoption because they typically introduce new rehabilitation devices to patients for both clinic and home use. We also learned therapists' utilization rate of a rehabilitation device influences a rehabilitation director's decision to acquire the device for facility use. Main drivers for each stakeholder are identified, along with specific criteria to add details to findings from previous literature. In addition, drivers for home adoption of rehabilitation devices by patients are identified. Rehabilitation device development should consider the best settings to first introduce the device, roles of each stakeholder, and drivers that influence each stakeholder, to accelerate successful adoption of the developed device.

The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis

Introduction: Although there is a need for rehabilitation treatment with the increase in the aging population, the shortage of skilled physicians frustrates this necessity. Robotic technology has been advocated as one of the most viable methods with the potential to replace humans in providing physical rehabilitation of patients with neurological impairment. However, because the pioneering robot devices suffer several reservations such as safety and comfort concerns in clinical practice, there is an urgent need to provide upgraded replacements. The rapid development of intelligent computing has attracted the attention of researchers concerning the utilization of computational intelligence algorithms for robots in rehabilitation. Areas covered: This article reviews the state of the art and advances of robotic neurorehabilitation with computational intelligence. We classified advances into two categories: mechanical structures and control methods. Prospective outlooks of rehabilitation robots also have been discussed. Expert opinion: The aggravation of global aging has promoted the application of robotic technology in neurorehabilitation. However, this approach is not mature enough to guarantee the safety of patients. Our critical review summarizes multiple computation algorithms which have been proved to be valuable for better robotic use in clinical settings and guide the possible future advances in this industry.

An Adaptive Fast Terminal Sliding Mode Controller of Exercise-Assisted Robotic Arm for Elbow Joint Rehabilitation Featuring Pneumatic Artificial Muscle Actuator

Due to the time-varying nonlinear dynamic, uncertain model and hysteresis characteristics of the pneumatic artificial muscle (PAM) actuator, it is not easy to apply model-based control algorithms for monitoring, as well as controlling, the operation of systems driven by PAM actuators. Hence, the main aim of this work is to propose an intelligent controller named adaptive sliding controller adding compensator (ASC + C) to operate a robotic arm, featuring a pneumatic artificial muscle actuator, which assists rehabilitation exercise of the elbow joint function. The structure of the proposed controller is a combination between the fuzzy logic technique and Proportional Integral Derivative (PID) algorithm. In which, the input of fuzzy logic controller is the sliding surface, meanwhile, its output is the estimated value of the unknown nonlinear function, meaning that the model-based requirement is released. A PID controller works as a compensator with online learning ability and is designed to compensate because of the approximate error and hysteresis characteristic. Additionally, to improve convergence and to obtain stability, a fast terminal sliding manifold is introduced and online learning laws for parameters of the controller are attainted through the stable criterion of Lyapunov. Finally, an experimental apparatus is also fabricated to evaluate control response of the system. The experimental result confirmed strongly the ability of the proposed controller, which indicates that the ASC + C can obtain a steady state tracking error less than 5 degrees and a position response without overshoot.