Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises

In conventional and robotic rehabilitation, the patient's active participation in exercises is essential for the maximum functional output to be received from therapy. In rehabilitation exercises performed with robotic devices, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient's therapy performance to improve active participation. However, the existing therapy performance evaluation methods are based on either some specific device designs or certain therapy tasks, which limits their widespread use. In this paper, the effectiveness of a participation assessment system, which can evaluate patients' therapy performance, tiredness, and slacking independent of any device design and therapy exercise, was clinically tested on ten patients diagnosed with frozen shoulder syndrome. The patients performed exercises using the system once a week throughout their 4-week treatment period. Multiple clinical measurements and scales were employed during the clinical study to assess patients' progress and status, such as tiredness throughout the therapy process. The clinical data, along with the patient findings obtained from the participation assessment system, were statistically analyzed and compared. The findings revealed that the patients' improvements and progress during the therapy process clinically coincide with the variations in the performance evaluation results of the system, and the implemented method successfully assesses the patients' participation during the rehabilitation exercises.

Model-free finite-time robust control using fractional-order ultra-local model and prescribed performance sliding surface for upper-limb rehabilitation exoskeleton

To address the trajectory tracking issue of upper-limb rehabilitation exoskeleton with uncertainties and external disturbances, this paper proposes a fractional-order ultra-local model-based model-free finite-time robust controller (FO-FTRC) using predefined performance sliding surface. Different from previous model-free control strategies, a novel multi-input multi-output (MIMO) fractional-order ultra-local model which is a virtual model is proposed to approximate the complex uncertain nonlinear exoskeleton dynamics in a short sliding time window. This allows the design of controller to be independent of any exoskeleton model information and reduces the difficulty of controller design. The developed robust model-free control method incorporates a fractional-order quasi-time delay estimator (FO-QTDE), unknown disturbance estimator (UDE) as well as prescribed performance sliding mode control (PPSMC). The FO-QTDE is utilized to estimate the unknown lumped uncertainties which employs short time delayed knowledge only about the control input. However, the low-pass filter is always added for FO-QTDE when disturbances change fast, which leads to unavoidable estimation error. Then, UDE is designed to further eliminate the estimation error of FO-QTDE to enhance control performance. The PPSMC is constructed to converge sliding surface to zero in a finite time. Besides, the sliding surface is always limited in performance boundaries. After that, the overall system stability and convergence analyses are demonstrated by using the Lyapunov theorem. Finally, with the comparison to other methods of α-variable adaptive model free control (α-AMFC), time-delay estimation-based continuous nonsingular fast terminal sliding mode controller (TDE-CNFTSMC), time delay estimation (TDE)-based model-free fractional-order nonsingular fast terminal sliding mode control (MFF-TSM) and fractional-order proportion-differential (PDβ), the co-simulation results on 7-degree-of-freedom (DOF) iReHave upper-limb exoskeleton virtual prototype and experiment results on 2-DOF upper-limb exoskeleton are obtained to illustrate the effectiveness and superiority of the proposed FO-FTRC method.

Human movement modifications induced by different levels of transparency of an active upper limb exoskeleton

Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency. In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different "transparent" controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics. In other words, very different residual interaction efforts do not necessarily induce very different movement kinematics. Such a behavior could be explained by a natural human tendency to expend effort to preserve their preferred kinematics, which should be taken into account in future transparent controllers evaluation.

A wearable system to assist impaired-neck patients: Design and evaluation

Patients with neurological disorders, such as amyotrophic lateral sclerosis, Parkinson's disease, and cerebral palsy, often face challenges due to head-neck immobility. The conventional treatment approach involves using a neck collar to maintain an upright head position, but this can be cumbersome and restricts head-neck movements over prolonged periods. This study introduces a wearable robot capable of providing three anatomical head motions for training and assistance. The primary contributions of this research include the design of an optimized structure and the incorporation of human-robot interaction. Based on human head motion data, our primary focus centered on developing a robot capable of accommodating a significant range of neutral head movements. To ensure safety, impedance control was employed to facilitate human-robot interaction. A human study was conducted involving 10 healthy subjects who participated in an experiment to assess the robot's assistance capabilities. Passive and active modes were used to evaluate the robot's effectiveness, taking into account head-neck movement error and muscle activity levels. Surface electromyography signals (sEMG) were collected from the splenius capitis muscles during the experiment. The results demonstrated that the robot covered nearly 85% of the overall range of head rotations. Importantly, using the robot during rehabilitation led to reduced muscle activation, highlighting its potential for assisting individuals with post-stroke movement impairments.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.8918 0 , 2.6753 0 and 8.0258 0 , respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

Exo Supportive Devices: Summary of Technical Aspects

Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement (for rehabilitation applications). There have been several studies on using exosuits with this purpose in mind. So, the current review offers a critical perspective and a detailed analysis of the steps and key decisions involved in the conception of an exoskeleton. Choices such as design aspects, base materials (structure), actuators (force and motion), energy sources (actuation), and control systems will be discussed, pointing out their advantages and disadvantages. Moreover, examples of exosuits (full-body, upper-body, and lower-body devices) will be presented and described, including their use cases and outcomes. The future of exoskeletons as possible assisted movement solutions will be discussed-pointing to the best options for rehabilitation.

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of advantages over manual therapies, but can suffer from slacking behavior, where the user lets the robot guide their movements even when they are capable of doing so by themselves, representing a major barrier to reaching the full potential of robot-assist rehabilitation. This is a pilot clinical study that investigates the use of an electromyography-based adaptive assist-as-needed controller to avoid slacking behavior during robotic rehabilitation for people with stroke. The study involved a convenience sample of five individuals with chronic stroke who underwent a robot therapy program utilizing horizontal arm tasks. The Fugl-Meyer assessment (FM) was used to document motor impairment status at baseline. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder surface electromyography (EMG) and electroencephalography (EEG) were used to assess the controller's performance. The cross-sectional results showed strong second-order relationships between FM score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed, EMG and EEG metrics were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks. This study highlights the importance of multi-sensor integration to monitor and improve upper-extremity robotic therapy.

Human-Centered Functional Task Design for Robotic Upper-Limb Rehabilitation

Robotic rehabilitation has demonstrated slight positive effects compared to traditional care, but there is still a lack of targeted high-level control strategies in the current state-of-the-art for minimizing pathological motor behaviors. In this study, we analyzed upper-limb motion capture data from healthy subjects performing a pick-and-place task to identify task-specific variability in postural patterns. The results revealed consistent behaviors among subjects, presenting an opportunity to develop a novel extraction method for variable volume references based solely on observations from healthy individuals. These human-centered references were tested on a simulated 4 degrees-of-freedom upper-limb exoskeleton, showing its compliant adaptation to the path considering the variance in healthy subjects' motor behavior.

Comparison of Admittance Control Dynamic Models for Transparent Free-Motion Human-Robot Interaction

This paper presents an experimental comparison of multiple admittance control dynamic models implemented on a five-degree-of-freedom arm exoskeleton. The performance of each model is evaluated for transparency, stability, and impact on point-to-point reaching. Although ideally admittance control would render a completely transparent environment for physical human-robot interaction (pHRI), in practice, there are trade-offs between transparency and stability-both of which can detrimentally impact natural arm movements. Here we test four admittance modes: 1) Low-Mass: low inertia with zero damping; 2) High-Mass: high inertia with zero damping; 3) Velocity-Damping: low inertia with damping; and 4) a novel Velocity-Error-Damping: low inertia with damping based on velocity error. A single subject completed two experiments: 1) 20 repetitions of a single reach-and-return, and 2) two repetitions of reach-and-return to 13 different targets. The results suggest that the proposed novel Velocity-Error-Damping model improves transparency the most, achieving a 70% average reduction of vibration power vs. Low-Mass, while also reducing user effort, with less impact on spatial/temporal accuracy than alternate modes. Results also indicate that different models have unique situational advantages so selecting between them may depend on the goals of the specific task (i.e., assessment, therapy, etc.). Future work should investigate merging approaches or transitioning between them in real-time.

Gait-Oriented Post-Stroke Rehabilitation Tasks Online Trajectory Generation for 1-DOF Hip Lower-Limb Exoskeleton

In the field of gait rehabilitation lower limb exoskeletons have received a lot of interest. An increasing number of them are revised to be adapted for post-stroke rehabilitation. These exoskeletons mostly work in complement of conventional physiotherapy in the subacute phase to practice gait training. For this gait training the reference trajectory generation is one of the main issues. This is why it usually consists in reproducing some averaged healthy patient's gait pattern. This paper's purpose is to display the online trajectory generation (OTG) algorithm developed to provide reference trajectories applied to gait-oriented tasks designed based on conventional physiotherapy. This OTG algorithm is made to reproduce trajectories similar to the ones a therapist would follow during the same tasks. In addition, experiments are presented in this paper to compare the trajectories generated with the OTG algorithm for two rehabilitation tasks with the trajectories followed by a therapist in the same conditions. During these experiments the OTG is implemented in a runtime system with a 500µs cycle time on a bench able to emulate late and early patients' interaction. These experiments results assess that the OTG can work at a 500µs cycle time to reproduce a similar trajectory as the one followed by the therapist during the two rehabilitation tasks implemented.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Introduction

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface.

Methods

To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model.

Results

Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed.

Discussion

The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients

Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.

Elbow-sideWINDER (Elbow-side Wearable INDustrial Ergonomic Robot): design, control, and validation of a novel elbow exoskeleton

Musculoskeletal Disorders associated with the elbow are one of the most common forms of work-related injuries. Exoskeletons have been proposed as an approach to reduce and ideally eliminate these injuries; however, exoskeletons introduce their own problems, especially discomfort due to joint misalignment. The Elbow-sideWINDER with its associated control strategy is a novel elbow exoskeleton to assist elbow flexion/extension during occupational tasks. This study describes the exoskeleton showing how this can minimize discomfort caused by joint misalignment, maximize assistive performance, and provide increased robustness and reliability in real worksites. The proposed medium-level control strategy can provide effective assistive torque using three control units as follows: an arm kinematics estimator, a load estimator, and a friction compensator. The combined hardware/software system of the Elbow-sideWINDER is tested in load-lifting tasks (2 and 7 kg). This experiment focuses on the reduction in the activation level of the biceps brachii and triceps brachii in both arms and the change in the range of motion of the elbow during the task. It is shown that using the Elbow-sideWINDER, the biceps brachii, responsible for the elbow flexion, was significantly less activated (up to 38.8% at 2 kg and 25.7% at 7 kg, on average for both arms). For the triceps brachii, the muscle activation was reduced by up to 37.0% at 2 kg and 35.1% at 7 kg, on average for both arms. When wearing the exoskeleton, the range of motion of the elbow was reduced by up to 13.0° during the task, but it was within a safe range and could be compensated for by other joints such as the waist or knees. There are extremely encouraging results that provide good indicators and important clues for future improvement of the Elbow-sideWINDER and its control strategy.

Home-based upper limb stroke rehabilitation mechatronics: challenges and opportunities

Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics. The present study undertakes a scoping review of designs for at-home upper limb stroke rehabilitation mechatronic devices to identify important design principles and areas for improvement. Online databases were used to identify papers published 2010-2021 describing novel rehabilitation device designs, from which 59 publications were selected describing 38 unique designs. The devices were categorized and listed according to their target anatomy, possible therapy tasks, structure, and features. Twenty-two devices targeted proximal (shoulder and elbow) anatomy, 13 targeted distal (wrist and hand) anatomy, and three targeted the whole arm and hand. Devices with a greater number of actuators in the design were more expensive, with a small number of devices using a mix of actuated and unactuated degrees of freedom to target more complex anatomy while reducing the cost. Twenty-six of the device designs did not specify their target users' function or impairment, nor did they specify a target therapy activity, task, or exercise. Twenty-three of the devices were capable of reaching tasks, 6 of which included grasping capabilities. Compliant structures were the most common approach of including safety features in the design. Only three devices were designed to detect compensation, or undesirable posture, during therapy activities. Six of the 38 device designs mention consulting stakeholders during the design process, only two of which consulted patients specifically. Without stakeholder involvement, these designs risk being disconnected from user needs and rehabilitation best practices. Devices that combine actuated and unactuated degrees of freedom allow a greater variety and complexity of tasks while not significantly increasing their cost. Future home-based upper limb stroke rehabilitation mechatronic designs should provide information on patient posture during task execution, design with specific patient capabilities and needs in mind, and clearly link the features of the design to users' needs.

Exoskeleton-Assisted Anthropomorphic Movement Training for the Upper Limb After Stroke: The EAMT Randomized Trial

BACKGROUND

Robot-assisted arm training is generally delivered in the robot-like manner of planar or mechanical 3-dimensional movements. It remains unclear whether integrating upper extremity (UE) natural coordinated patterns into a robotic exoskeleton can improve outcomes. The study aimed to compare conventional therapist-mediated training to the practice of human-like gross movements derived from 5 typical UE functional activities managed with exoskeletal assistance as needed for patients after stroke.

METHODS

In this randomized, single-blind, noninferiority trial, patients with moderate-to-severe UE motor impairment due to subacute stroke were randomly assigned (1:1) to receive 20 sessions of 45-minute exoskeleton-assisted anthropomorphic movement training or conventional therapy. Treatment allocation was masked from independent assessors, but not from patients or investigators. The primary outcome was the change in the Fugl-Meyer Assessment for Upper Extremity from baseline to 4 weeks against a prespecified noninferiority margin of 4 points. Superiority would be tested if noninferiority was demonstrated. Post hoc subgroup analyses of baseline characteristics were performed for the primary outcome.

RESULTS

Between June 2020 and August 2021, totally 80 inpatients (67 [83.8%] males; age, 51.9±9.9 years; days since stroke onset, 54.6±38.0) were enrolled, randomly assigned to the intervention, and included in the intention-to-treat analysis. The mean Fugl-Meyer Assessment for Upper Extremity change in exoskeleton-assisted anthropomorphic movement training (14.73 points; [95% CI, 11.43-18.02]) was higher than that of conventional therapy (9.90 points; [95% CI, 8.15-11.65]) at 4 weeks (adjusted difference, 4.51 points [95% CI, 1.13-7.90]). Moreover, post hoc analysis favored the patient subgroup (Fugl-Meyer Assessment for Upper Extremity score, 23-38 points) with moderately severe motor impairment.

CONCLUSIONS

Exoskeleton-assisted anthropomorphic movement training appears to be effective for patients with subacute stroke through repetitive practice of human-like movements. Although the results indicate a positive sign for exoskeleton-assisted anthropomorphic movement training, further investigations into the long-term effects and paradigm optimization are warranted.

REGISTRATION

URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2100044078.

Human Exteroception during Object Handling with an Upper Limb Exoskeleton

Upper limb exoskeletons may confer significant mechanical advantages across a range of tasks. The potential consequences of the exoskeleton upon the user's sensorimotor capacities however, remain poorly understood. The purpose of this study was to examine how the physical coupling of the user's arm to an upper limb exoskeleton influenced the perception of handheld objects. In the experimental protocol, participants were required to estimate the length of a series of bars held in their dominant right hand, in the absence of visual feedback. Their performance in conditions with an exoskeleton fixed to the forearm and upper arm was compared to conditions without the upper limb exoskeleton. Experiment 1 was designed to verify the effects of attaching an exoskeleton to the upper limb, with object handling limited to rotations of the wrist only. Experiment 2 was designed to verify the effects of the structure, and its mass, with combined movements of the wrist, elbow, and shoulder. Statistical analysis indicated that movements performed with the exoskeleton did not significantly affect perception of the handheld object in experiment 1 (BF₀₁ = 2.3) or experiment 2 (BF₀₁ = 4.3). These findings suggest that while the integration of an exoskeleton complexifies the architecture of the upper limb effector, this does not necessarily impede transmission of the mechanical information required for human exteroception.

Will Your Next Therapist Be a Robot?-A Review of the Advancements in Robotic Upper Extremity Rehabilitation

Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics.

A Trade-Off between Complexity and Interaction Quality for Upper Limb Exoskeleton Interfaces

Exoskeletons are among the most promising devices dedicated to assisting human movement during reeducation protocols and preventing musculoskeletal disorders at work. However, their potential is currently limited, partially because of a fundamental contradiction impacting their design. Indeed, increasing the interaction quality often requires the inclusion of passive degrees of freedom in the design of human-exoskeleton interfaces, which increases the exoskeleton's inertia and complexity. Thus, its control also becomes more complex, and unwanted interaction efforts can become important. In the present paper, we investigate the influence of two passive rotations in the forearm interface on sagittal plane reaching movements while keeping the arm interface unchanged (i.e., without passive degrees of freedom). Such a proposal represents a possible compromise between conflicting design constraints. The in-depth investigations carried out here in terms of interaction efforts, kinematics, electromyographic signals, and subjective feedback of participants all underscored the benefits of such a design. Therefore, the proposed compromise appears to be suitable for rehabilitation sessions, specific tasks at work, and future investigations into human movement using exoskeletons.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

Continuous mode adaptation for cable-driven rehabilitation robot using reinforcement learning

Continuous mode adaptation is very important and useful to satisfy the different user rehabilitation needs and improve human-robot interaction (HRI) performance for rehabilitation robots. Hence, we propose a reinforcement-learning-based optimal admittance control (RLOAC) strategy for a cable-driven rehabilitation robot (CDRR), which can realize continuous mode adaptation between passive and active working mode. To obviate the requirement of the knowledge of human and robot dynamics model, a reinforcement learning algorithm was employed to obtain the optimal admittance parameters by minimizing a cost function composed of trajectory error and human voluntary force. Secondly, the contribution weights of the cost function were modulated according to the human voluntary force, which enabled the CDRR to achieve continuous mode adaptation between passive and active working mode. Finally, simulation and experiments were conducted with 10 subjects to investigate the feasibility and effectiveness of the RLOAC strategy. The experimental results indicated that the desired performances could be obtained; further, the tracking error and energy per unit distance of the RLOAC strategy were notably lower than those of the traditional admittance control method. The RLOAC strategy is effective in improving the tracking accuracy and robot compliance. Based on its performance, we believe that the proposed RLOAC strategy has potential for use in rehabilitation robots.

Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review

Research on embodiment of objects external to the human body has revealed important information about how the human nervous system interacts with robotic lower limb exoskeletons. Typical robotic exoskeleton control approaches view the controllers as an external agent intending to move in coordination with the human. However, principles of embodiment suggest that the exoskeleton controller should ideally coordinate with the human such that the nervous system can adequately model the input-output dynamics of the exoskeleton controller. Measuring embodiment of exoskeletons should be a necessary step in the exoskeleton development and prototyping process. Researchers need to establish high fidelity quantitative measures of embodiment, rather than relying on current qualitative survey measures. Mobile brain imaging techniques, such as high-density electroencephalography, is likely to provide a deeper understanding of embodiment during human-machine interactions and advance exoskeleton research and development. In this review we show why future exoskeleton research should include quantitative measures of embodiment as a metric of success.

Comparative analysis of inverse kinematics methodologies to improve the controllability of rehabilitative robotic devices

The solution of the inverse kinematics problem in multi-degrees of freedom robots has been tackled, through the last three decades, by several different approaches including analytical, geometrical, differential and numerical methods. All these techniques present their own advantages and drawbacks. However, a guideline on which approach is better to follow, depending on the kind of task to perform and the type of robotic device used, is still missing. In this work, a quantitative comparative analysis of three different inverse kinematics methodologies for the control of rehabilitative robotic devices is proposed, with aim of devising best practices and guidelines for the selection of the most suitable approach. The analyzed methodologies are implemented and numerically tested on two actual devices, specifically an upper-limb exoskeleton and an upper-limb prosthetic arm.

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.

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

An Adaptive Integral Terminal Sliding Mode Controller to Track the Human Upper Limb during Front Crawl Swimming

AbstractInjuries are inevitable during swimming. The main goal for athletes especially competitive ones and coaches is to do the most mechanically effective motion patterns. In this case, biomechanical assessments could be beneficial in the management and prevention of injuries and pain in swimmers' vulnerable joints. As upper limb in swimming causes the highest propulsive force, the arm is exposed to more injuries. A skeletal model with 5 degrees of freedom is developed to simulate the swimmer's arm during front crawl swimming. This model includes shoulder and elbow joints with all of their degrees of freedom. An adaptive integral sliding mode (AITSM) controller is employed to track the desired joint trajectories during swimming. This controller can converge the tracking errors to zero in finite time. For tuning the controller gains regardless of the upper bounds of the system uncertainties, an adaptive controller is applied. Results demonstrate the performance of the AITSM strategy in tracking the desired trajectory of an underwater arm model during swimming. During the down sweep to catch phase, arm movements cause more stress in the shoulder than elbow. The applied moment at the shoulder is almost triple of elbow's moment. Therefore, the most vulnerable joint is the shoulder. By considering shoulder strength, the injury risk is predicted about 10% for the considered swimmer.

PARS, low-cost portable rehabilitation system for upper arm

This study introduces a compact low-cost single degree of freedom end-effector type upper arm rehabilitation system (PARS) along with its hardware and software elements. Proposed system is also suitable to be used in conjunction with a gaming environment. Throughout the study structural setup of the system was explained in detail along with its electronics, control system and gaming software. Introduced virtual gaming interface supports various game levels with different difficulties generated via interaction type control algorithms. Having simple structural design constructed by using basic available components, proposed system can be easily manufactured and utilized in physical rehabilitation procedures by using supplied open source codes. Introduced systems compactness and user friendly interface also allow its usage for individual home therapies for remote rehabilitation treatment procedures.

Ergodic Shared Control: Closing the Loop on pHRI Based on Information Encoded in Motion

Advances in exoskeletons and robot arms have given us increasing opportunities for providing physical support and meaningful feedback in training and rehabilitation settings. However, the chosen control strategies must support motor learning and provide mathematical task definitions that are actionable for the actuation. Typical robot control architectures rely on measuring error from a reference trajectory. In physical human-robot interaction, this leads to low engagement, invariant practice, and few errors, which are not conducive to motor learning. A reliance on reference trajectories means that the task definition is both over-specified—requiring specific timings not critical to task success—and lacking information about normal variability. In this article, we examine a way to define tasks and close the loop using an ergodic measure that quantifies how much information about a task is encoded in the human-robot motion. This measure can capture the natural variability that exists in typical human motion—enabling therapy based on scientific principles of motor learning. We implement an ergodic hybrid shared controller(HSC) on a robotic arm as well as an error-based controller—virtual fixtures—in a timed drawing task. In a study of 24 participants, we compare ergodic HSC with virtual fixtures and find that ergodic HSC leads to improved training outcomes.

Pneumatic exoskeletons for orthopedic rehabilitation of the upper arm—An overview

An exoskeleton is an external cast which can be used to maneuver, protect or even provide a greater magnitude of strength to the upper limb allowing for heightened efficiency and performance. The limb may be weakened due to so many diseases or reasons such as paralysis, stroke, muscular atrophy, and different kinds of injuries. Numerous designs and manufactured prototypes of pneumatic exoskeletons have been reviewed—their pros and cons weighed against each other. This paper covers the fundamental concepts of various prototypes that have been designed and developed over the years. The best mechanism has been highlighted, although the design of a fully efficient exoskeleton comes with its set of drawbacks. Types of energy used for the driver unit, types of actuators, materials used, design concepts, and overall weight and manufacturing cost of every prototype has been contrasted with the others to conclude what an ideal exoskeleton for rehabilitation purposes must look like and the principles and features of the prototype must be decided upon by keeping aesthetics and ergonomics in mind.

An Affordable Upper-Limb Exoskeleton Concept for Rehabilitation Applications

In recent decades, many researchers have focused on the design and development of exoskeletons. Several strategies have been proposed to develop increasingly more efficient and biomimetic mechanisms. However, existing exoskeletons tend to be expensive and only available for a few people. This paper introduces a new gravity-balanced upper-limb exoskeleton suited for rehabilitation applications and designed with the main objective of reducing the cost of the components and materials. Regarding mechanics, the proposed design significantly reduces the motor torque requirements, because a high cost is usually associated with high-torque actuation. Regarding the electronics, we aim to exploit the microprocessor peripherals to obtain parallel and real-time execution of communication and control tasks without relying on expensive RTOSs. Regarding sensing, we avoid the use of expensive force sensors. Advanced control and rehabilitation features are implemented, and an intuitive user interface is developed. To experimentally validate the functionality of the proposed exoskeleton, a rehabilitation exercise in the form of a pick-and-place task is considered. Experimentally, peak torques are reduced by 89% for the shoulder and by 84% for the elbow.

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

BACKGROUND

Appropriate training modalities for post-stroke upper-limb rehabilitation are key features for effective recovery after the acute event. This study presents a cooperative control framework that promotes compliant motion and implements a variety of high-level rehabilitation modalities with a unified low-level explicit impedance control law. The core idea is that we can change the haptic behavior perceived by a human when interacting with the rehabilitation robot by tuning three impedance control parameters.

METHODS

The presented control law is based on an impedance controller with direct torque measurement, provided with positive-feedback compensation terms for disturbances rejection and gravity compensation. We developed an elbow flexion-extension experimental setup as a platform to validate the performance of the proposed controller to promote the desired high-level behavior. The controller was first characterized through experimental trials regarding joint transparency, torque, and impedance tracking accuracy. Then, to validate if the controller could effectively render different physical human-robot interaction according to the selected rehabilitation modalities, we conducted tests on 14 healthy volunteers and measured their muscular voluntary effort through surface electromyography (sEMG). The experiments consisted of one degree-of-freedom elbow flexion/extension movements, executed under six high-level modalities, characterized by different levels of (i) corrective assistance, (ii) weight counterbalance assistance, and (iii) resistance.

RESULTS

The unified controller demonstrated suitability to promote good transparency and render both compliant and stiff behavior at the joint. We demonstrated through electromyographic monitoring that a proper combination of stiffness, damping, and weight assistance could induce different user participation levels, render different physical human-robot interaction, and potentially promote different rehabilitation training modalities.

CONCLUSION

We proved that the proposed control framework could render a wide variety of physical human-robot interaction, helping the user to accomplish the task while exploiting physiological muscular activation patterns. The reported results confirmed that the control scheme could induce different levels of the subject's participation, potentially applicable to the clinical practice to adapt the rehabilitation treatment to the subject's progress. Further investigation is needed to validate the presented approach to neurological patients.

Human Weight Compensation With a Backdrivable Upper-Limb Exoskeleton: Identification and Control

Active exoskeletons are promising devices for improving rehabilitation procedures in patients and preventing musculoskeletal disorders in workers. In particular, exoskeletons implementing human limb’s weight support are interesting to restore some mobility in patients with muscle weakness and help in occupational load carrying tasks. The present study aims at improving weight support of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments, for design and validation purposes, are conducted on a total of 65 participants who performed posture maintenance and elbow flexion/extension movements. The introduction of joint misalignments in the weight support model significantly reduced the model errors, in terms of weight estimation, and enhanced the estimation reliability. The introduced control architecture reduced model tracking errors regardless of the condition. Weight support significantly decreased the activity of antigravity muscles, as expected, but increased the activity of elbow extensors because gravity is usually exploited by humans to accelerate a limb downwards. These findings suggest that an adaptive weight support controller could be envisioned to further minimize human effort in certain applications.

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons

Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) “high-level” training modalities, 2) “low-level” control strategies, and 3) “hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.

A Hybrid Arm-Hand Rehabilitation Robot With EMG-Based Admittance Controller

Reach-and-grasp is one of the most fundamental activities in daily life, while few rehabilitation robots provide integrated and active training of the arm and hand for patients after stroke to improve their mobility. In this study, a novel hybrid arm-hand rehabilitation robot (HAHRR) was built for the reach-and-grasp task. This hybrid structure consisted of a cable-driven module for three-dimensional arm motion and an exoskeleton for hand motion, which enabled assistance of the arm and hand simultaneously. To implement active compliance control, an EMG-based admittance controller was applied to the HAHRR. Experimental results showed that the HAHRR with the EMG-based admittance controller could not only assist the subject in fulfilling the reach-and-grasp task, but also generate smoother trajectories compared with the force-sensing-based admittance controller. These findings also suggested that the proposed approach might be applicable to post-stroke arm-hand rehabilitation training.

Challenges and solutions for application and wider adoption of wearable robots

The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human-robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots.

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow-wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness.

Neglected physical human-robot interaction may explain variable outcomes in gait neurorehabilitation research

[Figure: see text].

Robotic Home-Based Rehabilitation Systems Design: From a Literature Review to a Conceptual Framework for Community-Based Remote Therapy During COVID-19 Pandemic

During the COVID-19 pandemic, the higher susceptibility of post-stroke patients to infection calls for extra safety precautions. Despite the imposed restrictions, early neurorehabilitation cannot be postponed due to its paramount importance for improving motor and functional recovery chances. Utilizing accessible state-of-the-art technologies, home-based rehabilitation devices are proposed as a sustainable solution in the current crisis. In this paper, a comprehensive review on developed home-based rehabilitation technologies of the last 10 years (2011-2020), categorizing them into upper and lower limb devices and considering both commercialized and state-of-the-art realms. Mechatronic, control, and software aspects of the system are discussed to provide a classified roadmap for home-based systems development. Subsequently, a conceptual framework on the development of smart and intelligent community-based home rehabilitation systems based on novel mechatronic technologies is proposed. In this framework, each rehabilitation device acts as an agent in the network, using the internet of things (IoT) technologies, which facilitates learning from the recorded data of the other agents, as well as the tele-supervision of the treatment by an expert. The presented design paradigm based on the above-mentioned leading technologies could lead to the development of promising home rehabilitation systems, which encourage stroke survivors to engage in under-supervised or unsupervised therapeutic activities.

Cooperative Control of Manipulator and Human Operator for Direct Teaching

A cooperative control of a manipulator and a human operator has been proposed for an efficient direct teaching operation in this research. The main goal is making the operator be convenient and relaxed when he is operating the manipulator for a direct teaching. The proposed control strategy has two layers: In the first layer, human motion estimator (HME) has been designed to estimate a human intention. The recursive least square method has been utilized for the HME to simultaneously estimate the interaction force and the human arm admittance model. In the second layer, human motion reactor has been designed to keep the human motion intention precisely by a proportional derivative and gravity compensation in real time. Real experiments with a 3-degree of freedom robotic manipulator guided by the human operator have been conducted to draw a diamond shape on a panel. The experimental results demonstrate the effectiveness of the proposed cooperative control strategy.

Low cost exoskeleton manipulator using bidirectional triboelectric sensors enhanced multiple degree of freedom sensory system

Rapid developments of robotics and virtual reality technology are raising the requirements of more advanced human-machine interfaces for achieving efficient parallel control. Exoskeleton as an assistive wearable device, usually requires a huge cost and complex data processing to track the multi-dimensional human motions. Alternatively, we propose a triboelectric bi-directional sensor as a universal and cost-effective solution to a customized exoskeleton for monitoring all of the movable joints of the human upper limbs with low power consumption. The corresponding movements, including two DOF rotations of the shoulder, twisting of the wrist, and the bending motions, are detected and utilized for controlling the virtual character and the robotic arm in real-time. Owing to the structural consistency between the exoskeleton and the human body, further kinetic analysis offers additional physical parameters without introducing other types of sensors. This exoskeleton sensory system shows a great potential of being an economic and advanced human-machine interface for supporting the manipulation in both real and virtual worlds, including robotic automation, healthcare, and training applications.

Adaptive Algorithms as Control Strategies of Smart Upper Limb Orthosis: A Protocol for a Systematic Scoping Review

Introduction: Adaptive algorithms for controlling orthosis emerged to overcome significant problems with automatic biosignal classification and personalized rehabilitation. Smart orthoses are evolving fast and need a better human-machine interaction performance since biosignals, feedback, and motor control dynamically change and must be adaptive. This manuscript outlines a scoping review protocol to systematically review the smart upper limb (UL) orthoses based on adaptive algorithms and feasibility tests. Materials and Methods: This protocol was developed based on the York framework. A field-specific structure was defined to achieve each phase. Eleven scientific databases (PubMed, Web of Science, SciELO, Koreamed, Jstage, AMED, CENTRAL, PEDro, IEEE, Scopus, and Arxiv) and five patent databases (Patentscope, Patentlens, Google Patents, Kripis, J-platpat) were searched. The developed framework will extract data (i.e., orthosis description, adaptive algorithms, tools used in the usability test, and benefits to the general population) from the selected studies using a rigorous approach. Data will be described quantitatively using frequency and trend analysis methods. Heterogeneity between the included studies will be assessed using the Chi-test and I-statistic. The risk of bias will be summarized using the latest Prediction Model Study Risk of Bias Assessment Tool. Discussion: This review will identify, map, and synthesize the advances about the description of adaptive algorithms for control strategies of smart UL orthosis using data extracted from patents and articles.

Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept

The limits of space travel are continuously evolving, and this creates increasingly extreme challenges for the crew’s health that must be addressed by the scientific community. Long-term exposure to micro-gravity, during orbital flights, contributes to muscle strength degradation and increases bone density loss. In recent years, several exercise devices have been developed to counteract the negative health effects of zero-gravity on astronauts. However, the relatively large size of these devices, the need for a dedicated space and the exercise time-frame for each astronaut, does not make these devices the best choice for future long range exploration missions. This paper presents a quasi-passive exosuit to provide muscle training using a small, portable, proprioceptive device. The exosuit promotes continuous exercise, by resisting the user’s motion, during routine all-day activity. This study assesses the effectiveness of the resistive exosuit by evaluating its effects on muscular endurance during a terrestrial walking task. The experimental assessment on biceps femoris and vastus lateralis, shows a mean increase in muscular activation of about 97.8% during five repetitions of 3 min walking task at 3 km/h. The power frequency analysis shows an increase in muscular fatigue with a reduction of EMG median frequency of about 15.4% for the studied muscles.

Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions?

Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system's resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.

A Cable-Driven Three-DOF Wrist Rehabilitation Exoskeleton With Improved Performance

This paper developed a cable-driven three-degree-of-freedom (DOF) wrist rehabilitation exoskeleton actuated by the distributed active semi-active (DASA) system. Compared with the conventional cable-driven robots, the workspace of this robot is increased greatly by adding the rotating compensation mechanism and by optimizing the distribution of the cable attachment points. In the meanwhile, the efficiency of the cable tension is improved, and the parasitic force (the force acting on the joint along the limb) is reduced. Besides, in order to reduce the effects of compliant elements (e.g., cables or Bowden cables) between the actuators and output, and to improve the force bandwidth, we designed the DASA system composed of one geared DC motor and four magnetorheological (MR) clutches, which has low output inertia. A fast unbinding strategy is presented to ensure safety in abnormal conditions. A passive training algorithm and an assist-as-needed (AAN) algorithm were implemented to control the exoskeleton. Several experiments were conducted on both healthy and impaired subjects to test the performance and effectiveness of the proposed system for rehabilitation. The results show that the system can meet the needs of rehabilitation training for workspace and force-feedback, and provide efficient active and passive training.