Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments

The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.

Wearable Robots: An Original Mechatronic Design of a Hand Exoskeleton for Assistive and Rehabilitative Purposes

Robotic devices are being employed in more and more sectors to enhance, streamline, and augment the outcomes of a wide variety of human activities. Wearable robots arise indeed as of-vital-importance tools for telerehabilitation or home assistance targeting people affected by motor disabilities. In particular, the field of “Robotics for Medicine and Healthcare” is attracting growing interest. The development of such devices is a primarily addressed topic since the increasing number of people in need of rehabilitation or assistive therapies (due to population aging) growingly weighs on the healthcare systems of the nation. Besides, the necessity to move to clinics represents an additional logistic burden for patients and their families. Among the various body parts, the hand is specially investigated since it most ensures the independence of an individual, and thus, the restoration of its dexterity is considered a high priority. In this study, the authors present the development of a fully wearable, portable, and tailor-made hand exoskeleton designed for both home assistance and telerehabilitation. Its purpose is either to assist patients during activities of daily living by running a real-time intention detection algorithm or to be used for remotely supervised or unsupervised rehabilitation sessions by performing exercises preset by therapists. Throughout the mechatronic design process, special attention has been paid to the complete wearability and comfort of the system to produce a user-friendly device capable of assisting people in their daily life or enabling recorded home rehabilitation sessions allowing the therapist to monitor the state evolution of the patient. Such a hand exoskeleton system has been designed, manufactured, and preliminarily tested on a subject affected by spinal muscular atrophy, and some results are reported at the end of the article.

Nonlinear time delay estimation based model reference adaptive impedance control for an upper-limb human-robot interaction

A nonlinear Time Delay Estimation (TDE) based model reference adaptive impedance controller was developed for Tarbiat Modares University Upper Limbs Rehabilitation Robot (TUERR). The proposed controller uses a stable reference impedance model, which produces desired dynamic relationship between applied force and position error for the robot End-effector to track the desired trajectory. TDE based model reference adaptive controller estimates unknown system dynamics and uncertainties, and the adaption law modifies the controller gains. Using a Lyapunov function was shown trajectory tracking errors in the overall system are bounded. In addition, a performance-based velocity profile proposed to modify the pace of trajectory planning considering the deviation from the desired path. Finally, the performance of the presented controller and rehabilitation process is experimentally investigated for TUERR.

Combination of Stem Cells and Rehabilitation Therapies for Ischemic Stroke

Stem cell transplantation with rehabilitation therapy presents an effective stroke treatment. Here, we discuss current breakthroughs in stem cell research along with rehabilitation strategies that may have a synergistic outcome when combined together after stroke. Indeed, stem cell transplantation offers a promising new approach and may add to current rehabilitation therapies. By reviewing the pathophysiology of stroke and the mechanisms by which stem cells and rehabilitation attenuate this inflammatory process, we hypothesize that a combined therapy will provide better functional outcomes for patients. Using current preclinical data, we explore the prominent types of stem cells, the existing theories for stem cell repair, rehabilitation treatments inside the brain, rehabilitation modalities outside the brain, and evidence pertaining to the benefits of combined therapy. In this review article, we assess the advantages and disadvantages of using stem cell transplantation with rehabilitation to mitigate the devastating effects of stroke.

Iterative learning-based path control for robot-assisted upper-limb rehabilitation

In robot-assisted rehabilitation, the performance of robotic assistance is dependent on the human user’s dynamics, which are subject to uncertainties. In order to enhance the rehabilitation performance and in particular to provide a constant level of assistance, we separate the task space into two subspaces where a combined scheme of adaptive impedance control and trajectory learning is developed. Human movement speed can vary from person to person and it cannot be predefined for the robot. Therefore, in the direction of human movement, an iterative trajectory learning approach is developed to update the robot reference according to human movement and to achieve the desired interaction force between the robot and the human user. In the direction normal to the task trajectory, human’s unintentional force may deteriorate the trajectory tracking performance. Therefore, an impedance adaptation method is utilized to compensate for unknown human force and prevent the human user drifting away from the updated robot reference trajectory. The proposed scheme was tested in experiments that emulated three upper-limb rehabilitation modes: zero interaction force, assistive and resistive. Experimental results showed that the desired assistance level could be achieved, despite uncertain human dynamics.

Prospects for intelligent rehabilitation techniques to treat motor dysfunction

More than half of stroke patients live with different levels of motor dysfunction after receiving routine rehabilitation treatments. Therefore, new rehabilitation technologies are urgently needed as auxiliary treatments for motor rehabilitation. Based on routine rehabilitation treatments, a new intelligent rehabilitation platform has been developed for accurate evaluation of function and rehabilitation training. The emerging intelligent rehabilitation techniques can promote the development of motor function rehabilitation in terms of informatization, standardization, and intelligence. Traditional assessment methods are mostly subjective, depending on the experience and expertise of clinicians, and lack standardization and precision. It is therefore difficult to track functional changes during the rehabilitation process. Emerging intelligent rehabilitation techniques provide objective and accurate functional assessment for stroke patients that can promote improvement of clinical guidance for treatment. Artificial intelligence and neural networks play a critical role in intelligent rehabilitation. Multiple novel techniques, such as brain-computer interfaces, virtual reality, neural circuit-magnetic stimulation, and robot-assisted therapy, have been widely used in the clinic. This review summarizes the emerging intelligent rehabilitation techniques for the evaluation and treatment of motor dysfunction caused by nervous system diseases.

Changes in arm kinematics of chronic stroke individuals following "Assist-As-Asked" robot-assisted training in virtual and physical environments: A proof-of-concept study

Introduction

In this proof-of-concept study, we introduce a custom-developed robot-assisted training protocol, named “Assist-As-Asked”, aiming at improving arm function of chronic stroke subjects with moderate-to-severe upper extremity motor impairment. The study goals were to investigate the feasibility and potential adverse effects of this training protocol in both physical and virtual environments.

Methods

A sample of convenience of four chronic stroke subjects participated in 10 half-hour sessions. The task was to practice reaching six targets in both virtual and physical environments. The robotic arm used the Assist-As-Asked paradigm in which it helped subjects to complete movements when asked by them. Changes in the kinematics of the reaching movements and the participants’ perception of the reaching practice in both environments were the outcome measures of interest.

Results

Subjects improved their reaching performance and none of them reported any adverse events. There were no differences between the two environments in terms of kinematic measures even though subjects had different opinions about the environment preference.

Conclusions

Using the Assist-As-Asked protocol in moderate-to-severe chronic stroke survivors is feasible and it can be used with both physical and virtual environments with no evidence of one of them to be superior to the other based on users’ perspectives and movement kinematics.

Patients' perspective and usability of innovation technology in a new rehabilitation pathway: An exploratory study in patients with multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease of the central nervous system, which causes sensori-motor and cognitive disabilities, as well as neuropsychiatric abnormalities. Technological innovations could offer a valuable way to improve neurorehabilitation outcomes. Aim of the study is to assess the feasibility and usability of new rehabilitation technologies as perceived by patients suffering from MS.

MATERIALS AND METHODS

MS inpatients attending the Robotic and Behavioral Neurorehabilitation Service of the IRCCS Centro Neurolesi Bonino Pulejo (Messina, Italy) from February 2017 to April 2019, were enrolled in this exploratory study. The patients were submitted to a personalized rehabilitation treatment using robotics (such as Lokomat, Geosystem, Ekso, Armeo) and virtual reality (i.e. BTS-Nirvana, CAREN, VRRS), following a dedicated innovative pathway.

RESULTS

All patients completed the study. Significant pre-post-treatment differences were found in the perception of patients' quality of life, regarding both physical and mental items (p<0,001), as well as in the achievement of the therapeutic goal. Finally, we observed that patients declared a high usability of the robotic devices, and that rehabilitation with the new devices was well tolerated.

CONCLUSIONS

our results support the idea that neurorehabilitation using innovation technologies can be useful for the commitment and motivation during the rehabilitation process, with possible positive effects on the functional and psychological outcomes of patients with MS.

Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study

Background

Brain injury survivors often present upper-limb motor impairment affecting the execution of functional activities such as reaching. A currently active research line seeking to maximize upper-limb motor recovery after a brain injury, deals with the combined use of functional electrical stimulation (FES) and mechanical supporting devices, in what has been previously termed hybrid robotic systems. This study evaluates from the technical and clinical perspectives the usability of an integrated hybrid robotic system for the rehabilitation of upper-limb reaching movements after a brain lesion affecting the motor function.

Methods

The presented system is comprised of four main components. The hybrid assistance is given by a passive exoskeleton to support the arm weight against gravity and a functional electrical stimulation device to assist the execution of the reaching task. The feedback error learning (FEL) controller was implemented to adjust the intensity of the electrical stimuli delivered on target muscles according to the performance of the users. This control strategy is based on a proportional-integral-derivative feedback controller and an artificial neural network as the feedforward controller. Two experiments were carried out in this evaluation. First, the technical viability and the performance of the implemented FEL controller was evaluated in healthy subjects (N = 12). Second, a small cohort of patients with a brain injury (N = 4) participated in two experimental session to evaluate the system performance. Also, the overall satisfaction and emotional response of the users after they used the system was assessed.

Results

In the experiment with healthy subjects, a significant reduction of the tracking error was found during the execution of reaching movements. In the experiment with patients, a decreasing trend of the error trajectory was found together with an increasing trend in the task performance as the movement was repeated. Brain injury patients expressed a great acceptance in using the system as a rehabilitation tool.

Conclusions

The study demonstrates the technical feasibility of using the hybrid robotic system for reaching rehabilitation. Patients’ reports on the received intervention reveal a great satisfaction and acceptance of the hybrid robotic system.

Trial registration

Retrospective trial registration in ISRCTN Register with study ID ISRCTN12843006.