Training modalities in robot-mediated upper limb rehabilitation in stroke : A framework for classification based on a systematic review

© 2014 Basteris et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The work described in this manuscript was partially funded by the European project ‘SCRIPT’ Grant agreement no: 288698 (http://scriptproject.eu). SN has been hosted at University of Hertfordshire in a short-term scientific mission funded by the COST Action TD1006 European Network on Robotics for NeuroRehabilitationRobot-mediated post-stroke therapy for the upper-extremity dates back to the 1990s. Since then, a number of robotic devices have become commercially available. There is clear evidence that robotic interventions improve upper limb motor scores and strength, but these improvements are often not transferred to performance of activities of daily living. We wish to better understand why. Our systematic review of 74 papers focuses on the targeted stage of recovery, the part of the limb trained, the different modalities used, and the effectiveness of each. The review shows that most of the studies so far focus on training of the proximal arm for chronic stroke patients. About the training modalities, studies typically refer to active, active-assisted and passive interaction. Robot-therapy in active assisted mode was associated with consistent improvements in arm function. More specifically, the use of HRI features stressing active contribution by the patient, such as EMG-modulated forces or a pushing force in combination with spring-damper guidance, may be beneficial.Our work also highlights that current literature frequently lacks information regarding the mechanism about the physical human-robot interaction (HRI). It is often unclear how the different modalities are implemented by different research groups (using different robots and platforms). In order to have a better and more reliable evidence of usefulness for these technologies, it is recommended that the HRI is better described and documented so that work of various teams can be considered in the same group and categories, allowing to infer for more suitable approaches. We propose a framework for categorisation of HRI modalities and features that will allow comparing their therapeutic benefits.Peer reviewedFinal Published versio

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration

Rehabilitative devices for a top-down approach

In recent years, neurorehabilitation has moved from a "bottom-up" to a "top down" approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new "top-down" approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to "Bottom up" approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system. Areas covered: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach. Expert commentary: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies

Technology-supported training of arm-hand skills in stroke

Impaired arm-hand performance is a serious consequence of stroke that is associated with reduced self-efficacy and poor quality of life. Task-oriented arm training is a therapy approach that is known to improve skilled arm-hand performance, even in chronic stages after stroke. At the start of this project, little knowledge had been consolidated regarding taskoriented arm training characteristics, especially in the field of technology-supported rehabilitation. The feasibility and effects of technology-supported client-centred task-oriented training on skilled arm-hand performance had not been investigated but to a very limited degree. Reviewing literature on rehabilitation and motor learning in stroke led to the identification of therapy oriented criteria for rehabilitation technology aiming to influence skilled arm-hand performance (chapter 2). Most rehabilitation systems reported in literature to date are robotic systems that are aimed at providing an engaging exercise environment and feedback on motor performance. Both, feedback and engaging exercises are important for motivating patients to perform a high number of exercise repetitions and prolonged training, which are important factors for motor learning. The review also found that current rehabilitation technology is focussed mainly on providing treatment at a function level, thereby improving joint range of motion, muscle strength and parameters such as movement speed and smoothness of movement during analytical movements. However, related research has found no effects of robot-supported training at the activity level. The review concluded that a challenge exists for upper extremity rehabilitation technology in stroke patients to also provide more patienttailored task-oriented arm-hand training in natural environments to support the learning of skilled arm-hand performance. Besides mapping the strengths of different technological solutions, the use of outcome measures and training protocols needs to become more standardized across similar interventions, in order to help determine which training solutions are most suitable for specific patient categories. Chapter 4 contributes towards such a standardization of outcome measurement. A concept is introduced which may guide the clinician/researcher to choose outcome measures for evaluating specific and generalized training effects. As an initial operationalization of this concept, 28 test batteries that have been used in 16 task-oriented training interventions were rated as to whether measurement components were measured by the test. Future research is suggested that elaborates the concept with information on the relative weighing of components in each test, with more test batteries (which may lead to additional components) and by adding more test properties into the concept (e.g. psychometric properties of the tests, possible floor- or ceiling effects). Task-oriented training is one of the training approaches that has been shown to be beneficial for skilled arm-hand performance after stroke. Important mechanisms for motor learning that are identified are patient motivation for such training, and the learning of efficient goaloriented movement strategies and task-specific problem solving. In this thesis we operationalize task-oriented training in terms of 15 components (chapter 3). A systematic review that included 16 randomized controlled trials using task-oriented training in stroke patients, evaluated the effects of these training components on skilled arm-hand performance. The number of training components used in an intervention aimed at improving arm-hand performance after stroke was not associated with the post-treatment effect size. Distributed practice and feedback were associated with the largest post-intervention effect sizes. Random practice and use of clear functional training goals were associated with the largest follow-up effect sizes. It may be that training components that optimize the storage of learned motor performance in the long-term memory are associated with larger treatment effects. Unfortunately, feedback, random practice and distributed practice were reported in very few of the included randomized controlled trials (in only 6,3 and 1 out of the 17 studies respectively). Client-centred training, i.e. training on exercises that support goals that are selected by the patients themselves, improves patient motivation for training. Motivation in turn has proven to positively influence motor learning in stroke patients, as attention during training is heightened and storage of information in the long-term memory improves. Chapter 5 reports on an interview of 40 stroke patients, investigating into training preferences. A list of 46 skills, ranked according to descending training preference scores, was provided that can be used for implementation of exercises in rehabilitation technology, in order for technologysupported training to be client-centred. Chapter 6 introduces T-TOAT, a technology supported task-oriented arm training method that was developed together with colleagues at Adelante (Hoensbroek, NL). T-TOAT enables the implementation of exercises that support task-oriented training in rehabilitation technology. The training method is applicable for different technological systems, e.g. robot and sensor systems, or in combination with functional electrical stimulation, etc. To enable the use of TTOAT for training with the Haptic Master Robot (MOOG-FCS, NL), special software named Haptic TOAT was developed in Adelante together with colleagues at the Centre of Technology in Care of Zuyd University (chapter 6). The software enables the recording of the patient’s movement trajectories, given task constraints and patient possibilities, using the Haptic Master as a recording device. A purpose-made gimbal was attached to the endeffector, leaving the hand free for the use and manipulating objects. The recorded movement can be replayed in a passive mode or in an active mode (active, active-assisted or activeresisted). Haptic feedback is provided when the patient deviates from the recorded movement trajectory, as the patient receives the sensation of bouncing into a wall, as well as feeling a spring that pulls him/her back to the recorded path. The diameter of the tunnel around the recorded trajectory (distance to the wall), and the spring force can be adjusted for each patient. An ongoing clinical trial in which chronic stroke patients train with Haptic-TOAT examines whether Haptic Master provides additional value compared to supporting the same exercises by video-instruction only. Together with Philips Research Europe (Eindhoven,Aachen), the T-TOAT method has been implemented in a sensor based prototype, called Philips Stroke Rehabilitation Exerciser. This system included movement tracking sensors and an exercise board interacting with real life objects. A very strong feature of the system is that feedback is provided to patients (real-time and after exercise performance), based on a comparison of the patient’s exercise performance to individual targets set by the therapist. Chapter 7 reports on a clinical trial investigating arm-hand treatment outcome and patient motivation for technology-supported task-oriented training in chronic stroke patients. It was found that 8 weeks of T-TOAT training improved arm-hand performance in chronic stroke patients significantly on Fugl-Meyer, Action Research Arm Test, and Motor Activity Log. An improvement was found in health-related quality of life. Training effects lasted at least 6 months post-training. Participants reported feeling intrinsically motivated and competent to use the system. The results of this study showed that T-TOAT is feasible. Despite the small number of stroke patients tested (n=9), significant and clinically relevant improvements in skilled arm-hand performance were found. In conclusion, this thesis has made several contributions. It motivated the need for clientcentred task-oriented training, which it has operationalized in terms of 15 components. Four of these 15 components were identified as most beneficial for the patient. A prioritized inventory of arm-hand training preferences of stroke patients was compiled by means of an interview study of 40 subacute and chronic stroke patients. T-TOAT, a method for technology-supported, client-centred, task-oriented training, was conceived and implemented in two target technologies (Haptic Master and Philips Stroke Rehabilitation Exerciser). Its feasibility was demonstrated in a clinical trial showing substantial and durable benefits for the stroke patients. Finally, the thesis contributes towards the standardization of outcome measures which is necessary for charting progress and guiding future developments of technology-supported stroke rehabilitation. Methodological considerations were discussed and several suggestions for future research were presented. The variety of treatment approaches and the various ways of support and challenge that are offered by existing rehabilitation technologies hold a large potential for offering a variety of extra training opportunities to stroke patients that may improve their arm-hand performance. Such solutions will be of increasing importance, to alleviate therapists and reduce economic pressure on the health care system, as the stroke incidence is increasing rapidly over the coming decades

Robotics in health care: Perspectives of robot-aided interventions in clinical practice for rehabilitation of upper limbs

This article belongs to the Special Issue Rehabilitation Robotics: Recent Advancements and New Perspectives about Training and Assessment of Sensorimotor Functions.Robot-aided systems to support the physical rehabilitation of individuals with neurological impairment is one of the fields that has been widely developed in the last few decades. However, the adoption of these systems in clinical practice remains limited. In order to better understanding the causes of this limitation, a systematic review of robot-based systems focused on upper extremity rehabilitation is presented in this paper. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the type of device, the data analysis capability, the therapy method, the human–robot interaction, the safety strategies, and the focus of treatment. As a conclusion, self-adaptation for personalizing the treatments, safeguarding and enhancing of patient–robot interaction towards training essential factors of movement generation into the same paradigm, or the use of lifelike environments in fully-immersive virtual reality for increasing the assimilation of motor gains could be relevant factors to develop more accepted robot-aided systems in clinical practice.This work was supported in part by the Spanish Ministry of Economy and Competitiveness via the ROBOESPASproject (DPI2017-87562-C2-1-R) and in part by the RoboCity2030-DIH-CMMadrid Robotics Digital Innovation Hub ("Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, Fase IV"; S2018/NMT-4331), which is funded by the Programas de Actividades I+DComunidad de Madrid and cofunded by the Structural Funds of the EU

A scoping review of scientific concepts concerning motor recovery after stroke as employed in clinical trials

The scientific literature on poststroke rehabilitation is remarkably vast. Over the last decades, dozens of rehabilitation approaches have been investigated. However, sometimes it is challenging to trace new experimental interventions back to some of the known models of motor control and sensorimotor learning. This scoping review aimed to investigate motor control models' diffusion among the literature on motor recovery after stroke. We performed a literature search on Medline, Cochrane, Web of Science, Embase, and Scopus databases. The last search was conducted in September 2023. This scoping review included full-text articles published in English in peer-reviewed journals that provided rehabilitation interventions based on motor control or motor learning frameworks for at least one individual with stroke. For each study, we identified the theoretical framework the authors used to design the experimental treatment. To this aim, we used a previously proposed classification of the known models of motor control, dividing them into the following categories: neuroanatomy, robotics, self-organization, and ecological context. In total, 2,185 studies were originally considered in this scoping review. After the screening process, we included and analyzed 45 studies: 20 studies were randomized controlled trials, 12 were case series, 4 were case reports, 8 were observational longitudinal pilot studies, and 1 was an uncontrolled trial. Only 10 studies explicitly declared the reference theoretical model. Considering their classification, 21 studies referred to the robotics motor control model, 12 to the self-organization model, 8 to the neuroanatomy model, and 4 to the ecological model. Our results showed that most of the rehabilitative interventions purposed in stroke rehabilitation have no clear theoretical bases on motor control and motor learning models. We suggest this is an issue that deserves attention when designing new experimental interventions in stroke rehabilitation

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices