Posts Tagged stroke rehabilitation

[ARTICLE] Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study – Full Text



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.


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.


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.


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.


Upper limb hemiparesis is one of the most common consequences after a brain injury accident [1]. This motor impairment has an adverse impact on the quality of life of survivors since it hinders the execution of activities of daily living. From the rehabilitation perspective, it is widely accepted that high-intensity and repetitive task-specific practice is the most effective principle to promote motor recovery after a brain injury [12]. However, traditional rehabilitation treatment offers a dose of movement repetition that is in most cases insufficient to facilitate neural reorganization [3]. In response to these current clinical shortcomings, there is a clear interest in alternative rehabilitation methods that improve the arm motor functionality of brain injury survivors.

Hybrid robotic systems for motor rehabilitation are a promising approach that combine the advantages of robotic support or assistive devices and functional electrical stimulation (FES) technologies to overcome their individual limitations and to offer more robust rehabilitation interventions [4]. Despite the potential benefits of using hybrid robotic systems for arm rehabilitation, a recent published review shows that only a few hybrid systems presented in the literature were tested with stroke patients [4]. Possible reasons could be the difficulties arising from the integration of both assistive technologies or the lack of integrated platforms that can be easily setup and used.

End-effector robotic devices combined with FES represent the most typical hybrid systems used to train reaching tasks under constrained conditions [567]. With these systems, patients’ forearms are typically restricted to the horizontal plane to isolate the training of the elbow extension movement. The main advantage of this approach is the simplicity of the setup, with only 1 Degree of Freedom (DoF). However, to maximize the treatment’s outcomes and achieve functional improvement it is necessary to train actions with higher range of motion (> 1 DoF) and functional connotations [89]. Yet, the complexity for driving a successful movement execution in such scenarios requires the implementation of a robust and reliable FES controller.

The appropriate design and implementation of FES controllers play a key role to achieve stable and robust motion control in hybrid robotic systems. The control strategy must be able to drive all the necessary joints to realize the desired movement, and compensate any disturbances to the motion, i.e. muscle fatigue onset as well as the strong nonlinear and time-varying response of the musculoskeletal system to FES [1011]. Consequently, open-loop and simple feedback controllers (e.g. proportional-integral-derivative -PID-) are not robust enough to cope with these disturbances [812]. Meadmore et al. presented a more suitable hybrid robotic system for functional rehabilitation scenarios [13]. They implemented a model-based iterative learning controller (ILC) that adjusts the FES intensity based on the tracking error of the previously executed movement (see [1314] for a detail description of the system). This iterative adjustment allows compensating for disturbances caused by FES. Although this approach addresses some of the issues regarding motion control with FES, it requires a detailed mathematical description of the musculoskeletal system to work properly. In this context, unmodeled dynamics and the linearization of the model can reduce the robustness of the controller performance. Also, the identification of the model’s parameters is complex and time consuming, which limits its applicability in clinical settings [1112].

The Feedback Error Learning (FEL) scheme proposed by Kawato [15] can be considered as an alternative to ILC. This scheme was developed to describe how the central nervous system acquires an internal model of the body to improve the motor control. Under this scheme, the motor control command of a feedback controller is used to train a feedforward controller to learn implicitly the inverse dynamics of the controlled system on-line (i.e. the arm). Complementary, this on-line learning procedure also allows the controller to adapt and compensate for disturbances. In contrast with the ILC, the main advantage of this strategy is that the controller does not require an explicit model of the controlled system to work correctly and that it can directly learn the non-linear characteristic of the controlled system. Therefore, using the FEL control strategy to control a hybrid robotic system can simplify the setup of the system considerably, which makes easier to deploy it in clinical settings as well as personalize its response according to each patient’s musculoskeletal characteristics and movement capabilities. The FEL has been used previously to control the wrist [16] and the lower limb [17] motion with FES in healthy subjects; but it has not been tested on brain injury patients. In a previous pilot study, we partially showed the suitability of the FEL scheme in hybrid robotic systems for reaching rehabilitation with healthy subjects [18]. However, a rigorous and robust analysis has not been presented neither this concept has not been tested with motor impaired patients.

The main objective of this study is to verify the usability of a fully integrated hybrid robotic system based on an FEL scheme for rehabilitation of reaching movement in brain injury patients. To attain such objective two-step experimentation was followed. The first part consists of demonstrating the technical viability and learning capability of the developed FEL controller to drive the execution of a coordinated shoulder-elbow joint movement. The second part consists of testing the usability of the platform with brain injury patients in a more realistic rehabilitation scenario. For this purpose, we assessed the patients’ performance and overall satisfaction and emotional response after using the system.


In this section, we present the hybrid robotic system for the rehabilitation of reaching movement in patients with a brain injury. The system focuses on aiding users to move their paretic arm towards specific distal directions in the space. During the execution of the reaching task, the FEL controller adjusts the intensities of the electrical stimuli delivered to target muscles in order to aid the subjects in tracking accurately the target paths.

Description of the hybrid rehabilitation platform for reaching rehabilitation

Figure 1 shows the general overview of the developed platform. This rehabilitation platform is composed of four main components: the hybrid assistive device (upper limb exoskeleton + FES device); the high-level controller (HLC); the visual feedback and; the user interface. […]

Fig. 1 a General overview of the presented hybrid robotic platform for reaching rehabilitation. bVisual feedback provided to the users. The green ball represents the actual arm position, the blue cross is the reference trajectory, the initial and final position are represented by the gray ball and red square respectively. c Interface for system configuration

Source: Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study | Journal of NeuroEngineering and Rehabilitation | Full Text


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[Abstract] Motor Imagery Training After Stroke: A Systematic Review and Meta-analysis of Randomized Controlled Trials


Background and Purpose: A number of studies have suggested that imagery training (motor imagery [MI]) has value for improving motor function in persons with neurologic conditions. We performed a systematic review and meta-analysis to assess the available literature related to efficacy of MI in the recovery of individuals after stroke.

Methods: We searched the following databases: PubMed, Web of Knowledge, Scopus, Cochrane, and PEDro. Two reviewers independently selected clinical trials that investigated the effect of MI on outcomes commonly investigated in studies of stroke recovery. Quality and risk of bias of each study were assessed.

Results: Of the 1156 articles found, 32 articles were included. There was a high heterogeneity of protocols among studies. Most studies showed benefits of MI, albeit with a large proportion of low-quality studies. The meta-analysis of all studies, regardless of quality, revealed significant differences on overall analysis for outcomes related to balance, lower limb/gait, and upper limb. However, when only high-quality studies were included, no significant difference was found. On subgroup analyses, MI was associated with balance gains on the Functional Reach Test and improved performance on the Timed Up and Go, gait speed, Action Research Arm Test, and the Fugl-Meyer Upper Limb subscale.

Discussion and Conclusions: Our review reported a high heterogeneity in methodological quality of the studies and conflicting results. More high-quality studies and greater standardization of interventions are needed to determine the value of MI for persons with stroke.

Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1,

Source: Motor Imagery Training After Stroke: A Systematic Review an… : Journal of Neurologic Physical Therapy

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[ARTICLE] SITAR: a system for independent task-oriented assessment and rehabilitation

Over recent years, task-oriented training has emerged as a dominant approach in neurorehabilitation. This article presents a novel, sensor-based system for independent task-oriented assessment and rehabilitation (SITAR) of the upper limb.

The SITAR is an ecosystem of interactive devices including a touch and force–sensitive tabletop and a set of intelligent objects enabling functional interaction. In contrast to most existing sensor-based systems, SITAR provides natural training of visuomotor coordination through collocated visual and haptic workspaces alongside multimodal feedback, facilitating learning and its transfer to real tasks. We illustrate the possibilities offered by the SITAR for sensorimotor assessment and therapy through pilot assessment and usability studies.

The pilot data from the assessment study demonstrates how the system can be used to assess different aspects of upper limb reaching, pick-and-place and sensory tactile resolution tasks. The pilot usability study indicates that patients are able to train arm-reaching movements independently using the SITAR with minimal involvement of the therapist and that they were motivated to pursue the SITAR-based therapy.

SITAR is a versatile, non-robotic tool that can be used to implement a range of therapeutic exercises and assessments for different types of patients, which is particularly well-suited for task-oriented training.

The increasing demand for intense, task-specific neurorehabilitation following neurological conditions such as stroke and spinal cord injury has stimulated extensive research into rehabilitation technology over the last two decades.1,2 In particular, robotic devices have been developed to deliver a high dose of engaging repetitive therapy in a controlled manner, decrease the therapist’s workload and facilitate learning. Current evidence from clinical interventions using these rehabilitation robots generally show results comparable to intensity-matched, conventional, one-to-one training with a therapist.35 Assuming the correct movements are being trained, the primary factor driving this recovery appears to be the intensity of voluntary practice during robotic therapy rather than any other factor such as physical assistance required.6,7 Moreover, most existing robotic devices to train the upper limb (UL) tend to be bulky and expensive, raising further questions on the use of complex, motorised systems for neurorehabilitation.

Recently, simpler, non-actuated devices, equipped with sensors to measure patients’ movement or interaction, have been designed to provide performance feedback, motivation and coaching during training.812 Research in haptics13,14 and human motor control15,16 has shown how visual, auditory and haptic feedback can be used to induce learning of a skill in a virtual or real dynamic environment. For example, simple force sensors (or even electromyography) can be used to infer motion control17and provide feedback on the required and actual performances, which can allow subjects to learn a desired task. Therefore, an appropriate therapy regime using passive devices that provide essential and engaging feedback can enhance learning of improved arm and hand use.

Such passive sensor-based systems can be used for both impairment-based training (e.g. gripAble18) and task-oriented training (ToT) (e.g. AutoCITE8,9, ReJoyce11). ToT views the patient as an active problem-solver, focusing rehabilitation on the acquisition of skills for performance of meaningful and relevant tasks rather than on isolated remediation of impairments.19,20 ToT has proven to be beneficial for participants and is currently considered as a dominant and effective approach for training.20,21

Sensor-based systems are ideal for delivering task-oriented therapy in an automated and engaging fashion. For instance, the AutoCITE system is a workstation containing various instrumented devices for training some of the tasks used in constraint-induced movement therapy.8 The ReJoyce uses a passive manipulandum with a composite instrumented object having various functionally shaped components to allow sensing and training of gross and fine hand functions.11 Timmermans et al.22reported how stroke survivors can carry out ToT by using objects on a tabletop with inertial measurement units (IMU) to record their movement. However, this system does not include force sensors, critical in assessing motor function.

In all these systems, subjects perform tasks such as reach or object manipulation at the tabletop level, while receiving visual feedback from a monitor placed in front of them. This dislocation of the visual and haptic workspaces may affect the transfer of skills learned in this virtual environment to real-world tasks. Furthermore, there is little work on using these systems for the quantitative task-oriented assessment of functional tasks. One exception to this is the ReJoyce arm and hand function test (RAHFT)23 to quantitatively assess arm and hand function. However, the RAHFT primarily focuses on range-of-movement in different arm and hand functions and does not assess the movement quality, which is essential for skilled action.2428

To address these limitations, this article introduces a novel, sensor-based System for Independent Task-Oriented Assessment and Rehabilitation (SITAR). The SITAR consists of an ecosystem of different modular devices capable of interacting with each other to provide an engaging interface with appropriate real-world context for both training and assessment of UL. The current realisation of the SITAR is an interactive tabletop with visual display as well as touch and force sensing capabilities and a set of intelligent objects. This system provides direct interaction with collocation of visual and haptic workspaces and a rich multisensory feedback through a mixed reality environment for neurorehabilitation.

The primary aim of this study is to present the SITAR concept, the current realisation of the system, together with preliminary data demonstrating the SITAR’s capabilities for UL assessment and training. The following section introduces the SITAR concept, providing the motivation and rationale for its design and specifications. Subsequently, we describe the current realisation of the SITAR, its different components and their capabilities. Finally, preliminary data from two pilot clinical studies are presented, which demonstrate the SITAR’s functionalities for ToT and assessment of the UL. […]

Continue —> SITAR: a system for independent task-oriented assessment and rehabilitation Journal of Rehabilitation and Assistive Technologies Engineering – Asif Hussain, Sivakumar Balasubramanian, Nick Roach, Julius Klein, Nathanael Jarrassé, Michael Mace, Ann David, Sarah Guy, Etienne Burdet, 2017

Figure 1. The SITAR concept with (a) the interactive table-top alongside some examples of intelligent objects developed including (b) iJar to train bimanual control, (c) iPen for drawing, and (d) iBox for manipulation and pick-and-place.

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[Abstract] The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination.

Source: The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

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[WEB SITE] Stroke rehabilitation gets personalised and interactive – CORDIS

Stroke rehabilitation gets personalised and interactive

The significant socioeconomic costs of stroke coupled with the rise in Europe’s ageing population highlights the need for effective but affordable stroke rehabilitation programmes. EU researchers made considerable headway in this regard through novel rehabilitation paradigms.
Stroke rehabilitation gets personalised and interactive
Computer-mediated rehabilitation tools require a high degree of motor control and are therefore inadequate for patients with significant impairment in motor control. Consequently, many stroke survivors are unable to benefit. The REHABNET (REHABNET: Neuroscience based interactive systems for motor rehabilitation) project came up with an innovative approach to address this critical need.

Researchers successfully developed a hybrid brain-computer interface (BCI)-virtual reality (VR) system that assesses user capability and dynamically adjusts its difficulty level. This motor imagery-based BCI system is tailored to meet the needs of patients using a VR environment for game training coupled with neurofeedback through multimodal sensing technologies.

The game training scenarios address both cognitive and motor abilities. The four rehabilitation scenarios include bimanual motor training, dual motor cognitive-motor training and a simulated city for training on daily living activities.

Pilot and longitudinal studies demonstrated the benefits of longitudinal VR training as compared to existing rehabilitation regimens. The self-report questionnaires also revealed a high user acceptance of the novel system.

Designed for at-home use, the REHABNET toolset is platform-independent and freely available globally as an app (Reh@Mote). Besides deeper insight on factors affecting stroke recovery, this could aid in further improvement of rehabilitation strategies. More importantly, these low-cost toolsets could also address the needs of patients with severe motor and cognitive deficits. Efforts are ongoing to facilitate future commercial exploitation through a technology transfer agreement.

Related information

Source: European Commission : CORDIS : Projects and Results : Stroke rehabilitation gets personalised and interactive

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[Abstract] Preliminary results of testing the recoveriX system on stroke patients 


Motor imagery based brain-computer interfaces (BCI) extract the movement intentions of subjects in real-time and can be used to control a cursor or medical devices. In the last years, the control of functional electrical stimulation (FES) devices drew researchers’ attention for the post-stroke rehabilitation field. In here, a patient can use the movement imagery to artificially induce movements of the paretic arms through FES in real-time.

Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10 to 24 sessions lasting about 40 min each with the recoveriX® system. The patients had to imagine 80 left and 80 right hand movements. The electroencephalogram (EEG) data was analyzed with Common Spatial Patterns (CSP) and linear discriminant analysis (LDA) and a feedback was provided in form of a cursor on a computer screen. If the correct imagination was classified, the FES device was also activated to induce the right or left hand movement.

In at least one session, all patients were able to achieve a maximum accuracy above 96%. Moreover, all patients exhibited improvements in motor function. On one hand, the high accuracies achieved within the study show that the patients are highly motivated to participate into a study to improve their lost motor functions. On the other hand, this study reflects the efficacy of combining motor imagination, visual feedback and real hand movement that activates tactile and proprioceptive systems.

Source: O174 Preliminary results of testing the recoveriX system on stroke patients – Clinical Neurophysiology

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[BLOG POST] Tyromotion Introduces Virtual Reality to Robotic Therapy to Facilitate Stroke Recovery

Rehabilitation technology leader Tyromotion has developed a rehabilitation device that combines virtual reality with robotic therapy to make stroke rehabilitation faster and more efficient.

Tyromotion has created a rehabilitation device that uses a bilateral 3D arm robot and virtual reality glasses to fully immerse stroke patients in virtual worlds where both the visual and physical environments can be shaped. The device is designed to help patients with limited arm function perform daily tasks by challenging and encouraging them to increase their range of motion and the number of repetitions during their therapy sessions. Both these elements are vital to motor learning.

The introduction of virtual reality into therapy delivers a 3D training environment that can be adapted to each individual patient’s abilities. The virtual setting has a gaming element to it, which helps motivate patients to keep repeating their exercises.

Tyromotion’s device is currently being tested by leading rehabilitation facilities in Europe and the United States. The initial reports from therapists and doctors have been very positive, indicating that the new approach to therapy has a strong potential to transform it by increasing patient motivation and making therapy programs more cost effective across the board.

Diego, the robot-assisted arm rehabilitation device used to deliver VR therapy, is the world’s most versatile arm-shoulder rehabilitation device, one that combines robotics with intelligent gravity compensation (IGC) and virtual reality to help patients regain lost arm function. The device offers passive, active and assistive, uni- and bilateral applications that are easily adapted to meet the needs of each patient.

The gravity compensation feature makes heavy arms lighter, allowing physiological movement of the arms in every phase of rehabilitation. The device gives patients more room and more freedom to move and is particularly well suited for task-oriented training with real objects.

Diego offers a versatile range of therapy options with interactive therapy modules that provide haptic and audiovisual feedback, immersing patients in motion in the virtual environment. The therapy modules have different levels of difficulty, which motivates patients to keep making progress. Their progress is then recorded to make their achievements visible.

Diego is suitable for patients of all ages and can be used in all phases of arm rehabilitation. Watch the video below to learn more about its features and benefits.

Related news:

Tyrostation Offers Versatile Range of Therapy Options

Source: Tyromotion Introduces Virtual Reality to Robotic Therapy to Facilitate Stroke Recovery | Fitness Gaming

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[REVIEW] Robotic Devices and Brain Machine Interfaces for Hand Rehabilitation Post-stroke: Current State and Future Potentials – Full Text PDF


This paper reviews the current state of the art in robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain machine interfaces (BMI). The main focus is on the technical specifications required for these devices to achieve their goals. From the literature reviewed, it is clear that these rehabilitation devices can increase the functionality of the human hand post-stroke. However, there are still several challenges to be overcome before they can be fully deployed. Further clinical trials are needed to ensure that substantial improvement can be made in limb functionality for stroke survivors, particularly as part of a programme of frequent at-home high-intensity training over an extended period.

This review serves the purpose of providing valuable insights into robotics rehabilitation techniques in particular for those that could explore the synergy between BMI and the novel area of soft robotics.


Strokes are a global issue affecting people of all ethnicities, genders and ages [1]; approximately 20 million people per year worldwide suffer a stroke [2, 3]. Five million of those patients remain severely handicapped and dependent on assistance in daily life [4]. Once a stroke has occurred the patient may be left with mild to severe disabilities, depending on the type and severity of the stroke. This paper will focus on the primary issues experienced which are the clawing of the hand and stiffening of the wrist. In recent years, several new forms of rehabilitation have been proposed using robot-aided therapy. This work reviews the current state-ofthe-art robotic devices and brain-machine interfaces (BMI) for post-stroke hand rehabilitation, analysing current challenges, highlighting the future potential and addressing any inherent ethical issues.[…]

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[Case Study] Goal-oriented feedback on motor behavior in virtual reality based stroke therapy: A case study using the rehabilitation gaming system – Full Text PDF


Aims: We address the role of short-term goals in virtual reality (VR) applications for motor relearning, which benefit stroke therapy.

Methods: We let stroke patients as well as healthy participants perform reaching tasks in a VR environment for motor rehabilitation, the so-called rehabilitation gaming system (RGS). During the task, patients were provided
with feedback about one’s own performance (mastery goal), healthy participants additionally received feedback of others performances (ego
goal). Measurements include protocols for motor learning and different kinetic variables (both stroke patients and healthy participants) as well as subscales of the intrinsic motivation inventory (IMI) (only healthy participants). As healthy participants showed lower fatigue levels, we could apply additional measurements.

Results: Both mastery goals and ego goals potentially enhance intrinsic motivation and adherence, as they show to foster task performance (e.g., response time in mastery goals decreased with p = 0.014 for healthy participants, for stroke patients with p = 0.011 in the first iteration) as well as perceived effort (p = 0.007 for mastery, p = 0.008 for ego goals). As a secondary outcome, by controlling task difficulty, motor learning does not change across conditions (p = 0.316 for stroke patients, p = 0.323 for healthy participants). This raises the question whether or not task difficulty alone fosters the effectivity of VR based therapy applications, i.e., motor learning, to which motivators such as short-term goals provide little trade-off.

Conclusion: Firstly, we suggest the implementation of mastery and ego goals in VR based stroke therapy, as adherence benefits from the motivational context they provide. Secondly, we argue towards simplicity regarding heuristics in therapeutic game design, which apparently often does not differ from conventional game design apart from setting the right level of challenge.

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[Abstract+References] Fuzzy System as an Assessment Tool for Analysis of the Health-Related Quality of Life for the People After Stroke


Stroke remains one of the leading causes of long-term disability in both developed and developing countries. Prevalence and impact of the stroke-related disability on Health-Related Quality of Life (HRQoL) as a recognized and important outcome after stroke is huge. Quick, valid and reliable assessment of the HRQoL in people after stroke constitutes a significant worldwide problem for scientists and clinicians – there are many tools, but no one fulfills all requirements or has prevailing advantages. This paper presents proposition of an evaluation of HRQoL based on the two-level hierarchical fuzzy system. It uses five clinical scores and scales as the inputs and gives in result value from the interval [0; 1]. It may constitute a useful semi-automated tool for supplementary initial assessment of patient functioning and further cyclic re-assessment for rehabilitation process and patient-centered goals of rehabilitation shaping purposes.

Source: Fuzzy System as an Assessment Tool for Analysis of the Health-Related Quality of Life for the People After Stroke | SpringerLink

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