A review: Motor rehabilitation after stroke with control based on human intent

Li, Min; Xu, Guanghua; Xie, Jun; Chen, Chaoyang

doi:10.1177/0954411918755828

Cited by 60 publications

(45 citation statements)

References 160 publications

(249 reference statements)

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“…The specific perspectives analyze different intervention-related aspects of the systems. Thus, scientific literature shows various classifications according to the type of feedback [13], the focus of treatment [14][15][16][17], the mechanical modularity of devices [18], the control strategies [19,20], the intervention techniques [21], or the user interfaces (EEG/EMG based) [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs

et al. 2019

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“…The strategy of BCI-robot systems in rehabilitation is to recognise the patient's intention to move or perform a task via an electroencephalography acquisition system [21], and then use the robotic device to provide assistive forces in a manner that mimics the actions of a therapist during standard therapy sessions [22]. The resulting feedback is patient-driven and is designed to aid in closing the neural loop from intention to execution.…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Baniqued

Stanyer

Awais

et al. 2019

Preprint

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Electroencephalography-based brain-computer interfaces (BCI) that allow the control of robotic devices to support stroke patients during upper limb rehabilitation are increasingly popular. Hand rehabilitation is focused on improving dexterity and fine motor control and is a core approach for helping stroke survivors regain activities of daily living. This systematic review examines recent developments in BCI-robotic systems for hand rehabilitation and identifies evidence-based clinical studies on stroke patients. A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation in various development stages involving tests on healthy human subjects or stroke survivors. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for two BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery. Most studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. Some studies report statistically significant improvements in functional recovery after stroke, but there is a need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

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“…Durante el proceso de rehabilitación, es importante detectar la intención de la persona de ejecutar un movimiento, ya sea para motivar o mejorar dicha acción [4]. La electromiografía de superficie (sEMG) es un método conveniente para valorar la intención de movimiento de un sujeto, no obstante, las señales de sEMG que provienen de los sujetos sanos difieren significativamente de aquellas que se leen en los pacientes con EVC; esto se debe a la falta de fuerza, falta de control muscular, coactivación muscular patológica, incremento de actividad muscular de fondo, alteración del tono muscular o espasticidad posteriores al EVC.…”

Section: Introductionunclassified

“…2, en las cuales se presentan señales de sEMG tanto de personas sanas, como de pacientes, respectivamente. Se han propuesto diversas técnicas para la extracción de información a partir de señales de sEMG [5]- [13], sin embargo, la utilidad de los resultados es todavía controversial [4].…”

Section: Introductionunclassified

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Modelo neurodifuso para el control de un exoesqueleto para rehabilitación de brazo en pacientes con EVC

Orozco-Soto¹,

Sanpablo²,

Romero-Ávila³

et al. 2019

RCS

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Resumen. En este trabajo se presenta un modelo neurodifuso para el comportamiento muscular de los movimientos de flexión y extensión del codo, utilizando electromiografía de superficie. Adicional a la electromiografía, se considera la dirección del movimiento para determinar el tipo de actividad y la correcta activación muscular. El modelo neurodifuso presentado funciona como un activador de autoridad para un exoesqueleto robótico que pretende usarse en la rehabilitación del brazo en pacientes con EVC. Se utilizaron datos de personas sanas y de pacientes para desarrollar y evaluar el modelo neurodifuso, mostrando resultados satisfactorios, ya que la activación solo se presenta al utilzar las señales de pacientes que realizaron extensión de codo.Palabras clave: modelado y control neurodifuso, exoesqueleto robótico, robótica de rehabilitación.Abstract. This paper presents a neural-fuzzy model for health behavior of elbow flexion and extension motions by means of surface electromiography. In addition to electromiography, the direction of the motion is considered to determine the type of motion and the correct muscular activation. The presented neural-fuzzy model works as the trigger for the authority of a robotic exoskeleton for arm rehabilitation of patients with stroke. Data of healthy persons and stroke patient were used for developing and testing the neural-fuzzy model, showing successful results, since the triggering control is present only with patient signals while performing elbow extension.

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A review: Motor rehabilitation after stroke with control based on human intent

Cited by 60 publications

References 160 publications

Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs

Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Modelo neurodifuso para el control de un exoesqueleto para rehabilitación de brazo en pacientes con EVC

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