Clinical relevance of the effects of reach-to-grasp training using trunk restraint in individuals with hemiparesis poststroke: A systematic review

Greisberger, Andrea; Aviv, Hanna; Garbade, Sven F.; Diermayr, Gudrun

doi:10.2340/16501977-2077

Cited by 26 publications

(16 citation statements)

References 35 publications

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“…Their effects are discussed diversely in the literature. For example, 5 of 8 studies named by Greisberger et al (2016) showed improvements of arm motion recovery, whereas one of the included studies states auditory feedback as more effective on movement patterns directly after training, and the other two studies did not reveal any effect. In conclusion, Greisberger et al (2016) considered the magnitude of change of the observed improvements as not consistently clinically relevant.…”

Section: Discussionmentioning

confidence: 99%

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

2020

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End-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices. In contrast, we propose a magnetometer-free sensor fusion method. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively; and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ±10 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.

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Section: Discussionmentioning

confidence: 99%

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

2020

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“…Initially, the idea was basically to physically restrict Michaelsen et al (2001); Michaelsen and Levin (2004); Pain et al (2015); Greisberger et al (2016); Bakhti et al (2017) to the movement of the trunk of stroke patients. This physical restriction of the patient's trunk in the chair happened through straps, in order to avoid the removal of the trunk from the chair and, consequently, the movement compensation.…”

Section: Related Workmentioning

confidence: 99%

Use of RGB-D Camera for Analysis of Compensatory Trunk Movements in Upper Limbs Rehabilitation

Garcia

Kelbouscas

Guimarães

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Assistive robotics has been shown to be an important tool in the patient's rehabilitation process. One of the rst steps in this process is to capture the movements performed by the patient to analyze the movement restrictions presented. The present work presents the development of a range of motion measurement system based on the position of thejoints in the three-dimensional space of the upper limbs using the Kinect sensor. In addition, preliminary tests are presented to capture compensatory movements of the trunk, in order to investigate the feasibility of using this system as a tool to detect compensatory movements.

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“…Inicialmente, a ideia consistia basicamente em restringir fisicamente (Michaelsen et al, 2001;Michaelsen and Levin, 2004;Pain et al, 2015;Greisberger et al, 2016;Bakhti et al, 2017) a movimentação do tronco dos pacientes com AVC. Esta restrição física do tronco do paciente na cadeira acontecia por meio de tiras, a fim de evitar o afastamento do tronco da cadeira e, consequentemente, a compensação do movimento.…”

Section: Introductionunclassified

Análise de Movimentos Compensatórios na Reabilitação de Membros Superiores através do Kinect

Garcia

Kelbouscas

Guimarães

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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A Robótica Assistiva vem se mostrando uma importante ferramenta no processo de reabilitação de pacientes. A reabilitação dos pacientes a partir de abordagens terapêuticas tradicionais não tem alcançado os resultados esperados, fazendo com que as abordagens terapêuticas que utilizam a robótica passassem a ganhar cada vez mais força. Com isso, uma série de novas intervenções passaram a ser utilizadas com o intuito de auxiliar na reabilitação dos pacientes com determinadas restrições, principalmente aqueles que sofreram traumas pós AVC. Os pacientes com AVC costumam usar o tronco para compensar a função motora do membro superior prejudicada durante o treinamento de reabilitação do membro superior, o que resulta em um efeito reduzido do treinamento de reabilitação. Detectar compensações de tronco pode melhorar o efeito do treinamento de reabilitação. Sendo assim, este estudo tem o intuito de analisar o comportamento da movimentação do tronco de quatro voluntários, que não possuem nenhum tipo de restrição, na execução de quatro movimentos pré-estabelecidos. O objetivo é verificar se, mesmo não possuindo nenhum tipo de restrição, os voluntários fazendo uso da movimentação do tronco para executar os movimentos. O sensor Kinect v2 foi utilizado para realizar a captura das informações.

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Clinical relevance of the effects of reach-to-grasp training using trunk restraint in individuals with hemiparesis poststroke: A systematic review

Abstract: Trunk restraint has immediate and some long-term effects in adults with chronic stroke. However, these effects are not consistently clinically relevant when referring to minimal detectable change or minimal clinically important difference values.

Cited by 26 publications

References 35 publications

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

Use of RGB-D Camera for Analysis of Compensatory Trunk Movements in Upper Limbs Rehabilitation

Análise de Movimentos Compensatórios na Reabilitação de Membros Superiores através do Kinect

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