The virtual reality system used for upper extremity rehabilitation

Gmerek, Artur

doi:10.1109/mmar.2012.6347900

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…The exoskeleton such as arm support systems are attached to the upper arm, forearm and sometimes also the wrist/hand as shown in Figure 5a [2,4,29,34,51,53,56,64,69]. Applying an exoskeleton arm support, the limbs can be controlled accurately.…”

Section: Exoskeleton Versus End-effectormentioning

confidence: 99%

Overview of Actuated Arm Support Systems and Their Applications

et al. 2013

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Arm support systems provide support throughout daily tasks, training or in an industrial environment. During the last decades a large diversity of actuated arm support systems have been developed. To analyze the actuation principles in these systems, an overview of actuated arm support systems is provided. This overview visualizes the current trends on research and development of these support systems and distinguishes three categories. These categories depend mainly on the functional status of the user environment, which defines the specifications. Therefore, the actuated arm support systems are classified according to their user environment, namely: ambulatory, rehabilitation and industrial. Furthermore, three main actuation principles and three mechanical construction principles have been identified

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Section: Exoskeleton Versus End-effectormentioning

confidence: 99%

Overview of Actuated Arm Support Systems and Their Applications

et al. 2013

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“…Despite the importance of pHMI design on device function, the methods and specifications associated with developing pHMI components have been largely ignored in published literature. An extensive review reveals that details on the pHMI tend to fall into one of three categories: (a) authors refer to an orthosis but the design is either not described or only briefly described in terms of appearance (Perry et al, 2007 ; Jarrassé et al, 2008 ; Klein et al, 2008 ; Pylatiuk et al, 2009 ; Gmerek, 2012 ; Ohnishi et al, 2013 ; Rohm et al, 2013 ; Vaca Benitez et al, 2013 ; Herrnstadt and Menon, 2016 ; Sangha et al, 2016 ); (b) authors describe a generic orthosis design with added compliance to mitigate forces due to misalignment or dissimilarities in anthropometric shape, in which case the design elements focus on kinematics (Jarrassé and Morel, 2012 ); or (c) authors explain human interface details but do not report on the shape of their orthosis design (Jackson et al, 2007 ; Rocon et al, 2007 ; Gupta et al, 2008 ; Vanderniepen et al, 2009 ; Ragonesi et al, 2010 ; Vitiello et al, 2013 ; Alavi et al, 2017 ; Ates et al, 2017 ). In other words, most papers present the mechanical design and/or controller design, but very little content, if any, is focused on the design of the physical interface with the user.…”

Section: Introductionmentioning

confidence: 99%

3D Scanning of the Forearm for Orthosis and HMI Applications

et al. 2021

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The rise of rehabilitation robotics has ignited a global investigation into the human machine interface (HMI) between device and user. Previous research on wearable robotics has primarily focused on robotic kinematics and controls but rarely on the actual design of the physical HMI (pHMI). This paper presents a data-driven statistical forearm surface model for designing a forearm orthosis in exoskeleton applications. The forearms of 6 subjects were 3D scanned in a custom-built jig to capture data in extreme pronation and supination poses, creating 3D point clouds of the forearm surface. Resulting data was characterized into a series of ellipses from 20 to 100% of the forearm length. Key ellipse parameters in the model include: normalized major and minor axis length, normalized center point location, tilt angle, and circularity ratio. Single-subject (SS) ellipse parameters were normalized with respect to forearm radiale-stylion (RS) length and circumference and then averaged over the 6 subjects. Averaged parameter profiles were fit with 3rd-order polynomials to create combined-subjects (CS) elliptical models of the forearm. CS models were created in the jig as-is (CS1) and after alignment to ellipse centers at 20 and 100% of the forearm length (CS2). Normalized curve fits of ellipse major and minor axes in model CS2 achieve R2 values ranging from 0.898 to 0.980 indicating a high degree of correlation between cross-sectional size and position along the forearm. Most other parameters showed poor correlation with forearm position (0.005 < R2 < 0.391) with the exception of tilt angle in pronation (0.877) and circularity in supination (0.657). Normalized RMSE of the CS2 ellipse-fit model ranged from 0.21 to 0.64% of forearm circumference and 0.22 to 0.46% of forearm length. The average and peak surface deviation between the scaled CS2 model and individual scans along the forearm varied from 0.56 to 2.86 mm (subject averages) and 3.86 to 7.16 (subject maximums), with the peak deviation occurring between 45 and 50% RS length. The developed equations allow reconstruction of a scalable 3D model that can be sized based on two user measures, RS length and forearm circumference, or based on generic arm measurements taken from existing anthropometric databases.

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Execution and perception of upper limb exoskeleton for stroke patients: a systematic review

Xia

et al. 2022

Intel Serv Robotics

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The virtual reality system used for upper extremity rehabilitation

Cited by 6 publications

References 5 publications

Overview of Actuated Arm Support Systems and Their Applications

Overview of Actuated Arm Support Systems and Their Applications

3D Scanning of the Forearm for Orthosis and HMI Applications

Execution and perception of upper limb exoskeleton for stroke patients: a systematic review

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