scholarly journals Computer vision devices for tracking gross upper limb movements in post-stroke rehabilitation

2021 ◽  
Vol 10 (6) ◽  
pp. e57910616143
Author(s):  
Júlia Tannús de Souza ◽  
Eduardo Lázaro Martins Naves ◽  
Angela Abreu Rosa de Sá
Keyword(s):  
Virtual Reality ◽  
Upper Limb ◽  
Video Game ◽  
Stroke Rehabilitation ◽  
Motion Tracking ◽  
Research Literature ◽  
Upper Limb Rehabilitation ◽  
Stroke Treatment ◽  
Post Stroke ◽  
Tracking Devices

Games and virtual reality are new concepts applied to upper limb rehabilitation after stroke. To perform upper limb physiotherapy rehabilitation and restore motor skills through virtual reality resources it is necessary to use an arm tracker, which would be the input of the video game. However, one of the main issues when starting a post-stroke rehabilitation game project is choosing the most suitable gross upper limb motion tracking device. Thus, this article aims to explore the gross upper limb motion tracking devices most commonly used in the scientific literature. To carry out this research, literature searches in English were conducted up to December 2020 in the ACM, PubMed and IEEE Xplore databases. We have selected a total of ninety-five (95) articles. In these studies, we identified the most used gross upper limb motion devices and we classified them into 5 different categories: RGB-D skeletal tracking, RGB object tracking, IR marker tracking, LeapMotion and RGB markerless body tracking. We found that most studies (52%) used RGB-D skeletal tracking. In addition, we found fifteen (15) different commercial systems or tracking devices and the most used was Kinect® (47% of all studies). However, it was not possible to generalize whether one device is better than the other. Although the amount of research in this area has increased significantly in recent years, additional studies are still needed to quantify the potential of the use of gross upper limb motion tracking devices in rehabilitation with games in post-stroke treatment.

Serious Games Design Principles Using Virtual Reality to Gamify Upper Limb Stroke Rehabilitation

2022 ◽  
pp. 235-261
Author(s):  
Robert Herne ◽  
Mohd Fairuz Shiratuddin ◽  
Shri Rai ◽  
David Blacker
Keyword(s):  
Virtual Reality ◽  
Upper Limb ◽  
Game Design ◽  
Serious Games ◽  
Stroke Rehabilitation ◽  
Design Principles ◽  
Stroke Survivors ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Limb Rehabilitation

Stroke is a debilitating condition that impairs one's ability to live independently while also greatly decreasing one's quality of life. For these reasons, stroke rehabilitation is important. Engagement is a crucial part of rehabilitation, increasing a stroke survivor's recovery rate and the positive outcomes of their rehabilitation. For this reason, virtual reality (VR) has been widely used to gamify stroke rehabilitation to support engagement. Given that VR and the serious games that form its basis may not necessarily be engaging in themselves, ensuring that their design is engaging is important. This chapter discusses 39 principles that may be useful for engaging stroke survivors with VR-based rehabilitation post-stroke. This chapter then discusses a subset of the game design principles that are likely to engage stroke survivors with VR designed for upper limb rehabilitation post-stroke.

scholarly journals Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

2019 ◽  
Vol 9 (8) ◽  
pp. 1620 ◽  
Author(s):  
Bai ◽  
Song ◽  
Li
Keyword(s):  
Upper Limb ◽  
Motion Tracking ◽  
Stroke Patients ◽  
Mean Velocity ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Post Stroke ◽  
Reachable Workspace ◽  
Rehabilitation System ◽  
Limb Rehabilitation

In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.

scholarly journals Terapia baseada em realidade virtual usando o Leap Motion Controller para reabilitação do membro superior após acidente vascular cerebral

2017 ◽  
Vol 27 (2) ◽  
pp. 25935
Author(s):  
Nayron Medeiros Soares ◽  
Gabriela Magalhães Pereira ◽  
Renata Italiano da Nóbrega Figueiredo ◽  
Gleydson Silva Morais ◽  
Sandy Gonzaga De Melo
Keyword(s):  
Virtual Reality ◽  
Upper Limb ◽  
Active Role ◽  
Reality Therapy ◽  
Leap Motion ◽  
Motion Controller ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Leap Motion Controller ◽  
Hand Coordination

*** Virtual reality therapy using the Leap Motion Controller for post-stroke upper limb rehabilitation ***AIMS: To evaluate the applicability of a virtual reality-based motion sensor for post-stroke upper limb rehabilitation.CASES DESCRIPTION: Three post-stroke patients were subjected to virtual reality training for rehabilitation of their upper limbs using the Leap Motion Controller technology and the game Playground 3D® for 3 consecutive days. On the first and last days, the Box and Blocks test, the De Melo Eye-Hand Coordination Test, and transcranial magnetic stimulation were applied. On the last day, the patients were evaluated with the Experience Evaluation Form. After the proposed training, a lower motor threshold was observed in both cerebral hemispheres, as well as better performance in the tests that evaluated hand and eye-hand coordination skills. The proposed therapy was well received by the patients.CONCLUSIONS: No adverse effects were observed, and promising and precise results were obtained for the virtual reality-based training using the Leap Motion Controller and Playground 3D®. The training allowed patients to have an active role in the rehabilitation of stroke-induced upper limb sequelae.

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