Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning

In order to make most patients recover most of their limb functions after rehabilitation training, virtual reality technology is an emerging human-computer interaction technology, which uses the computer and the corresponding application software to build the virtual reality environment. Completing the training tasks in the virtual environment attracts the patients to conduct repeated training in the game and task-based training mode and gradually realizes the rehabilitation training goals. For the rehabilitation population with certain exercise ability, the kinematics of human upper limbs is mainly analyzed, and the virtual reality system based on HTC VIVE is developed. The feasibility and work efficiency of the upper limb rehabilitation training system were verified by experiments. Adult volunteers who are healthy and need rehabilitation training to participate in the experiment were recruited, and experimental data were recorded. The virtual reality upper limb rehabilitation system was a questionnaire. By extracting the motion data, the system application effect is analyzed and evaluated by the simulation diagram. Follow-up results of rehabilitation training showed that the average score of healthy subjects was more than 4 points and 3.8 points per question. Therefore, it is feasible to perform upper limb rehabilitation training using the HTC VIVE virtual reality rehabilitation system.

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A Scoping Study on the Development of an Interactive Upper-Limb Rehabilitation System Framework for Patients with Stroke

Universal Access in Human-Computer Interaction. Virtual, Augmented, and Intelligent Environments - Lecture Notes in Computer Science ◽

10.1007/978-3-319-92052-8_30 ◽

2018 ◽

pp. 386-393

Author(s):

Kevin C. Tseng ◽

Alice M. K. Wong ◽

Chung-Yu Wu ◽

Tian-Sheuan Chang ◽

Yu-Cheng Pei ◽

...

Keyword(s):

Upper Limb ◽

System Framework ◽

Upper Limb Rehabilitation ◽

Scoping Study ◽

Rehabilitation System ◽

Limb Rehabilitation

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

Applied Sciences ◽

10.3390/app9081620 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1620 ◽

Cited By ~ 6

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.

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