Design of a Single-Degree-of-Freedom Immersive Rehabilitation Device for Clustered Upper-Limb Motion

Abstract Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern; hence, we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. By using the regression motion of the clustering result as the target, in this article, we seek to apply kinematic mapping-based motion synthesis framework to design a 1-degree-of-freedom (DOF) mechanism, such that it could lead the patients’ upper limb through the target motion. Also, considering rehab training generally involves a large amount of repetition on a daily basis, this article has developed a rehab system with unity3d based on virtual reality (VR). The proposed device and system could provide an immersive experience to the users, as well as the rehab motion data to the administrative staff for evaluation of users’ status. The construction of the integrated system and the experimental trial of the prototype are presented at the end of this article.

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Design of Single-DOF Immersive Upper Limb Rehabilitation System via Kinematic Mapping and Virtual Reality

Volume 10: 44th Mechanisms and Robotics Conference (MR) ◽

10.1115/detc2020-22168 ◽

2020 ◽

Author(s):

Ping Zhao ◽

Haiwei Guan ◽

Yating Zhang ◽

Yuwen Chen ◽

Xueting Deng ◽

...

Keyword(s):

Virtual Reality ◽

User Interface ◽

Upper Limb ◽

Daily Basis ◽

Integrated System ◽

Upper Limb Rehabilitation ◽

Kinematic Mapping ◽

Mechanical Devices ◽

Limb Rehabilitation ◽

Body Parameters

Abstract Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern, thus we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. Using the regression motion of the clustering result as the task motion, in this paper we seek to apply kinematic-mapping-based motion synthesis framework to design a one-DOF mechanism such that it could lead the patients’ upper limb through the task motion. Also,considering rehab training generally involves a large amount of repetition in daily basis, this paper has developed an immersive rehab system with Unity3D based on Virtual Reality (VR). A patient user interface as well as an administrator user interface are presented, and a two-mode rehabilitation strategy is proposed. The construction of the integrated system and a prototype of the upper limb rehab device are also shown in the end of this paper.

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Motion Synthesis for Upper-Limb Rehabilitation Motion With Clustering-Based Machine Learning Method

Volume 3: Biomedical and Biotechnology Engineering ◽

10.1115/imece2019-10435 ◽

2019 ◽

Author(s):

Wenxiu Chen ◽

Wanbing Song ◽

Haodong Chen ◽

Qi Li ◽

Ping Zhao

Keyword(s):

Machine Learning ◽

Upper Limb ◽

Spectral Clustering ◽

Optimal Solution ◽

Gaussian Mixture ◽

Machine Learning Algorithms ◽

Motion Patterns ◽

Upper Limb Rehabilitation ◽

Limb Rehabilitation ◽

Body Parameters

Abstract Nowadays, mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patient usually has its individual pattern. Thus it is obviously not an optimal solution to use a single motion generator to suit all patients. Yet it would also be unpractical if we design a different motion or even a different mechanism for each user individually. Therefore, in this paper we seek to adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. Firstly, the trajectory of a specified rehabilitation motion are recorded from various subjects, and then 4 types of machine learning algorithms (spectral clustering, hierarchical clustering, self-organizing mapping neural network and Gaussian mixture model) are implemented and compared. It is shown that spectral clustering (SC) yields the best performance and is hereby adopted to generate three clusters of motion patterns. After regression of each cluster, three types of motion for upper limb-rehabilitation are constructed, which could reflect the trajectories’ similarity and difference of people who have various body parameters. These work will provide help for the design of rehabilitation mechanisms.

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Configuration Design and Simulation of Exoskeleton for Upper Limb Rehabilitation Train

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.701-702.654 ◽

2014 ◽

Vol 701-702 ◽

pp. 654-658 ◽

Cited By ~ 2

Author(s):

Yuan Zhang ◽

Qiang Liu ◽

Ji Liang Jiang ◽

Li Yuan Zhang ◽

Rui Rui Shen

Keyword(s):

Upper Limb ◽

Simulation Analysis ◽

Motion Simulation ◽

Upper Limb Rehabilitation ◽

Motion Data ◽

Upper Limb Exoskeleton ◽

Movement Data ◽

Kinematics Simulation ◽

Motion Process ◽

Limb Rehabilitation

A new upper limb exoskeleton mechanical structure for rehabilitation train and electric putters were used to drive the upper limb exoskeleton and kinematics simulation was carried. According to the characteristics of upper limb exoskeleton, program control and master - slave control two different ways were presented. Motion simulation analysis had been done by Pro/E Mechanism, the motion data of electric putter and major joints had been extracted. Based on the analysis of the movement data it can effectively guide the electric putter control and analysis upper limb exoskeleton motion process.

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Design and evaluation of a 3-degree-of-freedom upper limb rehabilitation exoskeleton robot

2017 IEEE International Conference on Mechatronics and Automation (ICMA) ◽

10.1109/icma.2017.8015942 ◽

2017 ◽

Cited By ~ 7

Author(s):

Baofeng Gao ◽

Hongdao Ma ◽

Shuxiang Guo ◽

Hao Xu ◽

Shu Yang

Keyword(s):

Upper Limb ◽

Degree Of Freedom ◽

Upper Limb Rehabilitation ◽

Exoskeleton Robot ◽

Limb Rehabilitation

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Kinematic Design Optimization of an Eight Degree-of-Freedom Upper-Limb Exoskeleton

Robotica ◽

10.1017/s0263574719001085 ◽

2019 ◽

Vol 37 (12) ◽

pp. 2073-2086 ◽

Cited By ~ 3

Author(s):

Amin Zeiaee ◽

Rana Soltani-Zarrin ◽

Reza Langari ◽

Reza Tafreshi

Keyword(s):

Upper Limb ◽

Optimization Problem ◽

Degree Of Freedom ◽

Upper Limb Rehabilitation ◽

Upper Limb Exoskeleton ◽

Wearable Robots ◽

Design Variables ◽

Final Design ◽

Limb Rehabilitation

SummaryThis paper studies the problem of optimizing the kinematic structure of an eight degree-of-freedom upper-limb rehabilitation exoskeleton. The objective of optimization is achieving minimum volume and maximum dexterity in the workspace of daily activities specified by a set of upper-arm configurations. To formulate the problem, a new index is proposed for effective characterization of kinematic dexterity for wearable robots. Additionally, a set of constraints are defined to ensure that the optimal design can cover the desired workspace of the exoskeleton, while singular configurations and physical interferences are avoided. The formulated multi-objective optimization problem is solved using an evolutionary algorithm (Non-dominated Sorting Genetic Algorithm II) and the weighted sum approach. Among the resulted optimal points, the point with least sensitivity with respect to the variations of design variables is chosen as the final design.

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