Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation

2020 ◽  
Vol 79 ◽  
pp. 19-25
Author(s):  
Wang Wendong ◽  
Li Hanhao ◽  
Xiao Menghan ◽  
Chu Yang ◽  
Yuan Xiaoqing ◽  
...  
Keyword(s):  
Upper Limb ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Upper Limb Exoskeleton ◽  
Interaction System

scholarly journals Multi-connection load compensation and load information calculation for an upper-limb exoskeleton based on a six-axis force/torque sensor

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986318 ◽  
Author(s):  
Xin Wang ◽  
Qiuzhi Song ◽  
Shitong Zhou ◽  
Jing Tang ◽  
Kezhong Chen ◽  
...  
Keyword(s):  
Upper Limb ◽  
Interaction Force ◽  
Center Of Gravity ◽  
Human Robot Interaction ◽  
Whole Body ◽  
Torque Sensor ◽  
Robot Interaction ◽  
End Effector ◽  
Load Compensation ◽  
Upper Limb Exoskeleton

In this article, a method of multi-connection load compensation and load information calculation for an upper-limb exoskeleton is proposed based on a six-axis force/torque sensor installed between the exoskeleton and the end effector. The proposed load compensation method uses a mounted sensor to measure the force and torque between the exoskeleton and load of different connections and adds a compensator to the controller to compensate the component caused by the load in the human–robot interaction force, so that the human–robot interaction force is only used to operate the exoskeleton. Therefore, the operator can manipulate the exoskeleton with the same interaction force to lift loads of different weights with a passive or fixed connection, and the human–robot interaction force is minimized. Moreover, the proposed load information calculation method can calculate the weight of the load and the position of its center of gravity relative to the exoskeleton and end effector accurately, which is necessary for acquiring the upper-limb exoskeleton center of gravity and stability control of whole-body exoskeleton. In order to verify the effectiveness of the proposed method, we performed load handling and operational stability experiments. The experimental results showed that the proposed method realized the expected function.

scholarly journals A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Partha Chakraborty ◽  
Sabbir Ahmed ◽  
Mohammad Abu Yousuf ◽  
Akm Azad ◽  
Salem A. Alyami ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Interaction System ◽  
Attention Level

Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot

2020 ◽  
pp. 1-17
Author(s):  
Qing Sun ◽  
Shuai Guo ◽  
Leigang Zhang
Keyword(s):  
Upper Limb ◽  
Constraint Equation ◽  
Human Robot Interaction ◽  
Rehabilitation Robot ◽  
Connecting Rod ◽  
Robot Interaction ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Training Trajectory

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.

scholarly journals PARLOMA – A Novel Human-Robot Interaction System for Deaf-Blind Remote Communication

10.5772/60416 ◽  
2015 ◽  
Vol 12 (5) ◽  
pp. 57 ◽  
Author(s):  
Ludovico Orlando Russo ◽  
Giuseppe Airò Farulla ◽  
Daniele Pianu ◽  
Alice Rita Salgarella ◽  
Marco Controzzi ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Remote Communication ◽  
Robot Interaction ◽  
Interaction System

scholarly journals Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qiubo Zhong ◽  
Caiming Zheng ◽  
Haoxiang Zhang
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Sequence Data ◽  
Spatial Relationship ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Convolutional Network ◽  
Action Sequence ◽  
Interaction System ◽  
High Discrimination

A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

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