Inverse Kinematic solution of u-Rob4 an hybrid exoskeleton for stroke rehabilitation

De, Javier Sanjuan; Islam, Rasedul; Montenegro, Elias Munoz; Brahmi, Brahim; Rahman, Mohammad Habibur

doi:10.1109/ssd52085.2021.9429419

Cited by 5 publications

(1 citation statement)

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“…These exoskeletons and the user are connected through one or more interaction ports [13]. However, they have coaxial joints and limb joint axes, and their mechanical structure has more contact points with human joints, increasing the difficulty of human-computer interaction and control strategies and reducing the diversity of the exoskeleton mechanical structures [14][15][16][17][18]. Despite these challenges, exoskeleton rehabilitation robots are still a focus of research.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control

Ren,

Liu,

Wang

2023

Sensors

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To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb’s structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.

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