A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot
Shah M.F. Khan N.A. Hussain F. Jamwal P.K. Hussain S.
2024Institute of Electrical and Electronics Engineers Inc.
Proceedings of the IEEE International Multi Topic Conference, INMIC
2024Issue 2024
The problem of inverse kinematics in serially manipulated upper limb rehabilitation robots involves deducing joint rotation angles from the position of the end-effector. Unlike forward kinematics, inverse kinematics lacks systematic solution approaches, and it is especially challenging for certain robot morphologies. This study proposes a deep learning-based model to estimate joint angles from a specified end-effector position. The model shows considerable effectiveness in calculating joint angles for a variety of target positions. The enhanced position-tracking capability of the proposed algorithm than existing analytical methods will enable the development of efficient controllers in future.
Deep learning , Forward kinematics , inverse kinematics , Joint angles , Rehabilitation , Upper Limb
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University of Canberrra, School of Information Technology and Systems, Canberra, Australia
Nazarbayev University, Department of Electrical and Computer Engineering, Astana, Kazakhstan
University of Canberrra
Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026