A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot

Muhammad Faizan Shah; Naveed Ahmad Khan; Fahad Hussain; Prashant Jamwal; Shahid HUSSAIN

doi:10.1109/INMIC64792.2024.11004307

A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot

Muhammad Faizan Shah
, Naveed Ahmad Khan
, Fahad Hussain
, Prashant Jamwal
, Shahid HUSSAIN

AI & Robotics

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot
Editors	Muhammad Shaheer Mirza
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-6
Number of pages	6
Edition	2024
ISBN (Electronic)	9798331507213
DOIs	https://doi.org/10.1109/INMIC64792.2024.11004307
Publication status	Published - 2024
Event	26th International Multi Topic Conference, INMIC 2024 - Karachi, Pakistan Duration: 30 Dec 2024 → 31 Dec 2024

Conference

Conference	26th International Multi Topic Conference, INMIC 2024
Country/Territory	Pakistan
City	Karachi
Period	30/12/24 → 31/12/24

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10.1109/INMIC64792.2024.11004307

Cite this

Shah, M. F., Khan, N. A., Hussain, F., Jamwal, P., & HUSSAIN, S. (2024). A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. In M. Shaheer Mirza (Ed.), A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot (2024 ed., pp. 1-6). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/INMIC64792.2024.11004307

Shah, Muhammad Faizan ; Khan, Naveed Ahmad ; Hussain, Fahad et al. / A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. editor / Muhammad Shaheer Mirza. 2024. ed. IEEE, Institute of Electrical and Electronics Engineers, 2024. pp. 1-6

@inproceedings{8d4755fb927c4f749a7397f130e8c874,

title = "A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot",

abstract = "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.",

keywords = "Deep learning, Forward kinematics, inverse kinematics, Joint angles, Rehabilitation, Upper Limb",

author = "Shah, \{Muhammad Faizan\} and Khan, \{Naveed Ahmad\} and Fahad Hussain and Prashant Jamwal and Shahid HUSSAIN",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 26th International Multi Topic Conference, INMIC 2024 ; Conference date: 30-12-2024 Through 31-12-2024",

year = "2024",

doi = "10.1109/INMIC64792.2024.11004307",

language = "English",

pages = "1--6",

editor = "\{Shaheer Mirza\}, \{Muhammad \}",

booktitle = "A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

edition = "2024",

}

Shah, MF, Khan, NA, Hussain, F, Jamwal, P & HUSSAIN, S 2024, A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. in M Shaheer Mirza (ed.), A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. 2024 edn, IEEE, Institute of Electrical and Electronics Engineers, pp. 1-6, 26th International Multi Topic Conference, INMIC 2024, Karachi, Pakistan, 30/12/24. https://doi.org/10.1109/INMIC64792.2024.11004307

A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. / Shah, Muhammad Faizan; Khan, Naveed Ahmad; Hussain, Fahad et al.
A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. ed. / Muhammad Shaheer Mirza. 2024. ed. IEEE, Institute of Electrical and Electronics Engineers, 2024. p. 1-6.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot

AU - Shah, Muhammad Faizan

AU - Khan, Naveed Ahmad

AU - Hussain, Fahad

AU - Jamwal, Prashant

AU - HUSSAIN, Shahid

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Deep learning

KW - Forward kinematics

KW - inverse kinematics

KW - Joint angles

KW - Rehabilitation

KW - Upper Limb

UR - https://www.scopus.com/pages/publications/105011097367

UR - https://ieeexplore.ieee.org/xpl/conhome/11004179/proceeding

UR - https://shu.edu.pk/conference/inmic2024/

U2 - 10.1109/INMIC64792.2024.11004307

DO - 10.1109/INMIC64792.2024.11004307

M3 - Conference contribution

AN - SCOPUS:105011097367

SP - 1

EP - 6

BT - A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot

A2 - Shaheer Mirza, Muhammad

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 26th International Multi Topic Conference, INMIC 2024

Y2 - 30 December 2024 through 31 December 2024

ER -

Shah MF, Khan NA, Hussain F, Jamwal P, HUSSAIN S. A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. In Shaheer Mirza M, editor, A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot. 2024 ed. IEEE, Institute of Electrical and Electronics Engineers. 2024. p. 1-6 doi: 10.1109/INMIC64792.2024.11004307

A Deep Learning-Aided Framework for Joint Angle Estimation of an Upper Limb Rehabilitation Robot

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