Title: Artificial intelligence and machine learning for eye-hand coordination test: a review

Authors: Milind Shah; Avani Vasant

Addresses: Department of Computer Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), Charotar University of Science and Technology (CHARUSAT), Changa 388421, Gujarat, India ' Department of Computer Science and Engineering, Krishna School of Emerging Technology and Applied Research (KSET), Drs. Kiran and Pallavi Patel Global University (KPGU), Vadodara, Gujarat, India

Abstract: Eye-hand coordination refers to the ability of an individual to visually perceive their surroundings and accurately react with their hands to interact with objects or perform tasks. This essential skill is crucial in various activities, including writing, driving a car, exercising, and participating in sports. It is central to understanding how the brain creates internal models of the action space and generates movement within it. Eye-hand coordination remains a very complex and elusive problem, further complicated by its distributed representation in the brain. Diseases and disorders such as autism spectrum disorders, cerebral palsy, developmental delays, or visual disorders occur due to poor eye-hand coordination. Significant advancements in technologies like artificial intelligence (AI), deep learning, and machine learning have revolutionised the enhancement of eye-hand coordination. The combination of these technologies has opened up new possibilities in understanding real-time scenarios and improving eye-hand coordination in human visual perception, computer vision, and robotic vision. This study reviews 27 articles and discusses the role of AI, deep learning, and machine learning for eye-hand coordination tests and its related challenges.

Keywords: hand-eye coordination test; machine learning; deep learning; artificial intelligence; AI; robotic vision; eye-hand coordination test; skill recognition.

DOI: 10.1504/IJCVR.2026.153130

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.3, pp.309 - 329

Received: 26 Sep 2022
Accepted: 17 Dec 2023
Published online: 23 Apr 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article