International Journal of e-Collaboration (IJeC)
Huina Gao, Ravindra Luhach, Muhammed Alshehri
Abstract
In researching cognitive or motor learning aspects of activity control, motor imagery (MI) is a widely used model. Research has shown that motor imagery training can aid in memorizing motor functions because of the functional associations it shares with physical movement. Because of the high level of contact in these sports, players are more likely to sustain finger injuries. As a group of machine learning techniques, cognitive web services are designed to solve AI-related challenges. Because they are modular, cognitive web services can be easily integrated into any program, making AI more accessible to everyone. Some performers return to play early with defensive splinting, taping, and casting depending on the damage and the position played. Other injuries, predominantly in performers necessitating the full use of their hand for their position, require a more extended rehabilitation period and lengthy time away from the field. Therefore, this paper proposes the motor imagery-based finger rehabilitation system (MIFRS) in sports injury rehabilitation.