Authors:
S. Karthik, S. Bhaggiaraj, Edwin Shalom Soji, L. Sarala Deve, S. Silvia Priscila, S. Suman Rajest
Addresses:
Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. karthiks1087@gmail.com, Department of Information Technology, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India. ktsbhaggiaraj@srec.ac.in, Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. edwinshalomsoji.cbcs.cs@bharathuniv.ac.in, saraladeve.cse@bharathuniv.ac.in, silviaprisila.cbcs.cs@bharathuniv.ac.in, Department of Research and Development and International Student Affairs, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. sumanrajest414@gmail.com
Developing patients’ ability to recognize hand gestures is one of the benefits of deep learning generation. Intending to develop a stroke rehabilitation device that is low-cost, non-invasive, and environmentally friendly, this study intends to provide stroke patients with a mechanism that allows them to operate assistive devices via hand gestures. When bringing hand motions to life, superior photographs and preprocessing have been crucial in teaching a deep MediaPipe and OpenCV architecture. With the ability to enable patients to use assistive devices by applying hand gestures, the recommended machine shows promise as a valuable instrument for stroke rehabilitation. This is because the utilization of the recommended machine results in caution. Additional motions might be made to materialize on the device, which would assist stroke patients experiencing various limits. The fact that this is the case demonstrates how much knowledge of the past can improve the accuracy and efficiency of hand gesture popularity systems for stroke victims.
Keywords: Deep Learning; Hand Gesture Recognition; Stroke Rehabilitation; MediaPipe and OpenCV; Real-Time Processing; Machine Learning; Gesture Detection; Patient Engagement; Motor Function Recovery.
Received on: 03/03/2024, Revised on: 01/05/2024, Accepted on: 22/06/2024, Published on: 01/09/2024
AVE Trends in Intelligent Computing Systems, 2024 Vol. 1 No. 3, Pages: 157-172