Authors:
D. S. Shobha, K. Chitra, M. Mohamed Thariq, Saly Jaber
Addresses:
Department of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India. Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Analytical Chemistry, Saint Joseph University, Beirut, Lebanon.
Abstract:
Hands-Free Human-Machine Interaction is a cutting-edge technology that redesigns the man-computer interface. Disabling the use of traditional input devices, such as the mouse, eliminates most of the common issues associated with poor posture, including wrist and hand fatigue. It can be extremely helpful to those who spend many hours on the computer, as it minimises the possibility of repetitive stress injury. It also offers a simple solution for individuals with disabilities who may find traditional mouse use challenging or even impossible to use. Sophisticated machine learning and deep learning algorithms are utilised in the technology to track eye movements and translate them into accurate control of the mouse pointer. Reading from the pre-stored data, the system interprets even minute variations in eye movement and carries out the corresponding action with accuracy, making it extremely sensitive. Sound suppression algorithms are also employed to remove unwanted noises that could interfere with the device's operation, allowing for smooth interaction. It is extremely convenient for physically disabled individuals, giving them an additional degree of freedom and control. On a large scale, hands-free Human-Machine Interaction is a step towards increasing accessibility, comfort, and efficiency of computer use for a broader population.
Keywords: Advanced Machine Learning; Deep Learning Method; Graphical User Interface; Eye-Controlled Cursor; Human-Computer Interaction; Cutting-Edge Technology; Digital signal processing.
Received: 21/07/2024, Revised: 05/11/2024, Accepted: 09/12/2024, Published: 03/03/2025
DOI: 10.64091/ATIHL.2025.000119
AVE Trends in Intelligent Health Letters, 2025 Vol. 2 No. 1 , Pages: 40-51