Enhancing Text Input for Motor Disabilities through IoT and Machine Learning: A Focus on the Swipe-to-Type Algorithm

To improve text input for motor-disabled people, this research uses the Internet of Things (IoT) and machine learning. Swipe-to-Type, a popular touch-based input technique, is the study's focus. User swipe motions and contextual data are collected in real-time using IoT devices. A machine learning framework trains the algorithm to adapt to disabled users' motor skills and preferences. Addressing motor impairment problems, the suggested method improves text input efficiency and personalization. The Swipe-to-Type algorithm is constantly adjusted based on learned patterns to maximize text input speed and accuracy. Integration of IoT devices allows continuous monitoring and adaption, guaranteeing a responsive and user-centric solution. The study technique includes motor disability data gathering, machine learning model creation, and algorithm refining. Preliminary text input performance enhancements may improve communication and accessibility for motor-disabled people. By using IoT and machine learning to empower motor-disabled persons, this research advances assistive technology. The results show that adjusting input techniques to varied user demands is feasible and successful, encouraging digital inclusion.