Designing an Ml-Powered Assistive Technology for People With Cognitive and Mental Disabilities

• Published in: 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) pp. 1 - 7
• Main Authors: Roobini, M.S., Asish, B. Bhavani, Challa, Sujeeth, Kalaiarasi, G, Srividhya, E.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04-04-2024
• Subjects: Machine learning; Machine learning algorithms; Mental disorders; Natural language processing; Privacy; User interfaces; Visualization
• DOI: 10.1109/ICONSTEM60960.2024.10568651

The development of an assistive device powered by machine learning (ML) for people with cognitive and mental disorders is highlighted in this abstract. The objective is to develop an intelligent system that will improve these people's independence, communication, and quality of life. This technology's design incorporates ML algorithms, sensorbased data collecting, and an intuitive user interface. The system can understand and adapt to the particular requirements and difficulties faced by people with cognitive and mental disorders by employing ML approaches. The machine learning algorithms are taught to comprehend and interpret user inputs including gestures, speech, and facial expressions and offer the proper help or interventions. The system may gather real-time data on the user's environment, activity, and emotional state thanks to the sensor-based data gathering, enabling personalized and context-aware support. People with different levels of cognitive ability can readily interact with the system thanks to the user-friendly interface. It features visual signals, natural language processing, and adjustable settings to suit personal tastes. The keywords for this abstract include: machine learning, assistive technology, cognitive disabilities, mental disabilities, independence, communication, quality of life, ML algorithms, sensor-based data collection, user-friendly interface, gestures, speech, facial expressions,personalized support, context-aware support, natural language processing, visual cues, and customizable settings.