Cognitively Enhanced Control of a Robotic Feeding Arm: A Time-Series Approach for Adaptive Assistance

This research focuses on developing an advanced dexterous robotic arm integrated with a digital twin to enhance assistive technologies for individuals with disabilities and the elderly. By incorporating cognitive capabilities into the robotic control system, the project aims to improve the arm's adaptability to user-specific needs and environmental conditions. A major component of the work involves analyzing time-series data to enable real-time predictions and responses to feeding patterns, enhancing safety and user trust. The digital twin allows for continuous monitoring and optimization, facilitating simulation-based improvements and predictive control. Ultimately, this project seeks to increase user autonomy, lessen the burden on caregivers, and improve the overall quality of life for those with motor impairments, setting a new benchmark in personalized robotic assistance.