Recent research:
The body of work converges on empowering inclusive education, rehabilitation, and health technologies through accessible AI-driven tools, robotics, and computer vision. Across diverse applications—from EMG-based rehabilitation monitoring and fall/detection systems to serious games, multisensory stimulation, and early screening in pediatrics—the author integrates machine learning, deep learning, and data analytics with user-centered design to expand access in low-resource settings. Several projects explore autonomous robots or robotic assistants to support therapy, cognitive training, and communication for children with disabilities, as well as elder care and anxiety reduction in clinical contexts. A focus on education technology and accessibility emerges through models for adaptive content, learning analytics, and universal design of interactive resources, including open educational resources, MOOCs, and inclusive evaluation frameworks. Methodologies span experimental pilots, expert validation, surveys, data mining, regression trees, YOLOv5-based object recognition, MoveNet-based posture analysis, and standard evaluation metrics. Impact centers on increasing autonomy, reducing therapy/session burden, improving engagement and attention, and delivering cost-effective, scalable solutions in developing contexts while addressing SDGs related to inclusive education, health, and well-being.