Ongoing Research

The fusion of neuroscience, robotics, and artificial intelligence through our innovative project leverages Brain-Computer Interface (BCI) technology to revolutionize human-robot interaction. Enabling direct brain-to-robot communication, this initiative transcends conventional controls, creating a seamless interface for effortless collaboration, advancing assistive technologies, and enhancing user experiences.

Vitamin D is vital for health, but deficiencies are widespread due to poor diets, declining soil nutrients, and limited access to traditional diagnostics. Blood tests, though accurate, are invasive and costly, especially in low-resource areas. This project introduces a non-invasive, machine learning-based system to detect vitamin deficiencies via images of skin, hair, and nails, enhancing accessibility and supporting telemedicine.

This research is for a method to enhance robot intelligence for environment identification using visual perception. We explore five environments: electronics lab, mechatronics lab, lecture halls, roads, and playgrounds, using Computer Vision for object detection to identify 43 distinct objects. An AI model was trained for improved accuracy in live feeds, and a Python script was developed to count objects in each environment, creating a dataset based on object occurrences. This dataset was used to train a model capable of identifying environments by object counts. Our approach demonstrates the integration of object detection and neural networks for intelligent environment identification, enhancing robotic autonomy in varied settings.