I have actively engaged in impactful research endeavors, as reflected in my contributions to various research projects. Notably, one of my research involvements lead by Prof. Erik Scheme at the University of New Brunswick, where I spearheaded the development of a comprehensive Python application to capture and synchronize human mobility data from multimodal sources including pressure sensitive tiles, Runscribe wearables and IP cameras. We also worked on extracting skeletal features from the videos of human subjects to analyse and correlate it with gait patterns. This stands as a testament to my ability to navigate complex research challenges. Furthermore, my role at NYU Tandon School of Engineering, where I led the creation of a novel deep learning architecture for robotic hand prosthesis by analyzing sEMG signals from an armband.
1) Effective real time data collection : Receive gait data from the human subject and analyse them. For example, parameters like stride length, Cadence and gait symmetry.
2) Interpreting the data collected : The data collected from the wearables and cameras is analyzed for deciphering patterns in data and gaining meaningful insights by use of ML/DL algorithms.