SkillNet: Human Actions Assessment via Human-AI Collaboration
Jan 1, 2026·,,
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0 min read
Indrajeet Ghosh
Avijoy Chakma
Mohammad Saeid Anwar
Sreenivasan Ramasamy Ramamurthy
Nirmalya Roy
Abstract
SkillNet introduces a human-AI collaborative framework for assessing human actions, combining model-based scoring with human-in-the-loop feedback to achieve robust and interpretable skill evaluation across activities.
Type
Publication
ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM)
Authors
Assistant Professor (Tenure-Track) of Computer Science
Sreenivasan Ramasamy Ramamurthy is an Assistant Professor (Tenure-Track) of Computer Science at Bowie State University. His research interests include Human-Centered Intelligent Systems, Embodied AI and Robotics, Edge Intelligence, and Human-Machine Teaming. He received his Ph.D. in Information Systems from UMBC, a Master’s in Biomedical Engineering from VIT University, and a Bachelor’s in Electronics and Instrumentation Engineering from Amrita Vishwa Vidyapeetham. He is a recipient of grants from NAVAIR, Army Research Laboratory, and the Department of Energy in support of his research.