Subhash Pratap
Papers
3
Total Citations
33
H-Index
3
About
Subhash Pratap is pioneering the intersection of multisensory data and machine learning to decode the complexities of human grasping. His research focuses on grasp classification, tactile signal analysis, and the identification of grasp synergies—core areas with transformative potential for robotics, prosthetics, and rehabilitation. Pratap’s major contributions include developing novel methodologies that leverage instrumented data gloves to capture intricate finger dynamics and tactile feedback. His most cited work, "Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach" (2024, 16 citations), introduces a deep learning framework that significantly improves grasp classification accuracy by integrating multiple sensory inputs. In "From Tactile Signals to Grasp Classification: Exploring Patterns with Machine Learning" (2024, 11 citations), he demonstrates how machine learning can extract meaningful patterns from tactile data. His latest study, "Understanding Grasp Synergies During Reach-to-Grasp Using an Instrumented Data Glove" (2025, 6 citations), advances the understanding of underlying motor patterns to inform control strategies for five-fingered prosthetic hands and exoskeletons. Through these works, Pratap is laying the groundwork for more intuitive and responsive human-machine interfaces.
Research Focus
Key Achievements
Top Papers
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