Dilip Sarkar

Papers

Total Citations

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About

Dilip Sarkar’s research lies at the intersection of wearable sensing, human-robot interaction, and intelligent activity recognition. His most-cited work, “Activity monitoring and prediction for humans and NAO humanoid robots using wearable sensors” (2015), introduced a framework that uses data from body-worn sensors to not only track human movements but also predict actions, enabling seamless collaboration between people and humanoid robots like the NAO. This contribution is foundational for assistive robotics and smart health monitoring, where anticipatory systems can enhance safety and autonomy. With over four citations on this paper alone, Sarkar’s work has influenced subsequent studies in sensor-based activity modeling and human-robot coordination. His approach bridges the gap between raw sensor data and actionable predictions, offering a practical pathway for robots to understand and respond to human behavior in real time. For students and researchers exploring wearable technology or humanoid robotics, Sarkar’s research provides a clear, applied example of how sensor fusion and machine learning can create more intuitive, responsive robotic systems.