Jyotirmay Sanghvi

Papers

Total Citations

245

H-Index

About

Jyotirmay Sanghvi’s research lies at the intersection of affective computing, human-robot interaction, and social signal processing, with a focus on enabling machines to perceive and respond to human emotional states. His most influential work, “Automatic analysis of affective postures and body motion to detect engagement with a game companion” (2011, 245 citations), pioneered the use of full-body motion and posture cues—rather than just facial expressions—to infer engagement and affect during naturalistic human-robot play. This study demonstrated that contextualized, emergent affective expressions in real interactions are essential for training socially perceptive robots, challenging earlier approaches that relied on posed or decontextualized data. Sanghvi’s contributions have been instrumental in advancing the design of affect recognition systems that operate robustly in dynamic, unscripted settings, directly impacting the development of companion robots and interactive agents. His work is widely cited in robotics, computer vision, and human-computer interaction communities, underscoring its foundational role in bridging the gap between laboratory models and real-world social robotics.