Kalin Kalinkov

Papers

Total Citations

115

H-Index

About

Kalin Kalinkov is a leading researcher in affective computing and human-computer interaction, with a primary focus on the automated recognition of cognitive load, affect, and stress. His most significant contribution is the creation of the CLAS database (Cognitive Load, Affect and Stress), a landmark multimodal resource published in 2019 that has garnered over 115 citations. This meticulously designed dataset provides synchronized physiological, behavioral, and contextual signals, serving as a critical benchmark for developing and validating machine learning models that interpret human mental states in real-world settings. By addressing the scarcity of high-quality, ecologically valid data, Kalinkov’s work has directly enabled advances in adaptive learning systems, mental health monitoring, and intelligent user interfaces. His research bridges the gap between raw sensor data and meaningful psychological constructs, offering both theoretical insights and practical tools for the research community. Through the CLAS dataset, Kalinkov has established a foundational resource that continues to drive innovation in stress and affect recognition, making him a pivotal figure in the evolution of context-aware, human-centered technology.