Seminar Series: Dr. Namgyun Kim

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Augmenting Human Attention and Learning for Construction Safety and Health

The majority of injuries and fatalities in workplaces result from workers’ unsafe behaviors. Traditional instructor-centric safety training, commonly used in the construction industry, has shown limited effectiveness in significantly reducing accidents. This limitation primarily arises because conventional training fails to address the underlying cognitive biases driving unsafe behaviors and does not adequately reflect individual workers’ specific learning characteristics. To address these issues, Dr. Kim integrates data science and wearable sensors to uncover cognitive biases behind unsafe behaviors and tailor effective safety training interventions for construction workers. In this presentation, Dr. Kim will demonstrate how his research approach employs data-driven methods to: (1) objectively observe and quantify human cognitive biases using sensor-derived biosignals; (2) design targeted behavioral interventions grounded in empirical evidence; and (3) rigorously validate the effectiveness of these interventions in mitigating cognitive biases and preventing unsafe behaviors. Dr. Kim will share details from his ongoing interdisciplinary projects funded by the National Science Foundation (NSF) and the Construction Industry Institute (CII), concluding with a discussion of future research directions aimed at transforming the safety and health management paradigm in the construction industry through innovative, data-informed strategies.