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About

Haruka Sekino is a pioneering researcher in human-robot interaction, specializing in how robots can use nonverbal cues—such as facial expressions, gestures, and body language—to influence human behavior and emotional states. Her work bridges social robotics, affective computing, and brain-computer interfaces (BCI), with a focus on educational and assistive contexts. Sekino’s major contributions include demonstrating that a robot’s expressed confidence or unconfidence can shape a user’s self-efficacy during error-prone tasks, and that gesture-based encouragement can measurably alter acceptance tendencies, as tracked through biosensors like heartbeat and brain activity. She also developed an FML-based machine learning tool that integrates BCI data to create emotionally responsive agents for music applications. Though her most-cited papers each hold 3 citations, their collective impact lies in advancing the design of socially intelligent robots that communicate intention beyond words. Sekino’s work is notable for its interdisciplinary approach, combining behavioral analysis, fuzzy logic, and real-time physiological sensing to create more intuitive and effective human-robot partnerships.