Jaakko Lehtinen

Papers

Total Citations

H-Index

About

Jaakko Lehtinen is a pioneering researcher at the intersection of human-computer interaction, biomechanics, and artificial intelligence. His work focuses on developing predictive models to enhance user experience and physiological understanding in interactive systems. Lehtinen’s major contribution lies in using deep reinforcement learning to simulate and predict human movement and fatigue, particularly in mid-air interaction contexts. His most-cited paper, "Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement Learning" (2020, 63 citations), addresses the "Gorilla arm" effect—a common ergonomic issue in gesture-based interfaces—by training AI agents to replicate biomechanical responses, enabling low-cost, user-free testing. This work has significant implications for designing more comfortable virtual and augmented reality systems. Additionally, Lehtinen has explored neurophysiological applications, as seen in his paper "Improving closed-loop TMS timing using the Wavenet model" (2021, 8 citations), which advances robotic transcranial magnetic stimulation (TMS) mapping for motor learning studies in children. His research bridges computational modeling and real-world usability, offering tools to predict fatigue, optimize interaction design, and assess neural plasticity. Lehtinen’s innovative use of AI to simulate human behavior marks a notable achievement, positioning him as a key figure in ergonomic and neurotechnology research.