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About

Thomas Seel is a prominent researcher specializing in inertial motion tracking, human motion analysis, and intelligent control systems for robotics and rehabilitation engineering. His work has fundamentally advanced how inertial measurement units (IMUs) are used to capture and analyze human movement, particularly in clinical and biomedical contexts. Seel's most celebrated contribution, "IMU-Based Joint Angle Measurement for Gait Analysis" (2014), has garnered over 856 citations, reflecting its transformative impact on the field. By developing methods that account for the irregular geometry of the human body — avoiding simplistic assumptions about surface orientation — he made accurate, wearable motion capture practical for real-world gait analysis. Complementary work on joint axis and position estimation from inertial data further cemented his reputation as a leading innovator in sensor-based biomechanics. A recurring theme across his research is overcoming the limitation of magnetic disturbances in indoor environments, driving his development of magnetometer-free motion tracking methods that remain robust in clinical and industrial settings. His BROAD benchmark dataset has additionally provided the research community with standardized tools for evaluating inertial orientation algorithms. More recently, Seel has expanded into learning-based control, including reinforcement learning and neural network-driven model predictive control for soft and rehabilitation robots, demonstrating a broad and evolving research vision bridging sensing, biomechanics, and autonomous systems.