Tomoya Kaichi
Papers
1
Total Citations
34
H-Index
1
About
Tomoya Kaichi is a researcher at the forefront of human motion capture (MoCap), specializing in fusing inertial and vision-based sensing for robust 3D pose estimation. His key contributions address the fundamental challenge of position ambiguity in IMU-based systems, particularly in healthcare and human-robot collaboration settings. In his most-cited work, "Resolving Position Ambiguity of IMU-Based Human Pose with a Single RGB Camera" (2020, 34 citations), Kaichi introduced a novel method that combines orientation data from inertial measurement units with positional cues from a single camera—eliminating the need for multi-camera setups. This breakthrough significantly reduces system complexity while maintaining high accuracy, making MoCap more accessible for real-world applications. By bridging the gap between wearable sensors and monocular vision, Kaichi’s work has opened new possibilities for low-cost, portable motion analysis in clinical rehabilitation and interactive robotics. His research continues to push the boundaries of sensor fusion, demonstrating how sparse data streams can be intelligently integrated to reconstruct natural human movement. For students and researchers, Kaichi’s work exemplifies how elegant algorithmic solutions can overcome hardware limitations, offering a compelling pathway toward practical, everyday MoCap systems.
Research Focus
Key Achievements
Top Papers
- 1