Speaker
Description
Accurate motion capture is essential for the analysis and reproduction of human motion, particularly in dynamic activities like running. While traditional camera-based systems with retro-reflective markers remain the gold standard, they are limited by their reliance on controlled environments, restricted spatial range, and extensive setup requirements. Emerging alternatives utilizing inertial measurement units (IMUs)—comprising accelerometers and gyroscopes—offer significant advantages, including environmental independence, portability, and minimal setup time. However, leveraging IMUs for precise motion tracking presents two key technical challenges. The first challenge arises from the nature of IMU data, which consists of. Transforming local frame accelerations and angular velocities data into global orientations and positions requires integrating the measurements. This integration, however, is susceptible to drift, an accumulation of measurement errors, and necessitates a reliable initial pose estimate. The second challenge is the alignment of IMU-derived orientations with anatomically meaningful joint angles. Achieving this requires a robust "latching" mechanism, which maps IMU measurements from a global inertial frame to a neutral anatomical position. Current methods addressing the first challenge often focus on the foot segment and exploit the quasi-cyclic nature of running motion. For instance, principal component analysis (PCA) of angular velocity data over multiple strides has been used to estimate orientations, achieving approximately 7° one-dimensional errors. However, such approaches incur high computational costs and require significant data storage. Solutions to the second challenge frequently depend on exact sensor placement, additional hardware like cameras, or specific user postures or movement during calibration. These methods either negate the practical benefits of IMUs compared to traditional systems or are prone to errors due to sensor misplacement, inclined running surfaces, or user execution inaccuracies. In this study, we propose a novel method to address these challenges. The orientation problem is decoupled into two sub-problems: "leveling" and "heading," which are solved using sensor fusion techniques and physiological properties. The "latching" mechanism for the shank is implemented at the heel strike, utilizing an empirical relationship between shank orientation and impact accelerations. The sagittal foot angle is refined through precise analysis of acceleration data over one running cycle. The proposed latching methods demonstrate high robustness and reliability even on inclined running surfaces. This method was evaluated with 30 runners on a treadmill, achieving sagittal angle estimates within 2° of the gold standard. This demonstrates the feasibility of achieving accurate, reliable, and practical motion capture with IMUs, addressing the limitations of existing methods.
