Dynamic rider/bicycle pose estimation with force/IMU measurements

Pose estimation of human motor skills such as bicycling in natural environments is challenging because of highly-dimensional human motion. In this paper, we present a dynamic rider/bicycle pose estimation scheme that can be used in outdoor environments. The proposed estimation scheme is based on the integration of the rider/bicycle dynamic model with the measurements from the force sensor and inertial measurement units (IMU). We take advantages of the attractive properties of both the force and IMU sensors in the design, that is, the force measurements do not suffer drifting while the IMU measurements generate real-time attitude and acceleration information. The rider/bicycle dynamic model provides an underlying relationship between the force and the IMU measurements. We demonstrate the effectiveness and performance of the pose estimation design through extensive bicycle riding experiments.