Pose estimation of human–machine interactions such as bicycling plays an important role to understand and study human motor skills. In this paper, we report the development of a human whole-body pose estimation scheme with application to rider–bicycle interactions. The pose estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider's upper- and lower-limbs and the trunk. A single feature point is collocated with each wearable gyroscope and also on the body segment link where the gyroscope is not attached. An extended Kalman filter (EKF) is designed to fuse the visual-inertial measurements to obtain the drift-free whole-body poses. The pose estimation design also incorporates a set of constraints from human anatomy and the physical rider–bicycle interactions. The performance of the estimation design is validated through ten subject riding experiments. The results illustrate that the maximum errors for all joint angle estimations by the proposed scheme are within 3 degs. The pose estimation scheme can be further extended and used in other types of physical human–machine interactions.
FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration
