Pose estimation in physical human-machine interactions with application to bicycle riding

Tracking whole-body human pose in physical human-machine interactions such as bicycling is challenging because of highly-dimensional human motions and lack of inexpensive, effective motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the whole-body pose in human-machine interaction with application to the rider-bicycle system. The estimation scheme is built on the fusions of gyroscopes, accelerometers and force sensors with six Extended Kalman filter designs. The use of physical human-machine interaction constraints further helps to eliminate the integration drifts of inertial sensors measurements and also to reduce the number of the inertial sensors for whole-body pose estimation. For each set of upper- and lower-limb, only one tri-axial gyroscope is needed to accurately obtain the pose information. The performance of the drift-free, reliable estimation scheme is demonstrated through both the indoor and outdoor bicycle riding experiments. The proposed approach can be further extended to other types of physical human-machine interactions.