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research-article

Whole-Body Pose Estimation in Physical Rider-Bicycle Interactions with a Monocular Camera and Wearable Gyroscopes

[+] Author and Article Information
Xiang Lu

Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin 300071, P. R. China
lux@mail.nankai.edu.cn

Kaiyan Yu

Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854 USA
kaiyan.yu@rutgers.edu

Yizhai Zhang

Research Center of Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi'an, Shanxi 710072 China
zhangyizhai@nwpu.edu.cn

Jingang Yi

Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854 USASchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
jgyi@rutgers.edu

Jingtai Liu

Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300071, P. R. China
liujingtai@nankai.edu.cn

Qijie Zhao

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
zqj@shu.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4035760 History: Received April 19, 2016; Revised January 09, 2017

Abstract

Pose estimation of outdoor human-machine interactions such as bicycling also 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-limb 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 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 and for human-centered automation and rehabilitation.

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