This paper addresses the problem of gait estimation using visual and inertial data, as well as human motion models. Specifically, a batch least-squares (BLS) algorithm is presented that fuses data from a minimal set of sensors [two inertial measurement units (IMUs), one on each foot, and a head-mounted IMU-camera pair] along with motion constraints corresponding to the different walking states, to estimate the person's head and feet poses. Subsequently, gait models are employed to solve for the lower-body's posture and generate its animation. Experimental results against the VICON motion capture system demonstrate the accuracy of the proposed minimal sensors-based system for determining a person's motion.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Robotics and Automation, ICRA 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Sep 10 2018|
|Event||2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia|
Duration: May 21 2018 → May 25 2018
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2018 IEEE International Conference on Robotics and Automation, ICRA 2018|
|Period||5/21/18 → 5/25/18|
Bibliographical noteFunding Information:
This work was supported by the University of Minnesota and the National Science Foundation (IIS-1328722)
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