We present a novel technique that unobtrusively estimates interaction forces exerted by human participants in multi-contact interaction with rigid environments. Our method uses motion capture only, thus circumventing the need to setup cumbersome force transducers at all potential contacts between the human body and the environment. This problem is particularly challenging, as the knowledge of a given motion only characterizes the resultant force, which can generally be caused by an infinity of force distributions over individual contacts. We collect and release a large-scale dataset on how humans instinctively regulate interaction forces on diverse multi-contact tasks and motions. The force estimation framework we propose leverages physics-based optimization and neural networks to reconstruct force distributions that are physically realistic and compatible with real interaction force patterns. We show the effectiveness of our approach on various locomotion and multi-contact scenarios.

Reference

[pdf], [dataset]

@article{tii:pham:2017, 
    author = {Pham, Tu-Hoa and Caron, St{\'e}phane and Kheddar, Abderrahmane},
    journal={IEEE Transactions on Industrial Informatics}, 
    title={Multi-Contact Interaction Force Sensing from Whole-Body Motion Capture}, 
    year={2017}, 
    volume={PP}, 
    number={99}, 
    pages={1-1}, 
    keywords={Force;Informatics;Noise measurement;Optimization;Robot sensing systems;Tracking;Force sensing from motion capture;multi-contact;neural networks;physics-based optimization;whole-body}, 
    doi={10.1109/TII.2017.2760912}, 
    ISSN={1551-3203}, 
    month={},
}