Video-based Feedback for Assisting Physical Activity

Renato Baptista, Michel Antunes, Djamila Aouada, Björn Ottersten

2017

Abstract

In this paper, we explore the concept of providing feedback to a user moving in front of a depth camera so that he is able to replicate a specific template action. This can be used as a home based rehabilitation system for stroke survivors, where the objective is for patients to practice and improve their daily life activities. Patients are guided in how to correctly perform an action by following feedback proposals. These proposals are presented in a human interpretable way. In order to align an action that was performed with the template action, we explore two different approaches, namely, Subsequence Dynamic Time Warping and Temporal Commonality Discovery. The first method aims to find the temporal alignment and the second one discovers the interval of the subsequence that shares similar content, after which standard Dynamic Time Warping can be used for the temporal alignment. Then, feedback proposals can be provided in order to correct the user with respect to the template action. Experimental results show that both methods have similar accuracy rate and the computational time is a decisive factor, where Subsequence Dynamic Time Warping achieves faster results.

Paper Citation

in Harvard Style

Baptista R., Antunes M., Aouada D. and Ottersten B. (2017). Video-based Feedback for Assisting Physical Activity . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 274-280. DOI: 10.5220/0006132302740280

in Bibtex Style

@conference{visapp17,
author={Renato Baptista and Michel Antunes and Djamila Aouada and Björn Ottersten},
title={Video-based Feedback for Assisting Physical Activity},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={274-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006132302740280},
isbn={978-989-758-226-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Video-based Feedback for Assisting Physical Activity
SN - 978-989-758-226-4
AU - Baptista R.
AU - Antunes M.
AU - Aouada D.
AU - Ottersten B.
PY - 2017
SP - 274
EP - 280
DO - 10.5220/0006132302740280