Human Activity Recognition for an Intelligent Knee Orthosis

Diliana Rebelo, Christoph Amma, Hugo Gamboa, Tanja Schultz

2013

Abstract

This paper investigates the possibility to classify isolated human activities from biosignal sensors integrated into a knee orthosis. An intelligent orthosis that is capable to recognize its wearers activity would be able to adapt itself to the users situation for enhanced comfort. We use a setup with three modalities: accelerometry, electromyography and goniometry to measure leg motion and muscle activity of the wearer. We segment signals in motion primitives and apply Hidden Markov Models to classify these isolated motion primitives. We discriminate between seven activities like for example walking stairs and ascend or descend a hill. In a small user study we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%.

Paper Citation

in Harvard Style

Rebelo D., Amma C., Gamboa H. and Schultz T. (2013). Human Activity Recognition for an Intelligent Knee Orthosis . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 368-371. DOI: 10.5220/0004254903680371

in Bibtex Style

@conference{biosignals13,
author={Diliana Rebelo and Christoph Amma and Hugo Gamboa and Tanja Schultz},
title={Human Activity Recognition for an Intelligent Knee Orthosis},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={368-371},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004254903680371},
isbn={978-989-8565-36-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Human Activity Recognition for an Intelligent Knee Orthosis
SN - 978-989-8565-36-5
AU - Rebelo D.
AU - Amma C.
AU - Gamboa H.
AU - Schultz T.
PY - 2013
SP - 368
EP - 371
DO - 10.5220/0004254903680371