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Authors: Diliana Rebelo 1 ; Christoph Amma 2 ; Hugo Gamboa 3 and Tanja Schultz 2

Affiliations: 1 FCT-UNL, Portugal ; 2 Karlsruhe Institute of Technology (KIT), Germany ; 3 FCT-UNL and PLUX-Wireless Biosignals S.A, Portugal

Keyword(s): Biosignals, Human Activity Recognition, Signal-processing, Hidden Markov Models.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

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%.


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Paper citation in several formats:
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 (BIOSTEC 2013) - BIOSIGNALS; ISBN 978-989-8565-36-5; ISSN 2184-4305, SciTePress, pages 368-371. DOI: 10.5220/0004254903680371

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 (BIOSTEC 2013) - BIOSIGNALS},


JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2013) - BIOSIGNALS
TI - Human Activity Recognition for an Intelligent Knee Orthosis
SN - 978-989-8565-36-5
IS - 2184-4305
AU - Rebelo, D.
AU - Amma, C.
AU - Gamboa, H.
AU - Schultz, T.
PY - 2013
SP - 368
EP - 371
DO - 10.5220/0004254903680371
PB - SciTePress