Hierarchical Classification on Multimodal Sensing for Human Activity Recogintion and Fall Detection

Li, H., Fioranelli, F. , Le Kernec, J. and Heidari, H. (2018) Hierarchical Classification on Multimodal Sensing for Human Activity Recogintion and Fall Detection. In: IEEE Sensors 2018 Conference, New Delhi, India, 28-31 Oct 2018, ISBN 9781538647073 (doi: 10.1109/ICSENS.2018.8589797)

Abstract

This paper presents initial results on the usage of hierarchical classification for human activities discrimination and fall detection in the context of assisted living. Multimodal sensing is proposed by combining data from a wearable device and a radar system. The effect of different approaches in selecting the activities in each sub-group of the hierarchy are explored and reported as preliminary results in this work, while a more detailed investigation is undergoing. 1.2-2.2% improvement in accuracy with SVM and DL classifiers compared with the conventional case of activity classification is reported; subsequent improvement (1.6%) occurs when using SVM-SFS in the second stage of hierarchical classification.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fioranelli, Dr Francesco and Heidari, Professor Hadi and Le Kernec, Dr Julien and Li, Haobo
• Authors: Li, H., Fioranelli, F., Le Kernec, J., and Heidari, H.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2168-9229
• ISBN: 9781538647073
• Copyright Holders: Copyright © 2018 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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