Bi-LSTM network for multimodal continuous human activity recognition and fall detection

Li, H., Shrestha, A., Heidari, H. , Le Kernec, J. and Fioranelli, F. (2019) Bi-LSTM network for multimodal continuous human activity recognition and fall detection. IEEE Sensors Journal, (Accepted for Publication)

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Abstract

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Item Type:Articles
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Mr Haobo and Fioranelli, Dr Francesco and Shrestha, Mr Aman and Heidari, Dr Hadi and Le Kernec, Dr Julien
Authors: Li, H., Shrestha, A., Heidari, H., Le Kernec, J., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Sensors Journal
Publisher:IEEE
ISSN:1530-437X
ISSN (Online):1558-1748

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301526Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy