An Intrinsic Human Physical Activity Recognition from Fused Motion Sensor Data Using Bidirectional Gated Recurrent Neural Network in Healthcare

aut.relation.conference19th International Conference on Smart Business Technologiesen_NZ
dark.contributor.authorStephen, Oen_NZ
dark.contributor.authorMadanian, Sen_NZ
dark.contributor.authorNguyen, Men_NZ
dc.date.accessioned2023-01-10T20:48:48Z
dc.date.available2023-01-10T20:48:48Z
dc.date.copyright2022en_NZ
dc.date.issued2022en_NZ
dc.description.abstractAn intrinsic bi-directional gated recurrent neural network for recognising human physical activities from intelligent sensors is presented in this work. In-depth exploration of human activity data is significant for assisting different groups of people, including healthy, sick, and elderly populations in tracking and monitoring their level of healthcare status and general fitness. The major contributions of this work are the introduction of a bidirectional gated recurrent unit and a state-of-the-art nonlinearity function called rectified adaptive optimiser that boosts the performance accuracy of the proposed model for the classification of human activity signals. The bidirectional gated recurrent unit (Bi-GRU) eliminates the short-term memory problem when training the model with fewer tensor operations, and the nonlinear function, a variant of the classical Adam optimiser provides an instant dynamic adjustment to the adaptive models’ learning rate based on the keen observation of the impact of variance and momentum during training. A detailed comparative analysis of the proposed model performance was conducted with long-short-term-memory (LSTM), gated recurrent unit (GRU), and bi-directional LSTM. The proposed method achieved a remarkable landmark result of 99% accuracy on the test samples, outperforming the earlier architectures.
dc.identifier.citationIn Proceedings of the 19th International Conference on Smart Business Technologies - ICSBT, ISBN 978-989-758-587-6; ISSN 2184-772X, pages 26-32. DOI: 10.5220/0011296400003280
dc.identifier.doi10.5220/0011296400003280en_NZ
dc.identifier.isbn9789897585876en_NZ
dc.identifier.issn2184-772Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15790
dc.publisherSCITEPRESS - Science and Technology Publicationsen_NZ
dc.relation.urihttps://www.scitepress.org/Link.aspx?doi=10.5220/0011296400003280
dc.rights.accessrightsOpenAccessen_NZ
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHuman Physical Activity Recognition; Gated Recurrent Unit; Machine Leaning; Rectified Adam Optimiser; Deep Learning; Movement Recognition
dc.titleAn Intrinsic Human Physical Activity Recognition from Fused Motion Sensor Data Using Bidirectional Gated Recurrent Neural Network in Healthcareen_NZ
dc.typeConference Contribution
pubs.elements-id462800
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
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