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Authors: Okeke Stephen ; Samaneh Madanian and Minh Nguyen

Affiliation: Department of Computer Science and Software Engineering, Auckland University of Technology (AUT), 6 St. Paul Street, Auckland, New Zealand

Keyword(s): Human Physical Activity Recognition, Gated Recurrent Unit, Machine Leaning, Rectified Adam Optimiser, Deep Learning, Movement Recognition.

Abstract: An 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. (More)

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Paper citation in several formats:
Stephen, O.; Madanian, S. and Nguyen, M. (2022). An Intrinsic Human Physical Activity Recognition from Fused Motion Sensor Data using Bidirectional Gated Recurrent Neural Network in Healthcare. In Proceedings of the 19th International Conference on Smart Business Technologies - ICSBT; ISBN 978-989-758-587-6; ISSN 2184-772X, SciTePress, pages 26-32. DOI: 10.5220/0011296400003280

@conference{icsbt22,
author={Okeke Stephen. and Samaneh Madanian. and Minh Nguyen.},
title={An Intrinsic Human Physical Activity Recognition from Fused Motion Sensor Data using Bidirectional Gated Recurrent Neural Network in Healthcare},
booktitle={ Proceedings of the 19th International Conference on Smart Business Technologies - ICSBT},
year={2022},
pages={26-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011296400003280},
isbn={978-989-758-587-6},
issn={2184-772X},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Smart Business Technologies - ICSBT
TI - An Intrinsic Human Physical Activity Recognition from Fused Motion Sensor Data using Bidirectional Gated Recurrent Neural Network in Healthcare
SN - 978-989-758-587-6
IS - 2184-772X
AU - Stephen, O.
AU - Madanian, S.
AU - Nguyen, M.
PY - 2022
SP - 26
EP - 32
DO - 10.5220/0011296400003280
PB - SciTePress