Titel
Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
Autor*in
Olena Pavliuk
Department of Distributed Systems and Informatic Devices, Silesian University of Technology
Autor*in
Myroslav Mishchuk
Department of Automated Control Systems, Lviv Polytechnic National University
Abstract
Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthcare, sports, and human activity monitoring. Convolutional neural networks (CNN) are becoming a popular approach for addressing HAR problems. However, this method requires extensive training datasets to perform adequately on new data. This paper proposes a novel deep learning model pre-trained on scalograms generated using the continuous wavelet transform (CWT). Nine popular CNN architectures and different CWT configurations were considered to select the best performing combination, resulting in the training and evaluation of more than 300 deep learning models. On the source KU-HAR dataset, the selected model achieved classification accuracy and an F1 score of 97.48% and 97.52%, respectively, which outperformed contemporary state-of-the-art works where this dataset was employed. On the target UCI-HAPT dataset, the proposed model resulted in a maximum accuracy and F1-score increase of 0.21% and 0.33%, respectively, on the whole UCI-HAPT dataset and of 2.82% and 2.89%, respectively, on the UCI-HAPT subset. It was concluded that the usage of the proposed model, particularly with frozen layers, results in improved performance, faster training, and smoother gradient descent on small HAR datasets. However, the use of the pre-trained model on sufficiently large datasets may lead to negative transfer and accuracy degradation.
Stichwort
biomedical signal processinghuman activity recognitionconvolutional neural networkscontinuous wavelet transformtransfer learning
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:1924419
Erschienen in
Titel
Algorithms
Band
16
Ausgabe
2
ISSN
1999-4893
Erscheinungsdatum
2023
Verlag
MDPI AG
Erscheinungsdatum
2023
Zugänglichkeit
Rechteangabe
© 2023 by the authors

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