Roll Padding and WaveNet for Multivariate Time Series in Human Activity Recognition

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Rui Gonçalves (Author) Other; Fernando Lobo Pereira (Author) FEUP; Vitor Miguel Ribeiro (Author) Other; Ana Paula Rocha (Author) Other

Conference proceedings International

Title: Trends and Applications in Information Systems and Technologies - Volume 1, WorldCIST 2021, Terceira Island, Azores, Portugal, 30 March - 2 April, 2021.

Pages: 238-248

World Conference on Information Systems and Technologies, WorldCIST 2021

1 April 2021 through 2 April 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00T-ZR7

DOI: 10.1007/978-3-030-72657-7_23

Abstract (EN): Padding is a process used for the border treatment of data before the convolution operation in Convolutional Neural Networks. This study proposes a new type of padding designated by roll padding, which is conceived for multivariate time series analysis when using convolutional layers. The Human Activity Recognition raw time distributed dataset is used to train, test and compare four Deep Learning architectures: Long Short-Term Memory, Convolutional Neural Networks with and without roll padding, and WaveNet with roll padding. Two main findings are obtained: on the one hand, the inclusion of roll padding improves the accuracy of the basic standard Convolutional Neural Network and, on the other hand, WaveNet extended with roll padding provides the best performance result. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11