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Sport Activity Classification Using Interlaced Multivariate Time Series Signals. / Matarmaa, Jarno; Dolganov, Anton.
Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 266-269.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Matarmaa, J & Dolganov, A 2023, Sport Activity Classification Using Interlaced Multivariate Time Series Signals. in Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 266-269, 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Екатеринбург, Russian Federation, 15/05/2023. https://doi.org/10.1109/USBEREIT58508.2023.10158886

APA

Matarmaa, J., & Dolganov, A. (2023). Sport Activity Classification Using Interlaced Multivariate Time Series Signals. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book (pp. 266-269). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT58508.2023.10158886

Vancouver

Matarmaa J, Dolganov A. Sport Activity Classification Using Interlaced Multivariate Time Series Signals. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 266-269 doi: 10.1109/USBEREIT58508.2023.10158886

Author

Matarmaa, Jarno ; Dolganov, Anton. / Sport Activity Classification Using Interlaced Multivariate Time Series Signals. Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 266-269

BibTeX

@inproceedings{a1f8ea47621b4f9090e562c3164f1dc0,
title = "Sport Activity Classification Using Interlaced Multivariate Time Series Signals",
abstract = "This study investigates performance of Time Series Classification (TSC) models in non-laboratory conditions created dataset which contains sport activities in three categories, such as biking, running, and other. The main challenge conducted is to convert multivariate data to univariate. There are several methods to conduct that transformation, but in this case, we develop feature interlacement wherein three features are combined into the same signal from three dimensional multivariate time series data for each sport activity file, producing so called interlaced multivariate signals. Five univariate TSC models from sktime API evaluated in analysis are Time Series Forest, Supervised Time Series Forest, Random Interval Spectral Forest, Random Interval, and Shapelet Transform classifiers. Interlaced multivariate signal data was successfully constructed from the raw dataset and applied to TSC models achieving 91% accuracies on average among the models. Based on the overall scores obtained, applied data transformation method is well applicable in retrospective sport activity classification (SAC) using univariate time series analysis algorithms.",
author = "Jarno Matarmaa and Anton Dolganov",
year = "2023",
month = may,
day = "15",
doi = "10.1109/USBEREIT58508.2023.10158886",
language = "English",
pages = "266--269",
booktitle = "Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) ; Conference date: 15-05-2023 Through 17-05-2023",

}

RIS

TY - GEN

T1 - Sport Activity Classification Using Interlaced Multivariate Time Series Signals

AU - Matarmaa, Jarno

AU - Dolganov, Anton

PY - 2023/5/15

Y1 - 2023/5/15

N2 - This study investigates performance of Time Series Classification (TSC) models in non-laboratory conditions created dataset which contains sport activities in three categories, such as biking, running, and other. The main challenge conducted is to convert multivariate data to univariate. There are several methods to conduct that transformation, but in this case, we develop feature interlacement wherein three features are combined into the same signal from three dimensional multivariate time series data for each sport activity file, producing so called interlaced multivariate signals. Five univariate TSC models from sktime API evaluated in analysis are Time Series Forest, Supervised Time Series Forest, Random Interval Spectral Forest, Random Interval, and Shapelet Transform classifiers. Interlaced multivariate signal data was successfully constructed from the raw dataset and applied to TSC models achieving 91% accuracies on average among the models. Based on the overall scores obtained, applied data transformation method is well applicable in retrospective sport activity classification (SAC) using univariate time series analysis algorithms.

AB - This study investigates performance of Time Series Classification (TSC) models in non-laboratory conditions created dataset which contains sport activities in three categories, such as biking, running, and other. The main challenge conducted is to convert multivariate data to univariate. There are several methods to conduct that transformation, but in this case, we develop feature interlacement wherein three features are combined into the same signal from three dimensional multivariate time series data for each sport activity file, producing so called interlaced multivariate signals. Five univariate TSC models from sktime API evaluated in analysis are Time Series Forest, Supervised Time Series Forest, Random Interval Spectral Forest, Random Interval, and Shapelet Transform classifiers. Interlaced multivariate signal data was successfully constructed from the raw dataset and applied to TSC models achieving 91% accuracies on average among the models. Based on the overall scores obtained, applied data transformation method is well applicable in retrospective sport activity classification (SAC) using univariate time series analysis algorithms.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85164906080

U2 - 10.1109/USBEREIT58508.2023.10158886

DO - 10.1109/USBEREIT58508.2023.10158886

M3 - Conference contribution

SP - 266

EP - 269

BT - Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

Y2 - 15 May 2023 through 17 May 2023

ER -

ID: 41990400