Abstract
The detection of change points in multivariate signal without access to annotated data is a challenging task. The fully unsupervised approach requires the development of a robust algorithm that can effectively identify unknown a priori patterns. In this article we will present one of the solutions to “Human Activity Segmentation Challenge” ECML/PKDD’23 [4] where the task was to predict the offsets of activity transitions for multivariate time series. The described solution won the first place.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aminikhanghahi, S., Cook, D.J.: Using change point detection to automate daily activity segmentation. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, pp. 262–267 (2017). https://doi.org/10.1109/PERCOMW.2017.7917569
Ermshaus, A., Schäfer, P., Leser, U.: Clasp: parameter-free time series segmentation. Data Min. Knowl. Disc. 37, 1262–1300 (2023). https://doi.org/10.1007/s10618-023-00923-x
Ermshaus, A., Schäfer, P., Leser, U.: Window size selection in unsupervised time series analytics: a review and benchmark. In: Guyet, T., Ifrim, G., Malinowski, S., Bagnall, A., Shafer, P., Lemaire, V. (eds.) Advanced Analytics and Learning on Temporal Data: 7th ECML PKDD Workshop, AALTD 2022, Grenoble, 19–23 September 2022, Revised Selected Papers, pp. 83–101. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-24378-3_6
Ermshaus, A., et al.: Human activity segmentation challenge. In: ECML/PKDD 2023 Discovery Challenge (2023)
Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et du jura. Bull. Soc. Vaud. Sci. Nature 37, 547–579 (1901). https://doi.org/10.5169/SEALS-266450
Rauhameri, A., et al.: A comparison of online methods for change point detection in ion-mobility spectrometry data. Array 14, 100151 (2022). https://doi.org/10.1016/j.array.2022.100151
Truong, C., Oudre, L., Vayatis, N.: Selective review of offline change point detection methods. Signal Process. 167, 107299 (2020). https://doi.org/10.1016/j.sigpro.2019.107299
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Harańczyk, G. (2023). Change Points Detection in Multivariate Signal Applied to Human Activity Segmentation. In: Ifrim, G., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2023. Lecture Notes in Computer Science(), vol 14343. Springer, Cham. https://doi.org/10.1007/978-3-031-49896-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-49896-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49895-4
Online ISBN: 978-3-031-49896-1
eBook Packages: Computer ScienceComputer Science (R0)
