Abstract
Time series segmentation (TSS) is a research problem that focuses on dividing long multivariate sensor data into smaller, homogeneous subsequences. This task is critical for various real-world data analysis applications, such as energy consumption monitoring, climate change assessment, and human activity recognition (HAR). Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data. To advance the field, we organized the Human Activity Segmentation Challenge at ECML/PKDD and AALTD 2023, featuring 57 participants. Collaborating with 15 bachelor computer science students, we gathered and annotated 10.7 h of real-world human motion sensor data. The challenge required participants to segment the resulting 250 multivariate time series into an unknown number of variable-sized activities. The top-8 approaches outperformed existing baselines, but show only limited improvements, capped at 1.9% points. The segmentation of real-world mobile sensing recordings remains challenging. We release the labelled challenge data for future research.
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Acknowledgements
We would like to thank Jonas Albrecht, Alexandria Arnold, Leo Baur, Malte Borgmann, Simon Bosse, Sinan Genc, Alina Hartwich, Isabel Heise, Jan Evert Hinrichs, Hoai Ngoc Ho, Malte Hückelkempkes, Wei Jin, Elida Sengül, Gerrit Slomma and Katharina Winde for their work in creating, recording and annotating the motion sequences for the challenge data.
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Ermshaus, A. et al. (2023). Human Activity Segmentation Challenge @ ECML/PKDD’23. 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_1
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DOI: https://doi.org/10.1007/978-3-031-49896-1_1
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