Time series segmentation (TSS) is a far too under-studied research field that plays a crucial role in many real-world data analysis workflows for energy consumption, climate change, or human activity recognition (HAR).

In a collaborative effort with 16 computer science students, we collected and annotated real-world human motion sensor data. We propose a TSS competition to partition the resulting 250 multi-variate TS into an a priori unknown amount of variable-sized activities. This could greatly increase the accuracy of HAR systems and would promote benchmarking in TSS.

This repository contains supporting materials, including the data set, Python data loaders and baselines for the challenge. The folder notebooks contains Jupyter notebooks to run the baselines on the challenge data. The challenge data set is stored in the datasets folder.

This competition is hosted by the ECML/PKDD 2023 conference as a Discovery Challenge. It is organized using the Kaggle platform starting 11th April and ending 11th June. The results and top three winning solutions will be presented at the AALTD workshop in September, 2023.

Please fill out this google form to register for the challenge. Upon registration, we will send you the invitation link to the Kaggle competition website via mail. Participants are strictly prohibited from registering multiple times to gain an unfair advantage. No pseudonyms may be used for registration.

In order to run the baselines, you have to install the packages from requirements.txt.

pip install -r requirements.txt

You will need to install the following packages with segmentation algorithms to run all baselines:

aeon==0.1.0rc0, claspy, ruptures, stumpy

We collected a data set of 250 annotated twelve-dimensional multivariate TS, for a total of 10.7 hours, sampled at 50 Hertz (Hz). These TS contain between seven seconds and fourteen minutes - the median is median 100 seconds - of human motion data, capturing one to fifteen potentially recurring activities from a total of one hundred different ones, each lasting from half a second (for waiting) to ten minutes (for running). The data in this repository does not contain any labels. You may explore the data with the provided exploration.ipynb notebook.

The following two images show examples of the first two time series in the data set.

We provide code for six baseline codes in the `notebooks folder:

• Binary-Segmentation from ruptures
• ClaSP from claspy
• FLUSS from stumpy
• GreedyGaussianSegmentation (GGS) from aeon
• InformationGainSegmentation (IGTS) from aeon
• STRAY from aeon

If we run ClaSP, FLUSS and BinSeg baselines on the y-axis acceleration channel of TS0, we get the following segmentation.

If you use the challenge data in your publication, please cite us as:

@misc{segmentation_challenge,
	author = {Ermshaus, Arik, and Sch{\"a}fer, Patrick, and Leser, Ulf and  Bagnall, Anthony and Tavenard, Romain and Leverger, Colin and Lemaire, Vincent and Malinowski, Simon and Guyet, Thomas and Ifrim, Georgiana},
	howpublished = {ECML/PKDD 2023 Discovery Challenge},
	title = {Human Activity Segmentation Challenge},
	month = {April},
	year = {2023}
}

1. tslearn
2. aeon