{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/human-activity-segmentation-challenge-ecml","title":"Human Activity Segmentation Challenge @ ECML/PKDD’23","arxiv_id":null,"date":"2023-09-18","proceeding":"Advanced Analytics and Learning on Temporal Data 2023 9","authors":["Arik Ermshaus","Patrick Schäfer","Anthony Bagnall","Thomas Guyet","Georgiana Ifrim","Vincent Lemaire","Ulf Leser","Colin Leverger","Simon Malinowski"],"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.","url_abs":"https://doi.org/10.1007/978-3-031-49896-1_1","url_pdf":"https://link.springer.com/content/pdf/10.1007/978-3-031-49896-1_1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"human-activity-segmentation-challenge-ecml","repo_url":"https://github.com/patrickzib/human_activity_segmentation_challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"change-point-detection","task_name":"Change Point Detection"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[{"slug":"hascd","name":"HASCD","full_name":"Human Activity Segmentation Challenge Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}