{"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/time-series-segmentation-applied-to-a-new","title":"Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities","arxiv_id":null,"date":"2023-03-28","proceeding":"Data Analytics solutions for Real-LIfe APplications 2023 3","authors":["Arik Ermshaus","Sunita Singh","Ulf Leser"],"abstract":"Human activity recognition (HAR) systems implement workflows that automatically detect activities from motion data,\r\ncaptured e.g. by wearable devices such as smartphones. These devices contain multiple sensors that record human motion as\r\nacceleration, rotation and orientation in long time series (TS) data. As a first step, HAR methods typically partition such\r\nrecordings into smaller subsequences before applying feature extraction and classification. In this study, we evaluate the\r\nperformance of 6 classical and recently published TS segmentation (TSS) algorithms on a new large HAR benchmark of 126\r\nTS with up to 13 different activities, called MOSAD, recorded with 6 participants using ordinary smartphone sensors. Our\r\nresults show that the ClaSP algorithm achieves significantly more accurate results compared to the other methods, scoring\r\nthe best segmentations in 57 out of 126 TS. The FLOSS algorithm also shows promising results, particularly for long TS with\r\nmany segments. MOSAD is freely available at https://github.com/ermshaua/mobile-sensing-human-activity-data-set.","url_abs":"https://ceur-ws.org/Vol-3379/DARLI-AP_2023_2.pdf","url_pdf":"https://ceur-ws.org/Vol-3379/DARLI-AP_2023_2.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":"time-series-segmentation-applied-to-a-new","repo_url":"https://github.com/ermshaua/mobile-sensing-human-activity-data-set","is_official":1,"mentioned_in_paper":1,"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":"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":[{"method_slug":"ts","method_name":"TS"}],"datasets_introduced":[{"slug":"mosad","name":"MOSAD","full_name":"Mobile Sensing Human Activity Data Set"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}