{"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/unsupervised-motion-artifact-detection-in","title":"Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data","arxiv_id":"1707.08287","date":"2017-07-26","proceeding":null,"authors":["Yuning Zhang","Maysam Haghdan","Kevin S. Xu"],"abstract":"One of the main benefits of a wrist-worn computer is its ability to collect a\nvariety of physiological data in a minimally intrusive manner. Among these\ndata, electrodermal activity (EDA) is readily collected and provides a window\ninto a person's emotional and sympathetic responses. EDA data collected using a\nwearable wristband are easily influenced by motion artifacts (MAs) that may\nsignificantly distort the data and degrade the quality of analyses performed on\nthe data if not identified and removed. Prior work has demonstrated that MAs\ncan be successfully detected using supervised machine learning algorithms on a\nsmall data set collected in a lab setting. In this paper, we demonstrate that\nunsupervised learning algorithms perform competitively with supervised\nalgorithms for detecting MAs on EDA data collected in both a lab-based setting\nand a real-world setting comprising about 23 hours of data. We also find,\nsomewhat surprisingly, that incorporating accelerometer data as well as EDA\nimproves detection accuracy only slightly for supervised algorithms and\nsignificantly degrades the accuracy of unsupervised algorithms.","url_abs":"http://arxiv.org/abs/1707.08287v1","url_pdf":"http://arxiv.org/pdf/1707.08287v1.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":"unsupervised-motion-artifact-detection-in","repo_url":"https://github.com/IdeasLabUT/EDA-Artifact-Detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"artifact-detection","task_name":"Artifact Detection"},{"task_slug":"small-data","task_name":"Small Data Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}