{"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/pmdata-a-sports-logging-dataset","title":"PMData: A Sports Logging Dataset","arxiv_id":null,"date":"2022-07-08","proceeding":"MMSys’20 2022 7","authors":["Vajira Thambawita et al."],"abstract":"In this paper, we present data: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person’s weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes’ performance.PMDatacombines input from Fitbit Versa 2 smartwatch wristbands, thePMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.","url_abs":"https://dl.acm.org/doi/pdf/10.1145/3339825.3394926","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3339825.3394926","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":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"pmdata","name":"PMData","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}