{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:23:34Z","timestamp":1740201814705,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs). This method is particularly suitable for modeling complex joint distributions via latent variables. We also chose this specific procedure because it is a generative approach, i.e., artificial samples can be generated to explore the learned structure. We describe how the data can be preprocessed to be compatible with binary DBMs. The results reveal two distinct usage patterns in which one group frequently uses trackers on Mondays and Tuesdays, whereas the other uses trackers during the entire week. This exemplary result shows that DBMs are feasible and can be useful for modeling activity tracker data.<\/jats:p>","DOI":"10.3233\/978-1-61499-896-9-155","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T08:47:32Z","timestamp":1740127652000},"source":"Crossref","is-referenced-by-count":0,"title":["Modeling Activity Tracker Data Using Deep Boltzmann Machines"],"prefix":"10.3233","author":[{"family":"Treppner Martin","sequence":"additional","affiliation":[]},{"family":"Lenz Stefan","sequence":"additional","affiliation":[]},{"family":"Binder Harald","sequence":"additional","affiliation":[]},{"family":"Z&ouml;ller Daniela","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences: A Learning Healthcare System"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T09:15:04Z","timestamp":1740129304000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-895-2&spage=155&doi=10.3233\/978-1-61499-896-9-155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-896-9-155","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}