{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:19:12Z","timestamp":1766067552549,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Regular physical activity has a positive impact on our physical and mental health. Adhering to a fixed physical activity regimen is essential for good health and mental wellbeing. Today, fitness trackers and smartphone applications are used to promote physical activity. These applications use step counts recorded by accelerometers to estimate physical activity. In this research, we performed a two-level clustering on a dataset based on individuals\u2019 physical and physiological features, as well as past daily activity patterns. The proposed model exploits the user data with partial or complete features. To include the user with partial features, we trained the proposed model with the data of users who possess exclusive features. Additionally, we classified the users into several clusters to produce more accurate results for the users. This enables the proposed system to provide data-driven and personalized activity planning recommendations every day. A personalized physical activity plan is generated on the basis of hourly patterns for users according to their adherence and past recommended activity plans. Customization of activity plans can be achieved according to the user\u2019s historical activity habits and current activity objective, as well as the likelihood of sticking to the plan. The proposed physical activity recommendation system was evaluated in real time, and the results demonstrated the improved performance over existing baselines.<\/jats:p>","DOI":"10.3390\/axioms11070346","type":"journal-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T11:22:24Z","timestamp":1658316144000},"page":"346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Dynamic Physical Activity Recommendation Delivered through a Mobile Fitness App: A Deep Learning Approach"],"prefix":"10.3390","volume":"11","author":[{"given":"Subramaniyaswamy","family":"Vairavasundaram","sequence":"first","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3752-7220","authenticated-orcid":false,"given":"Vijayakumar","family":"Varadarajan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia"},{"name":"School of NUOVOS, Ajeenkya DY Patil University, Pune 412105, India"}]},{"given":"Deepthi","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, India"}]},{"given":"Varshini","family":"Balaganesh","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, India"}]},{"given":"Srijith Bharadwaj","family":"Damerla","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, India"}]},{"given":"Bhuvaneswari","family":"Swaminathan","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, India"}]},{"given":"Logesh","family":"Ravi","sequence":"additional","affiliation":[{"name":"SENSE, Vellore Institute of Technology, Chennai 600127, India"},{"name":"Data Engineering Research Group, Vellore Institute of Technology, Chennai 600127, India"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"unstructured":"(2021, December 03). 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