PersonalFit: Fitness App with intelligent plan generator
Pages 127 - 128
Abstract
The growth of the mobile app market and the fitness market led to some interesting projects combining both areas to be developed over the last decade. In this paper we present PersonalFit an intelligent and adaptive prototype that helps gym clients to manage and adapt their training plans in a dynamic and personalized way, envisaging the achievement of better results. Three main modules compose PersonalFit: communications, Android Application and an intelligent plan generator that implements clustering techniques.
References
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Jang, K. et al. 2015. Cloud Mat: Context-Aware Personalization of Fitness Content. 2015 IEEE International Conference on Services Computing. (2015).
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Buttussi, F. and Chittaro, L. 2008. MOPET: A context-aware and user-adaptive wearable system for fitness training. Artificial Intelligence in Medicine. 42, 2 (2008), 153--163.
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F. Buttussi and L. Chittaro, 'MOPET: A context-aware and user-adaptive wearable system for fitness training', Artificial Intelligence in Medicine, vol. 42, no. 2, pp. 153--163, 2008.
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Information & Contributors
Information
Published In
July 2016
152 pages
ISBN:9781450340755
DOI:10.1145/2948992
- Editor:
- Evan Desai,
- General Chair:
- Bipin C. Desai,
- Program Chairs:
- Ana Alameida,
- Jorge Bernardino
Copyright © 2016 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
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Published: 20 July 2016
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- Refereed limited
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C3S2E '16
C3S2E '16: Ninth International C* Conference on Computer Science & Software Engineering
July 20 - 22, 2016
Porto, Portugal
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Overall Acceptance Rate 12 of 42 submissions, 29%
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