Abstract
The mobile ecosystem is rife with applications that aim for individuals to persue a more active and healthier lifestyle. Applications vary from simple diaries that track your weight, calorie intake or blood glucose values towards more advanced ones that offer health recommendations while monitoring your fitness levels during workouts and throughout the day. Leveraging machine learning techniques is a popular approach to recognize non-trivial activities, such as different types of sports. However, such applications face a time consuming training phase before they become practical. In this work, we report on our feasibility analysis of transfer learning as a way to apply learned models from one individual on another, and report on various feature variabilities that may jeopardize the applicability of transfer learning.
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Van Assche, M., Ramakrishnan, A., Preuveneers, D., Berbers, Y. (2013). Towards a Transfer Learning-Based Approach for Monitoring Fitness Levels. In: O’Grady, M.J., et al. Evolving Ambient Intelligence. AmI 2013. Communications in Computer and Information Science, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-319-04406-4_5
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DOI: https://doi.org/10.1007/978-3-319-04406-4_5
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