UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions

• URL: http://arxiv.org/abs/2212.14731v2
• Date: Tue, 3 Jan 2023 15:43:24 GMT
• Title: UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions
• Authors: Asterios Bampakis, Sofia Yfantidou, Athena Vakali
• Abstract summary: UBIWEAR is an end-to-end framework for intelligent physical activity prediction. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error.
• Score: 3.4483987421251516
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions. To achieve this, we experiment with numerous machine learning and deep learning paradigms as a robust benchmark for physical activity prediction tasks. To train our models, we utilize, "MyHeart Counts", an open, large-scale dataset collected in-the-wild from thousands of users. We also propose a prescriptive framework for self-tracking aggregated data preprocessing, to facilitate data wrangling of real-world, noisy data. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error, proving the feasibility of the physical activity prediction task, and paving the way for future research.

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