ArXiv Preprint
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.
Asterios Bampakis, Sofia Yfantidou, Athena Vakali
2022-12-30
