  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.

Artificial Intelligence;Human-Computer Interaction