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.

机器学习（ML）从业人员和组织正在建立预训练模型的模型动物园，其中包含元数据描述ML模型和数据集的属性，这些模型和数据集可用于报告，审计，可重复性和解释性目的。Metatada目前尚未标准化；它的表现力是有限的；并且没有可互操作的方法来存储和查询它。因此，阻碍了模型搜索，重用，比较和组成。在本文中，我们倡导标准化的ML模型元数据表示和管理，并提出了一个支持从业者管理和查询元数据的工具包。