论文标题
UBIWEAR：端到端，数据驱动的智能体育活动预测以增强MHealth干预措施的框架
UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions
论文作者
论文摘要
毫无疑问，体育活动对于个人的健康和健康至关重要。但是，全球身体不活动的普遍性引起了重大的个人和社会经济意义。近年来，大量工作展示了自我追踪技术创造积极健康行为改变的能力。这项工作是由个性化和自适应目标设定技术通过自我追踪鼓励体育锻炼的潜力的动机。为此，我们提出了Ubiwear，这是一个智能体育活动预测的端到端框架，其最终目标是增强数据驱动的目标设定干预措施。为了实现这一目标，我们试验了许多机器学习和深度学习范式，作为体育活动预测任务的强大基准。为了训练我们的模型，我们利用“ MyHeart Counts”，这是一个开放的大规模数据集，从数千名用户中收集了野外。我们还提出了一个规范性的框架，用于自我跟踪汇总数据预处理，以促进现实世界中嘈杂数据的数据争吵。我们的最佳模型实现了1087个步骤的MAE，就绝对错误而言，比艺术的状态低65％，证明了体育活动预测任务的可行性，并为未来的研究铺平了道路。
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.
