论文标题
快速体育锻炼建议：移动健康中有效的超参数学习
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
论文作者
论文摘要
可以通过在其移动设备上提出相关和及时的建议来支持用户采用健康的行为，例如定期体育锻炼。最近，已经发现增强学习算法对于学习提供建议的最佳背景是有效的。但是，这些算法不一定是为移动健康（MHealth）设置所带来的约束而设计的，即它们有效，域信息和计算负担得起。我们提出了一种用于在MHealth设置中提供体育活动建议的算法。使用域科学，我们制定了一种使用线性混合效应模型的上下文强盗算法。然后，我们介绍了一种程序，以有效地执行高参数更新，使用计算资源要比竞争方法要少得多。我们的方法不仅在计算上有效，而且还可以通过封闭形式的矩阵代数更新轻松实现，并且我们在速度和准确性分别表现出对最高99％和56％的最高速度和准确性的改进。
Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning algorithms have been found to be effective for learning the optimal context under which to provide suggestions. However, these algorithms are not necessarily designed for the constraints posed by mobile health (mHealth) settings, that they be efficient, domain-informed and computationally affordable. We propose an algorithm for providing physical activity suggestions in mHealth settings. Using domain-science, we formulate a contextual bandit algorithm which makes use of a linear mixed effects model. We then introduce a procedure to efficiently perform hyper-parameter updating, using far less computational resources than competing approaches. Not only is our approach computationally efficient, it is also easily implemented with closed form matrix algebraic updates and we show improvements over state of the art approaches both in speed and accuracy of up to 99% and 56% respectively.
