The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.

翻译：睡眠质量对人们的身心健康具有深远影响。睡眠不足者更易出现身心不适、活动受限、焦虑及疼痛等症状。近年来，活动监测与健康追踪类应用与设备呈现爆发式增长。从这些可穿戴设备采集的信号可用于研究和改善睡眠质量。本文利用体力活动与睡眠质量之间的关联关系，探索如何借助机器学习技术帮助人们改善睡眠。人体生物功能通常可划分为若干行为模式。通过对活动数据进行时间序列聚类，我们找到了与特定受试者最显著行为模式相关联的聚类中心。随后为每个聚类中的每种行为模式生成有助于优质睡眠的活动方案。这些活动方案将被输入活动推荐引擎，用于建议受试者在日常生活中进行从放松到高强度活动的组合安排。推荐内容进一步根据受试者的生活方式约束条件（如年龄、性别、身体质量指数（BMI）、静息心率等）进行个性化定制，其核心目标是提升当夜的睡眠质量。这将最终服务于更长期的健康目标，例如降低心率、改善整体睡眠质量等。