In today's world, sleep quality is pivotal for overall well-being. While
wearable sensors offer real-time monitoring, they often lack actionable
insights, leading to user abandonment. This paper delves into the role of
technology in understanding sleep patterns. We introduce a two-stage framework,
utilizing Large Language Models (LLMs), aiming to provide accurate sleep
predictions with actionable feedback. Leveraging the GLOBEM dataset and
synthetic data from LLMs, we highlight enhanced results with models like
XGBoost. Our approach merges advanced machine learning with user-centric
design, blending scientific accuracy with practicality.

本文通过引入基于大型语言模型（LLM）的两阶段框架，结合 GLOBEM 数据集和 XGBoost 等模型，研究技术在理解睡眠模式方面的作用，以提供准确的睡眠预测和可行的反馈，从而改善智能可穿戴设备在睡眠监测中用户的参与度。

ZzzGPT: 提升睡眠质量的交互式 GPT 方法

ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

Sleep plays a vital role in our physical, cognitive, and psychological
well-being. Despite its importance, long-term monitoring of personalized sleep
quality (SQ) in real-world contexts is still challenging. Many sleep researches
are still developing clinically and far from accessible to the general public.
Fortunately, wearables and IoT devices provide the potential to explore the
sleep insights from multimodal data, and have been used in some SQ researches.
However, most of these studies analyze the sleep related data and present the
results in a delayed manner (i.e., today's SQ obtained from last night's data),
it is sill difficult for individuals to know how their sleep will be before
they go to bed and how they can proactively improve it. To this end, this paper
proposes a computational framework to monitor the individual SQ based on both
the objective and subjective data from multiple sources, and moves a step
further towards providing the personalized feedback to improve the SQ in a
data-driven manner. The feedback is implemented by referring the insights from
the PMData dataset based on the discovered patterns between life events and
different levels of SQ. The deep learning based personal SQ model (PerSQ),
using the long-term heterogeneous data and considering the carry-over effect,
achieves higher prediction performance compared with baseline models. A case
study also shows reasonable results for an individual to monitor and improve
the SQ in the future.

本文提出了一个基于多源客观和主观数据的计算框架来监测个体睡眠质量，并通过数据驱动的个性化反馈来提高睡眠质量。基于 PMData 数据集的洞察和不同生活事件及不同水平 SQ 之间的发现模式来实现反馈。使用深度学习模型 (PerSQ) 基于长期异构数据和考虑 carry-over effect，相较于基线模型，获得更高的预测性能。一项个案研究还表明个人可以通过该模型监测和改善其睡眠质量。

从长期生活日志监测和改善个性化的睡眠质量

Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs

The importance of sleep is paramount for maintaining physical, emotional and
mental wellbeing. Though the relationship between sleep and physical activity
is known to be important, it is not yet fully understood. The explosion in
popularity of actigraphy and wearable devices, provides a unique opportunity to
understand this relationship. Leveraging this information source requires new
tools to be developed to facilitate data-driven research for sleep and activity
patient-recommendations.
In this paper we explore the use of deep learning to build sleep quality
prediction models based on actigraphy data. We first use deep learning as a
pure model building device by performing human activity recognition (HAR) on
raw sensor data, and using deep learning to build sleep prediction models. We
compare the deep learning models with those build using classical approaches,
i.e. logistic regression, support vector machines, random forest and adaboost.
Secondly, we employ the advantage of deep learning with its ability to handle
high dimensional datasets. We explore several deep learning models on the raw
wearable sensor output without performing HAR or any other feature extraction.
Our results show that using a convolutional neural network on the raw
wearables output improves the predictive value of sleep quality from physical
activity, by an additional 8% compared to state-of-the-art non-deep learning
approaches, which itself shows a 15% improvement over current practice.
Moreover, utilizing deep learning on raw data eliminates the need for data
pre-processing and simplifies the overall workflow to analyze actigraphy data
for sleep and physical activity research.

本文探讨了利用深度学习建立基于 actigraphy 数据的睡眠质量预测模型，并使用卷积神经网络在原始穿戴式传感器输出上进行建模以提高睡眠质量的预测价值，使得睡眠和身体活动研究的数据预处理不再需要，简化了整个工作流程