Sleep is the primary mean of recovery from accumulated fatigue and thus plays a crucial role in fostering people's mental and physical well-being. Sleep quality monitoring systems are often implemented using wearables that leverage their sensing capabilities to provide sleep behaviour insights and recommendations to users. Building models to estimate sleep quality from sensor data is a challenging task, due to the variability of both physiological data, perception of sleep quality, and the daily routine across users. This challenge gauges the need for a comprehensive dataset that includes information about the daily behaviour of users, physiological signals as well as the perceived sleep quality. In this paper, we try to narrow this gap by proposing Bilateral Heart rate from multiple devices and body positions for Sleep measurement (BiHeartS) dataset. The dataset is collected in the wild from 10 participants for 30 consecutive nights. Both research-grade and commercial wearable devices are included in the data collection campaign. Also, comprehensive self-reports are collected about the sleep quality and the daily routine.

翻译：睡眠是恢复累积疲劳的主要途径，因此对促进人们的身心健康起着至关重要的作用。睡眠质量监测系统通常利用可穿戴设备，通过其传感能力为用户提供睡眠行为洞察和建议。由于生理数据、睡眠质量感知以及用户日常习惯的变异性，构建从传感器数据估算睡眠质量的模型是一项具有挑战性的任务。这一挑战凸显了对涵盖用户日常行为、生理信号以及感知睡眠质量的综合数据集的需求。在本文中，我们通过提出用于睡眠测量的双侧心率（Bilateral Heart rate from multiple devices and body positions for Sleep measurement, BiHeartS）数据集来尝试缩小这一差距。该数据集在自然环境下从10名参与者中连续收集30个夜晚。数据收集活动同时包含了研究级和商业级可穿戴设备。此外，还收集了关于睡眠质量和日常习惯的全面自我报告。