Machine learning and pervasive sensing for sleep assessment and health

良好的睡眠是健康生活的基础。长期睡眠不足会对我们的生活产生各种负面影响，如白天嗜睡和认知功能受损，心脏病，高血压和II型糖尿病。许多现有的已发表论文集中在端到端的机器学习解决方案，包括自动睡眠-觉醒分类，睡眠障碍评估，这样的模型摄取原始信号数据，诸如多导睡眠图（PSG）或活动图（类似的消费产品，如Fitbit、Jawbone等）。作为生成最终预测的输入，以便区分睡眠或清醒状态。这些研究中的大多数都是基于单一模态数据源（如几个EEG通道）完成的。多模态融合是提高预测和分类精度的有吸引力的方法之一，例如包括心率、呼吸和肢体运动的加速度计数据。在过去的十年中，开源和商业可穿戴设备，身体传感器网络（BSN），环境和物联网技术已经很好地用于睡眠和日常活动研究。但在自由生活的环境中，长期准确监测睡眠-觉醒模式甚至睡眠阶段仍然是一项具有挑战性的任务。由于人类行为是由复杂的活动组成的，环境因素是非静态的，单一模态传感器数据很难准确地捕获所有活动。例如，基于WIFI和雷达的方法在单人睡眠监测场景中工作良好，但它们无法区分夫妻睡眠情况。多种异质感知方法可以提高信噪比、扩大参数覆盖范围、整合独立特征和先验知识，目的利用基于公共数据集的多模态数据融合和迁移学习方法，了解日常活动模式、生活方式和合并症如何影响人们的睡眠参数、阶段和主观感受（例如英国生物库，白色Hall II）。调查普适传感技术的采用情况（例如物联网设备）和开发的多模态框架，以鼓励公众行为的改变。方法博士工作的第一阶段将集中在算法的开发，模型和框架基于现有的睡眠和健康数据集，包括Fenland II数据集，英国生物银行和白厅II数据集，采用数值分析数学建模。该研究将以计算机视觉和机器学习的已发表成果为起点，第二阶段的博士研究将专注于采用普适传感技术和机器学习方法，开发一种廉价的可穿戴和可接近的系统，以更好地监测睡眠质量和持续时间。该研究将从传感器（音频、运动、温度、雷达等）收集多模态数据。以及通过招募的参与者获得的真实PSG睡眠数据。之后，第一阶段开发的框架将用于调查身体活动，睡眠，生活方式和非传染性疾病之间的关系。开发的系统将为研究人员，临床医生和最终用户提供机会，了解这些因素如何影响他们的健康，以鼓励睡眠行为和生活方式的改变。

A good night's sleep is the foundation for a healthy life. The insufficient long-term sleep can result in a various negative influence on our lives, such as daytime drowsiness and impaired cognitive function, heart disease, high blood pressure and type II diabetes.Many existing published papers focused on the end-to-end machine learning solutions that consist of automatic sleep-wake classification, sleep disorder assessment, etc. Such models intake the raw signal data such as the Polysomnography(PSG) or Actigraphy (similar consumer product like Fitbit, Jawbone etc.) as the inputs to generate the final predictions such as to distinguish sleep or wake status. Most of those studies were completed based on a single modality data source such as a few channels of EEG. The multimodality fusion is one of the appealing methods to improve the prediction and classification accuracy such as to include heart rate, respiration and limb movements' accelerometer data. In the past decade, open source and commercial wearables, Body sensor network (BSN), ambient and IoT technologies have been well used in sleep and daily activity research. But accurately monitoring sleep-wake pattern or even the sleep stages over a long period is still a challenging task in a free-living environment. Since human behaviour consists of complex activities and the environmental factors are non-static, it is difficult for a single modality sensor data to capture all activities accurately. For instance, the WIFI and Radar based method works well on a single person sleep monitoring scenario but they are unable to distinguish a couple sleep situation. Multiple heterogeneous sensing methods can improve Signal-to-Noise Ratio, extend parameter coverage and integrate the independent features and prior knowledge.AimsTo understand how daily activity pattern, lifestyle and comorbidities affect people's sleep parameters, stages and subjective feelings by utilising the multimodality data fusion and transfer learning method based on the public dataset (e.g. UK biobank, White Hall II).To investigate the adoption of pervasive sensing technology (e.g. IoT devices) and the developed multimodality framework to encourage behaviour change in public.MethodologyThe first stage of the PhD work will focus on the development of algorithm, models and frameworks based on existing sleep and health dataset that includes Fenland II Dataset, UK Biobank, and Whitehall II dataset by adopting the numerical analysis mathematical modelling. The study will consider the published work from computer vision and machine learning as the starting point.The second stage of the PhD work will focus on the adoption of pervasive sensing technology and machine learning method to develop an inexpensive wearable and nearable system to better monitoring sleep quality and duration. The study will collect multi-modality data from sensors (audio, movement, temperature, radar, etc.) and the ground truth PSG sleep data through the recruited participants. Afterwards, the first stage developed framework will be used to investigate the relationship between physical activities, sleep, lifestyle and non-communicable diseases The developed system will provide opportunities for researchers, clinicians and end users to understand how those factors will impact their health so as to encourage sleep behaviour and the change of lifestyle.