Perez-Pozuelo Ignacio, Zhai Bing, Palotti Joao, Mall Raghvendra, Aupetit Michaël, Garcia-Gomez Juan M, Taheri Shahrad, Guan Yu, Fernandez-Luque Luis
1Department of Medicine, University of Cambridge, Cambridge, UK.
2The Alan Turing Institute, London, UK.
NPJ Digit Med. 2020 Mar 23;3:42. doi: 10.1038/s41746-020-0244-4. eCollection 2020.
In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.
近年来，用于监测身体活动、睡眠和昼夜节律的多模态传感器及技术在开发和应用方面有了显著扩展。这些进展首次使大规模的精确睡眠监测成为可能。大量的多传感器数据正在生成，其潜在应用范围广泛，从将睡眠模式与疾病联系起来的大规模流行病学研究，到健康应用，包括对慢性病患者的睡眠指导。然而，为了让这些技术在个人、医学和研究方面充分发挥潜力，必须克服几个重大挑战。在性能评估以及数据存储、管理、处理、整合、建模和解释方面，存在一些重要的悬而未决的问题。在此，我们利用神经科学、临床医学、生物工程、电气工程、流行病学、计算机科学、移动健康和人机交互等领域的专业知识，从跨学科角度探讨睡眠的数字化。我们介绍睡眠监测技术的最新进展，并讨论从数据采集到最终在临床和消费者环境中应用这些见解所面临的机遇和挑战。此外，我们还探讨了当前和新兴传感方法的优势与局限性，特别关注新型数据驱动技术，如人工智能。
