Cosoli Gloria, Scalise Lorenzo, Poli Angelica, Spinsante Susanna
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica Delle Marche, via Brecce Bianche, 60131 Ancona, Italy.
Department of Information Engineering, Università Politecnica Delle Marche, via Brecce Bianche, 60131 Ancona, Italy.
Health Technol (Berl). 2021;11(3):673-675. doi: 10.1007/s12553-021-00540-y. Epub 2021 Mar 7.
Today, the use of wearable devices is continuously increasing with many different application fields. Their low-cost and wide availability make these devices proper instruments for long-term monitoring, potentially useful to detect physiological changes related to influenza or other viruses. The relevance of this aspect and the impact of such technology have become evident particularly in the last year, during COVID-19 emergency; (big) data from wearable devices (already worn by many citizens) together with artificial intelligence techniques gave birth to specific studies dedicated to quickly identify patterns discriminating between healthy and infected people. These evaluations are made on the basis of parameters measured by these devices, among which heart rate, physical activity, and sleep seem to play a dominant role. This could be extremely significant in terms of early detection and limit of contagion risk. However, there is still a lot of research to be conducted in terms of measurement accuracy, data management (privacy and security issues), and results exploitation, in order to reach an accurate and reliable solution helping the whole healthcare system particularly in epidemic events, such as the SARS-CoV-2 pandemic.
如今，可穿戴设备在众多不同应用领域的使用量持续增长。其低成本和广泛可得性使这些设备成为长期监测的合适工具，可能有助于检测与流感或其他病毒相关的生理变化。这方面的相关性以及此类技术的影响在去年新冠疫情期间尤为明显；可穿戴设备（许多市民已经佩戴）产生的（大量）数据与人工智能技术催生了专门用于快速识别区分健康人群和感染人群模式的特定研究。这些评估是基于这些设备测量的参数进行的，其中心率、身体活动和睡眠似乎起着主导作用。这在早期检测和限制传染风险方面可能极其重要。然而，在测量准确性、数据管理（隐私和安全问题）以及结果利用方面仍有大量研究要做，以便达成一个准确可靠的解决方案，尤其在诸如新冠疫情这样的疫情事件中帮助整个医疗系统。
