可穿戴计算机
医学
重症监护医学
大流行
2019年冠状病毒病(COVID-19)
呼吸频率
远程医疗
血压
心率
氧饱和度
远程医疗
疾病
计算机科学
内科学
传染病(医学专业)
医疗保健
嵌入式系统
化学
有机化学
氧气
经济
经济增长
作者
Wei Jiang,Sumit Majumder,Samarth Kumar,Sophini Subramaniam,Xiaohe Li,Ridha Khédri,Tapas Mondal,Mansour Abolghasemian,Imran Satia,M. Jamal Deen
出处
期刊:IEEE Reviews in Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:15: 61-84
被引量:45
标识
DOI:10.1109/rbme.2021.3069815
摘要
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.
科研通智能强力驱动
Strongly Powered by AbleSci AI