With the advances in smart wearables, internet of things (IoT), and big-data machine learning technologies in the past decade, continuous monitoring of human vital signs, such as electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, pulse, heart sound, respiration and temperature, has become feasible in many scenarios, offering enormous potential for applications to healthcare. It provides insight into not only the risk of disease but also the status of health in daily life.

Although a wealth of valuable medical information is hidden in the long-term dynamic vital signs, efficient signal analysis and health-monitoring deployment remain challenging, especially for the prediction of individual cardiovascular risk, as well as the evaluation of sleep and emotional health status. These studies will play an important role in healthcare-related domains as they can effectively help physicians and healthcare professionals make decisions regarding treatment options and well-being. Monitoring of human vital signs, such as timely and convenient oxygen saturation, respiration, and temperature, can also benefit the diagnosis of viral infections, such as COVID-19.

The aim of this Special Issue is to publish high-quality research as well as review articles addressing recent advances in continuous monitoring of human vital signs, dynamic signal processing, and big data-driven machine learning algorithms, and the incorporation of these technologies into healthcare applications. Original, high-quality contributions are welcome, with special emphasis on the following research topics.

Potential topics include but are not limited to the following:

• Signal quality assessment and control for human vital signs, especially wearable ECG, EEG, blood pressure, pulse, heart sound, respiration, etc.
• Monitoring of human vital signs for diagnosis of viral infections, especially for COVID-19
• Signal pre-processing and feature extraction methods for long-term vital signs
• Multimodality signal processing, machine learning, and artificial intelligence for long-term vital signs
• Assessment models for individual health risk based on signal and image processing (e.g., heart disease, hypertension, viral infection, skin cancer, bone fracture and brain tumour classification, etc.)
• Assessment models for individual health risk based on signal and image processing (e.g., heart disease, hypertension, viral infection, skin cancer, bone fracture and brain tumour classification, etc.)
• Assessments of sleep and emotional health based on long-term vital signs
• Human navigation, positioning, tracking, and action recognition using sensor and vision data (e.g., quantity of motion, fall, and abnormality detection)
• Developments of wearable devices for the monitoring of long-term vital signs
• Healthcare applications for the detection of chronic illnesses, sleep disorders, emotional problems, cognitive impairment and functional decline, viral infection, etc.