Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs: 2nd Edition

Dear Colleagues,

Wearable sensors can be extremely useful in providing accurate and reliable information about people’s activities and behaviors. In recent times, there has been a surge in the usage of wearable sensors, especially in the medical sciences, where they have many applications in monitoring physiological activities. In the medical field, it is possible to monitor patients’ body temperature, heart rate, brain activity, muscle motion, and other critical data. It is important for us to have very simple sensors that could be worn on the body to perform standard medical monitoring. The extraction of relevant features is the most challenging part of the mobile and wearable-sensor-based human activity recognition pipeline. Feature extraction influences the algorithm’s performance and reduces computation time and complexity. The complexity and variety of body activities makes it difficult to quickly, accurately, and automatically recognize body activities. To solve this problem, Artificial Intelligence is becoming more and more important. Following the emergence of deep learning and increased computational power, these methods have been adopted for automatic feature learning in several areas such as health, image classification, and, recently, for feature extraction and the classification of simple and complex human activity recognition information from mobile and wearable sensors. Human activity recognition technology that analyzes data acquired from various types of sensing devices, including vision sensors and embedded sensors, has motivated the development of various context-aware applications in emerging domains, e.g., the Internet of Things (IoT) and healthcare.

The objective of this Special Issue is to collect state-of-the-art research contributions, tutorials, and position papers that address the broad challenges that have been faced in the development of wearable-sensor-based solutions in the field of human health. Original papers describing completed and unpublished work that are not currently under review by any other journal, magazine, or conference are solicited.

Prof. Dr. Eros Pasero
Dr. Vincenzo Randazzo
Guest Editors

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• wearable sensors
• electronic health
• telemedicine
• artificial intelligence
• machine learning
• deep neural networks
• human health
• vital signs

• Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs in Sensors (8 articles)