Affective Computing in Healthcare

An emerging interdisciplinary field, affective computing focuses on developing smart devices and systems that recognize, interpret, and respond to human affective states. Healthcare is the most promising area of application for affective computing, where developments using biomedical signals have grown rapidly. Based on affective computing principles, intelligent real-time assistive and diagnosis devices are being developed using machine learning and deep learning algorithms. This book provides an introduction to affective computing and its applications in healthcare.

An overview of affective computing and affective computing models, artificial intelligence, probability theory, and statistical learning is presented in this comprehensive book. Following a review of biomedical signal acquisition and pre-processing for biomedical signals, topics such as noise elimination and baseline wandering effects are discussed. This book discusses biomedical signals in affective states and artificial intelligence-based methods of biomedical signal classification, including support vector machines and neural networks. In its conclusion, the book discusses recent research in neurodegenerative diseases and neurological disorders, as well as future challenges in these areas.

Researchers and industry professionals in affective computing and biomedical engineering will find this book useful, as it contains both fundamental concepts and recent applications.