Development and Progress in Sensors and Technologies for Human Emotion Recognition.

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

COVID-19, Sensors, and Internet of Medical Things (IoMT)

Industry 4.0 is preparing to confront the difficulties arising due to the COVID-19 pandemic. These advances can provide automated and computer-assisted services for our day-to-day lives during this emergency. Different advantages of Industry 4.0 that can be conceived for alleviating impacts of COVID-19 pandemic are (i) manufacturing of prudent things identified with this infection,(ii) providing clinical assistance on time, utilizing the graceful chain, (iii) automating the clinical assistance and treatment to the infected patient to lessen the burden of specialists, (iv) learning from the experience and generate better machine learning models, (v) providing a few developments with the assistance of advance assembling and computerized innovations, and (vi) developing better hazard appraisal and worldwide general wellbeing crisis of this infection. This chapter provides details about the sensing systems used for healthcare in the view of COVID-19 crises.