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The highly infectious and serious nature of coronavirus disease 2019 (COVID-19) has highlighted the need for hospital space disinfection technology and the prevention of human exposure to pathogenic environments. This research developed novel chlorine dioxide (ClO2) sterilization technology to reduce bacteria and viruses in the air and on surfaces. A smart sterilization robot system was also developed to spray disinfectants in operating theaters or patients' rooms, designed according to the results of controlled experiments and the requirements for hospital disinfection. The system was built incorporated a semi-automatic remote-controlled module and an automatic intelligent disinfection function; that is, it could operate independently according to specific epidemic prevention strategies, which were implemented using a combination of Internet of Things (IoT) applications and a gesture recognition function. The elimination of Escherichia coli (E. coli) bacteria on sample plates was 99.8 % effective. This paper reviews the evolution of various disinfection technologies and describes a disinfection robot system in detail.

In order to mitigate the challenges of air pollution prevention and improvement, a large number of Internet of Things (IoT) related technologies have been developed to evaluate and monitor various parameters of air quality. This paper reviews the fundamental characteristics of IoT technologies and accordingly proposes an intelligent and multifunctional IoT monitoring platform to prevent and improve air pollution. The techniques of radio frequency identification (RFID), M2M and sensor network were discussed and compared. To improve the ambient air quality more efficiently, the comprehensive network communication system, cloud decision system, tracking information system, and online management system should be well established using IoT technologies. Meanwhile, we also discussed several cases verifying the availability and feasibility of the performance of the smart ambient air quality management platform on the IoT basis.

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This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.