Since the outbreak of the novel coronavirus disease 2019 (COVID-19), the epidemic has gradually slowed down in various countries and people’s lives have gradually returned to normal. To monitor the spread of the epidemic, studies discussing the design of related healthcare information systems have been increasing recently. However, these studies might not consider the aspect of user-centric design when developing healthcare information systems. This study examined these innovative technology applications and rapidly built prototype systems for smart healthcare through a systematic literature review and a study of patient innovation. The design guidelines for the Smart Healthcare System (SHS) were then compiled through an expert review process. This will provide a reference for future research and similar healthcare information system development.

Today, the learning model of a single instructor interacting with many students that is applied in conventional classrooms forms a passive bottleneck. Moreover, the instructor cannot effectively record students’ individual learning histories for instantaneous and suitable feedback. To address these issues using a mobile learning system (MLS) the authors have applied the principles of human-computer interaction (HCI) to construct a usable MLS on a personal digital assistant (PDA) The research focuses on adaptive content delivery within the context of small-screen design. The authors utilized task analysis, a literature review, experimental design and approaches to small-screen design, and addressed potential issues with presentation in order to design a friendly PDA interface whose performance was analyzed with a postexperiment user satisfaction questionnaire. The results of this experiment show that the mobile-based classroom learning experience provides appropriate mobile tools to help students to become capable, self-reliant, self-motivated, and independent. The findings also demonstrate that variations among students’ preference for content are associated with differences in the display presentation mode, and that thelandscape interface is recommended for use in some contexts.

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.