No abstract

Purpose COVID-19 hits every country’s health-care system and economy. There is a trend toward using automation technology in response to the COVID-19 crisis not only in developed countries but also in those with lower levels of technology development. However, current studies mainly focus on the world level, and only a few ones report deployments at the country level. The purpose of this paper is to investigate the use of automation solutions in Vietnam with locally available materials mainly in the first wave from January to July 2020. Design/methodology/approach The authors collected COVID-related automation solutions during the first wave of COVID-19 in Vietnam from January to July 2020 through a search process. The analysis and insights of a panel consisting of various disciplines (i.e. academia, health care, government, entrepreneur and media) aim at providing a clear picture of how and to what extent these solutions have been deployed. Findings The authors found seven groups of solutions from low to high research and development (R&D) levels deployed across the country with various funding sources. Low R&D solutions were widely spread owing to simplicity and affordability. High R&D solutions were mainly deployed in big cities. Most of the solutions were deployed during the first phases when international supply chains were limited with a significant contribution of the media. Higher R&D solutions have opportunities to be deployed in the reopening phase. However, challenges can be listed as limited interdisciplinary research teams, market demand, the local supporting industry, end-user validation and social-ethical issues. Originality/value To the authors’ best knowledge, this is the first study analyzing the use of automation technology in response to COVID-19 in Vietnam and also in a country in Southeast Asia. Lessons learned from these current deployments are useful for future emerging infectious diseases. The reality of Vietnam’s automation solutions in response to COVID-19 might be a reference for other developing countries with similar social-economic circumstances and contributes to the global picture of how different countries adopt technology to combat COVID-19.

The camera mounted on unmanned Aerial Vehicles (UAVs) for sea surface evaluation has recently attracted attention because it is flexible for moving and easy to use. However, it is known that the absence of measurement occurs when image quality is strongly affected by sun glitter. In this study, we develop a method to evaluate wave propagation from different oblique aerial video images. Ortho-images were created from video frames by using the collinearity equation and perspective transform. Time-averaged images were created by the average of frames during the time after that the grayscale images were calculated. The relationship between the time-averaged grayscale image and light environment was investigated by considering the reflection properties of the sea surface and the radiance distribution of incident light. This model emphasized the best observation angle. The significant wave period and the wave celerity can be evaluated from the gray-scale images. The wave spectrum, wave direction, and long-component wavelength were handled by using 2D Fourier transform and low-pass filter. As a result, applying the present method to data acquired in the Tateyama coast, the wave propagation characteristic was successfully evaluated.

Background: Previous studies have investigated technology-aided needling training systems for acupuncture on phantom models using various measurement techniques. In this study, we developed and validated a vision-based needling training system (noncontact measurement) and compared its training effectiveness with that of the traditional training method. Methods: Needle displacements during manipulation were analyzed using OpenCV to derive three parameters, i.e., needle insertion speed, needle insertion angle (needle tip direction), and needle insertion length. The system was validated in a laboratory setting and a needling training course. The performances of the novices (students) before and after training were compared with the experts. The technology-aided training method was also compared with the traditional training method. Results: Before the training, a significant difference in needle insertion speed was found between experts and novices. After the training, the novices approached the speed of the experts. Both training methods could improve the insertion speed of the novices after 10 training sessions. However, the technology-aided training group already showed improvement after five training sessions. Students and teachers showed positive attitudes toward the system. Conclusion:The results suggest that the technology-aided method using computer vision has similar training effectiveness to the traditional one and can potentially be used to speed up needling training.

Nghiên cứu trình bày mô hình hỗ trợ giảng dạy thực hành châm cứu cho sinh viên ngành Y học dân tộc bằng kỹ thuật thị giác máy tính. Mô hình gồm một máy ảnh thương mại, một máy tính nhúng Raspberry Pi có thể kết nối tới màn hình máy tính. Phần mềm được thiết kế bằng Python trên nền tảng thư viện OpenCV, trên hệ điều hành Ubuntu.  Hệ thống có thể đo được góc châm, độ sâu của kim và vận tốc khi châm. Kết quả cho thấy hệ thống cho độ chính xác cao với các sai số nhỏ. Nghiên cứu đã được hội đồng khoa học trường đại học Y Dược Cần Thơ thông qua và đang được sử dụng giảng dạy để đánh giá ưu điểm của thiết bị so với phương pháp giảng dạy truyền thống.