ABSTRACT
Medical professionals have started favoring the use of non-contact intravenous injection robots owing to their importance during the COVID-19 outbreak. However, there are currently few studies considering the robot's needle insertion angle, and most of the needle insertion operations are performed at a steep angle. This increases the rate of puncture failure, and sometimes causes significant pain in patients depending on their individual differences. Therefore, the intravenous injection of the dorsal hand is performed in this study to investigate the determination of the robot's needle insertion angle. with a focus on the optimization of the measurement data to ensure accuracy in the calculation of the needle insertion angle. First, the space point cloud of the needle insertion area on the dorsal hand is obtained by combining a monocular camera with the linear structured light scanning method , and the dorsal hand plane is obtained via fitting dorsal hand point clouds using the least squares method. During the calibration process for the linear structured light system , the measurement error is eliminated by formulating an error function and using the optimization method to iteratively solve it. Subsequently. the needle insertion angle is determined based on the obtained needle insertion area plane. Finally, experiments are conducted for the accuracy verification of the proposed method. Based on the experimental results, the average error in the optimized structured light plane position is approximately 0. 1 mm, and this serves as a foundation for subsequent automatic injection studies.
ABSTRACT
The outbreak of the COVID-19 has increased the demand for point-of-care testing ( POCT), and as the most indispensable tools for human beings at present, smartphones have great application potential in POCT. Smartphone-based POCT has the following unique advantages: ( 1) easy to operate and without the need for professional training;( 2) shorter wait times and quicker test results;( 3) low fabrication cost and convenient to use in limited-resource areas. Therefore, smartphone-based POCT is rapidly emerging as a potential alternative to traditional laboratory testing. Herein, we perform a comprehensive review of recent progress and applications of smartphone-based sensors in POCT for the past three years, which uses the tested objects ( body fluids, volatile organic compounds, vital signs) by POCT as the basis for classification, and combines with the current mainstream sensing strategies, including colorimetric, fluorescent, electrochemical technology, piezoelectric, pyroelectric, ultrasonic and photoelectric sensor, etc. We evaluate the performance and development potential of these sensors, in addition, the emerging technologies used in POCT are introduced, such as nanotechnology, flexible electronic devices, microfluidic technology, biodegradable technology, self- powered technology, multichannel detection and so on. Finally, current problems are summarized and the future development of the smartphone-based POCT is discussed.