RESUMO
Human‐machine interfaces (HMIs) play important role in the communication between humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great promise in medical applications, especially during the pandemic of coronavirus disease 2019 (COVID‐19) to reduce the spread of virus. However, current touchless HMIs are mainly restricted by limited types of gesture recognition, the requirement of wearing accessories, complex sensing platforms, light conditions, and low recognition accuracy, obstructing their practical applications. Here, an intelligent noncontact gesture‐recognition system is presented through the integration of a triboelectric touchless sensor (TTS) and deep learning technology. Combined with a deep‐learning‐based multilayer perceptron neural network, the TTS can recognize 16 different types of gestures with a high average accuracy of 96.5%. The intelligent noncontact gesture‐recognition system is further applied to control a robot for collecting throat swabs in a noncontact mode. Compared with present touchless HMIs, the proposed system can recognize diverse complex gestures by utilizing charges naturally carried on human fingers without the need of wearing accessories, complicated device structures, adequate light conditions, and achieves high recognition accuracy. This system could provide exciting opportunities to develop a new generation of touchless medical equipment, as well as touchless public facilities, smart robots, virtual reality, metaverse, etc. [ FROM AUTHOR] Copyright of Advanced Functional Materials is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
RESUMO
Safety concerns about novel vaccines and necessity of COVID-19 vaccination for children, especially with underlying medical conditions, are the obstacle of COVID-19 vaccination program among pediatric population. The study was conducted to investigate the vaccine hesitancy reasons among the parents, and to monitor the adverse events of inactivated COVID-19 vaccines in children and teenagers with underlying medical conditions in China. Children with underlying medical conditions encountered to the Immunization Advisory Clinic for COVID-19 vaccine counseling were enrolled. They were given immunization recommendation and followed up at 72 h and 28 d after immunization to monitor the immunization compliance after consultation and adverse events. A total of 324 children aged 3-17 y were included. The top three primary medical conditions for counseling were allergy (33.6%), neurological diseases (31.2%) and rheumatic diseases (8.3%). COVID-19 vaccination was promptly recommended for 242 (74.7%) children. Seventy-one (65.7%) children who had allergy issues were recommend to take vaccination, which was significantly lower than that of other medical conditions (p < .05). The follow-up record showed that 180 children received 340 doses of inactivated COVID-19 vaccine after consultation. Overall, 39 (21.6%) children reported at least one adverse event within 28 d of either vaccination. No serious adverse reactions were observed. No difference of adverse effects between the first dose and the second dose of vaccination except fever. Parents' hesitancy in COVID-19 vaccination for children with underling medical conditions are mainly due to the safety concerns. Specialist consultation is helpful to improve the vaccine uptake.