Drivers' Comprehensive Emotion Recognition Based on HAM.

Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver's complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers' comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers' comprehensive emotion recognition. By combining the three modalities of a driver's voice, facial image, and video sequence, the six classification tasks of drivers' emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers' negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.

The impact of COVID-19 pandemic on hand hygiene compliance of healthcare workers in a tertiary hospital in East China.

Introduction: Hand hygiene is a cost-effective measure to reduce healthcare-associated infections (HAIs) in healthcare facilities. The impact of the coronavirus disease 2019 (COVID-19) pandemic on hand hygiene performance (HHP) provided evidence for targeted hand hygiene intervention measures. Methods: This study evaluated the HHP rate in a tertiary hospital before and after the COVID-19 outbreak. HHP was checked by infection control doctors or nurses every day, and they inputted the HHP rate to the full-time infection control staff every week. A random examination of HHP was conducted by a confidential worker every month. The HHP of healthcare workers (HCWs) was monitored in the outpatient department, inpatient ward, and operating room from January 2017 to October 2022. The influence of COVID-19 prevention and control strategies on HHP was elucidated by analyzing the results of HHP during the study period. Results: The average HHP rate of HCWs was 86.11% from January 2017 to October 2022. The HHP rate of HCWs after the COVID-19 pandemic was statistically significantly higher than that before the pandemic (P < 0.001). The HHP rate was the highest (93.01%) in September 2022 when the local epidemic occurred. Among the different occupation categories, medical technicians showed the highest HHP rate (89.10%). The HHP rate was the highest after contact with body fluids or blood of patients (94.47%). Conclusion: The HHP rate of HCWs in our hospital showed an increasing trend in the recent 6 years, especially during the COVID-19 pandemic, and the increase was most obvious during the local epidemic.