The aim of this study is to explore nurse activities leading to high cognitive stress for intensive care unit (ICU) nurses. The quantification of stress-related nurse activities has been acquired before and after the working shift, with a general gap in analyzing the whole shift. To address this gap, the task analysis of stress-related nurse activities during whole shift was conducted for two participants. The administration tasks (processing clinical data, exchanging professional information and ordering examination), and communicating with patients were most performed when under high cognitive stress. Future work should investigate the contribution of administration and patient-related tasks to high cognitive stress for ICU nurses.

Intensive care nurses are highly prone to occupational stress and burnout, affecting their physical and mental health. The occurrence of the pandemic and related events increased nurses’ workload and exacerbated stress and burnout. We conducted a prospective longitudinal mixed-methods study with a cohort of nurses working in a medical ICU (COVID unit; n = 14) and cardiovascular ICU (non-COVID unit; n = 5). Each participant was followed for six 12-hour shifts. Validated questionnaires measured occupational stress and burnout prevalence. Wrist-worn wearable technologies recorded physiological indices of stress. Participants elaborated on the contributors to stress via post-study questionnaire. Data were analyzed using statistical and qualitative methods. Participants who cared for COVID patients at the COVID unit were 3.71 times more likely to experience stress (p < .001) in comparison to non-COVID unit participants. No differences in stress levels were found when the same participants worked with COVID and non-COVID patients at different shifts at the COVID unit. The cohorts expressed similar contributors to stress including communication tasks, patient acuity, clinical procedures, admission processes, proning, labs, and assisting coworkers. Nurses in COVID units, irrespective of whether they care for a COVID patient, may experience high occupational stress and burnout.

The rapid proliferation of drones across various industries has introduced significant challenges related to privacy, security, and noise pollution. Current drone detection systems, primarily based on visual and radar technologies, face limitations under certain conditions, highlighting the need for effective acoustic-based detection methods. This paper presents a unique and comprehensive dataset of drone acoustic signatures, encompassing 32 different categories differentiated by brand and model. The dataset includes raw audio recordings, spectrogram plots, and Mel-frequency Cepstral Coefficient (MFCC) plots for each drone. Additionally, we introduce an interactive web application that allows users to explore this dataset by selecting specific drone categories, listening to the associated audio, and viewing the corresponding spectrogram and MFCC plots. This tool aims to facilitate research in drone detection, classification, and acoustic analysis, supporting both technological advancements and educational initiatives. The paper details the dataset creation process, the design and implementation of the web application, and provides experimental results and user feedback. Finally, we discuss potential applications and future work to expand and enhance the project.

The rapid proliferation of drones across various industries has introduced significant challenges related to privacy, security, and noise pollution. Current drone detection systems, primarily based on visual and radar technologies, face limitations under certain conditions, highlighting the need for effective acoustic-based detection methods. This paper presents a unique and comprehensive dataset of drone acoustic signatures, encompassing 32 different categories differentiated by brand and model. The dataset includes raw audio recordings, spectrogram plots, and Mel-frequency cepstral coefficient (MFCC) plots for each drone. Additionally, we introduce an interactive web application that allows users to explore this dataset by selecting specific drone categories, listening to the associated audio, and viewing the corresponding spectrogram and MFCC plots. This tool aims to facilitate research in drone detection, classification, and acoustic analysis, supporting both technological advancements and educational initiatives. The paper details the dataset creation process, the design and implementation of the web application, and provides experimental results and user feedback. Finally, we discuss potential applications and future work to expand and enhance the project.

OCCUPATIONAL APPLICATIONS Physical workload may lead to negative outcomes, including musculoskeletal disorders. In this study, we found that there were observable changes in facial features over the length of a low intensity, prolonged assembly task, and that these changes were correlated to other measures of physical workload. This method can be implemented by practitioners to evaluate physical workload. TECHNICAL ABSTRACT Background: Understanding physical workload is essential to avoid fatigue-related errors and to minimize injury risk. However, existing measures may not always be feasible for on-site, real-time, physical workload assessment, due to intrusiveness and interruption to the workers. Purpose: Determine whether changes in facial features are related to physical workload. Methods: Sixteen participants performed an assembly task with sustained postures until exhaustion in two separate sessions. Independent variables were time (first and last data window) and session. Dependent variables were ratings of perceived exertion (RPEs), electromyography (EMG), percent of heart rate reserve, and changes in facial features. Results: The sum of the movement path and distance of all selected facial landmarks in the regions of the eye, eyebrows, mouth, chin, and head increased significantly from the start to the end of the task. These features were significantly and positively correlated with other physical workload measures, such as RPEs, EMG, and percent of heart rate reserve. Conclusions: The observable facial changes related to physical workload are potential non-intrusive and non-disruptive indicators of physical workload.