• Exploring the impact of mental workload and psychosocial factors on biomechanical responses in healthcare activities

This research aims to investigate whether higher levels of mental workload led to increased physical risk by affecting biomechanical responses. The objective is to establish a base for further exploration into the influence of perceived mental workload on physical demands and WMSDs. By examining the variations in biomechanical responses resulting from changes in mental workload due to modifications in the work environment and conditions, this study seeks to gain insights and enhance our understanding of how individuals negotiate between mental and physical demands when performing healthcare-related tasks. 

• Comparison of different weighting scales when measuring mental workload

The NASA Task Load Index (NASA-TLX) and the Surgery Task Load Index (SURG-TLX) are two tools used to subjectively assess mental workload. Both tools offer unweighted and weighted mental workload measurements. Weighted TLX assessments are infrequently employed by researchers due to their strong correlation with unweighted TLX measures and the additional time needed to collect pairwise weightings during experiments. The aim of this study is to compare weighted and unweighted TLX scores. Different weighting scales (weighted, unweighted, rating, and ranking) were used to estimate the TLX scores. Pearson correlations and paired t-test analysis were used to compare the unweighted and weighted TLX scores across tasks and conditions. The results will allow us to know if there is any significant difference in the scores between weighting alternatives; therefore, improving the process of selecting the appropriate scale when using the TLX surveys. The goal is to identify a technique that will produce accurate results but does not require too much time or 6 resources and especially will not generate fatigue, stress, or boredom on those filling out the surveys.

• Investigating the impact of emotions on the quality of novice programmers’ code

This study investigates how emotions affect the quality of code produced by novice programmers in an experimental setting using Python or Java. Using a non-invasive brain-computer interface, EEG signals are recorded from the brain to assess activations in the left and right prefrontal cortex, which are often associated with emotions. Code quality is evaluated using the Code-based Deep Knowledge Tracing method. Preliminary findings indicate that positive emotions may lead to lower-quality code among novice programmers. Further research could solidify this relationship and explore how negative emotions impact code quality, ultimately offering recommendations on how to manage programmers' mental states for optimal coding performance.

• Exploring Exoskeletons Applications in Nursing: A Preliminary Study on Usability and Biomechanics

The increasing physical demands of nursing, coupled with the high prevalence of work-related injuries and burnout, necessitate innovative solutions to support healthcare professionals. Exoskeletons, wearable devices designed to provide mechanical support and reduce physical strain, present a promising avenue for alleviating the physical toll of nursing tasks. This preliminary research project aims to evaluate the utility of exoskeletons across various nursing activities to identify their potential applications and limitations. The study will begin with an exploration of different exoskeleton designs and their adaptability to the diverse demands of nursing, emphasizing tasks requiring repetitive lifting, prolonged static postures, and high physical exertion. In addition to assessing usability, the project will evaluate the impact of exoskeleton use on muscle activity and body postures, focusing on their effectiveness in reducing musculoskeletal strain and promoting ergonomic practices. This evaluation will serve as a foundation for designing controlled experiments to assess the broader implications of exoskeleton use in healthcare, specifically within nursing environments. By establishing a detailed understanding of exoskeleton applications and their biomechanical impacts, this research aims to pave the way for tailored interventions that enhance nurses' productivity, reduce burnout, and improve job satisfaction, ultimately benefiting both healthcare workers and patient outcomes.

• The hybrid virtual nurse care model in rural acute care settings

This project explores the implementation of a hybrid virtual nurse care model in rural acute care settings, focusing on its impact on patient outcomes, efficiency, effectiveness, and access to care. By integrating virtual nursing support with traditional in-person care, we aim to address the unique challenges faced by rural healthcare facilities, such as limited resources and workforce shortages. Our research assesses how this model influences patient health outcomes, streamlines care processes, enhances the quality of care, and improve accessibility for patients in remote areas. Through comprehensive data analysis and stakeholder feedback, we strive to develop a sustainable and scalable approach that can significantly elevate the standard of healthcare in rural communities. This is an interdisciplinary initiative where professors and professionals from the College of Engineering, College of Computing and Software Engineering, College of Business, and WellStar Health System are working together with the objective of offering recommendations and insights to healthcare providers, administrators, and health IT companies interested in incorporating innovative approaches to increase access to care in rural settings.