Human Performance Engineering

Ph.D. students are challenged to discover new ideas, invent new products or break through barriers on existing problems. As a Ph.D. student I am leading a new area of research in the STEM discipline. As an industrial engineer, I am attempting to extend the reach of engineering methods and tools traditionally applied in manufacturing and service-related settings to the area of human performance. Human Performance Engineering, IE 402 008, is a new creative inquiry class that Dr. Kevin Taaffe and I have created. The research includes many focus areas such as quality, decision making, perception, game theory, biology, simulation, and disciplines from engineering to psychology to management and the sciences can all potentially play a role. For the last two semesters I have guided undergraduate students in investigating the cause and effect relationships in human performance in individual or team sports. As a research group, we are challenged to learn materials that are beyond our current knowledge base and to examine psychological and biological factors that affect decisions people make in a competitive environment. Moreover, we aim to quantify the extent to which changes to our mental and physical abilities translate into an increased performance during the sporting event

Healthcare Facility Evacuations: Lessons Learned, Research Activity, and the Need for Engineering Contributions

Over the past few years, there has been an increase in research related to a healthcare facility's role during a disaster. Most of this literature relates to emergencies where the facility is a resource to the affected population, and the facility must make decisions associated with sudden, increased patient demands. Some emergencies, however, may affect the facility's ability to function and may therefore force the need for a complete patient evacuation. This paper provides an overview of the available literature including lessons learned from actual healthcare facility evacuations and research focusing on making improvements. The purpose is to summarize a variety of healthcare evacuation issues and highlight the research in this area. We raise questions for further research and conclude with an example of using engineering techniques to improve healthcare facility evacuations by prioritizing patients for transport

Evaluation of Google’s Voice Recognition and Sentence Classification for Health Care Applications

This study examined the use of voice recognition technology in perioperative services (Periop) to enable Periop staff to record workflow milestones using mobile technology. The use of mobile technology to improve patient flow and quality of care could be facilitated if such voice recognition technology could be made robust. The goal of this experiment was to allow the Periop staff to provide care without being interrupted with data entry and querying tasks. However, the results are generalizable to other situations where an engineering manager attempts to improve communication performance using mobile technology. This study enhanced Google’s voice recognition capability by using post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy). The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual’s voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that engineering managers could significantly enhance Google’s voice recognition technology by using post-processing techniques, which would facilitate its use in health care and other applications