Human-Robot Interaction in Health Care: Focus on Human Factors

Abstract

In-patient and out-patient care processes in the health care system are strongly affected by the effects of demographic change and a tense skill shortage. Digitization - both as a digitizing transformation process and as a process of implementing technical innovations such as robots - is intended to counteract these processes. Not only for safe interaction with a care robot, but also for its acceptance, humans must be able to understand and predict the robot’s movements. The ability to make predictions about the movements of a robot/technology and to be willing to interact with it is linked to the construct of situation awareness. In an experimental setting, a care situation was simulated in the full-scope simulator and the effects of the interaction on the situation awareness and the acceptance of the robot were investigated. The experiment was realized with 33 participants. The methods used were the SAGAT technique and an adapted UTAUT questionnaire. Significant correlations between UTAUT predictors and added measures were found. A stepwise hierarchical regression model found Performance Expectancy and Attitude as significant predictors of acceptance, but none of the other UTAUT and added factors were identified to be significant predictors. There was no significant effect between situation awareness and acceptance. A descriptive analysis of the measures of situation awareness showed an average to good situation awareness across all participants.