A Novel Safety Evaluation Approach of Transfer Interaction based on Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals

Monitoring human-robot interaction (HRI) process of people with mobility inconvenience when to employ multi-robots is an effective approach to mitigate human error and enhance safety. However, the interaction process of transfer that occurs frequently in daily life has not been studied. In this study, we proposed a comprehensive transfer monitoring method according to the mental states monitoring by functional Near-InfraRed Spectroscopy (fNIRS) and subjective workload questionnaire. 7 subjects were to perform transfer behavior under two contrasted levels (self-rising transfer vs. assisted-rising transfer by welfare-robots). After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness, and kurtosis-were calculated. All possible 2-and 3-feature combinations of the calculated features were then used to classify self-rising transfer vs. assisted-rising transfer by linear discriminant analysis (LDA). Then, we established the Subj ective Workload Assessment System (SWAS) for 2 subj ects, which was used to make an evaluation for HRI. The experiment results demonstrated that the optimal feature-combination selection by mean and peak values, which provided a sound theoretical basis for distinguishing mental states of transfer tasks. The SWAS effectively quantify the mental states and physical load of users when to implement multiply welfare-robots. It was contributed to guide the motion planning of multiply welfare-robots for the ultimate goal of safe transfer, and also could be applied in similar scenarios.