Performance evaluation of preschool companion robots supported by automated data collection and analysis methods
The research uses automated data collection and analysis to establish a comprehensive preschool companion robot inter-action model.Focus on tangible interaction design and consider the binocular stereoscopic vision measurement model.The results show that the FDMF denoising algorithm works best when the median variance in the frequency domain is 0.421.Expert scores reflect that the robot is good in education and personalized adaptation,with scores ranging from 85 to 97 points,with a minimum of 80 points for personalized adaptation and an overall maximum of 96 points.The overall user satisfaction score is above 80 points,especially in terms of interactive reaction time under the binocular stereoscopic vision measurement model.This provides an important reference for preschool education.
data collectionperformance analysispreschool educationcompanion robot
万方数据
维普
