Design and Embedded Validation of Compact ML Models for Affective Touch Classification in a Soft Interactive Companion

תקציר מקורי באנגליתarXiv:2607.16196v2 Announce Type: replace Abstract: Soft plush companions provide a safe and intuitive platform for affective human-robot interaction, but their deformable structure and distributed tactile signals make reliable gesture recognition difficult. This study presents a complete workflow for developing and validating compact affective-touch classifiers for an interactive plush companion. A newly collected dataset comprised 1,326 labelled recordings before curation, including interactions from 25 children, teenagers, and adults. Each classifier received 2.5-s windows containing ten capacitive channels and one accelerometer-magnitude channel. MATLAB supported acquisition, quality control, window generation, and a 468-run exploratory study of dilated one-dimensional convolutional ne