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

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 neural networks (1D CNNs). A closely matched Python workflow then preserved participant provenance, fitted preprocessing inside each fold, and evaluated shortlisted models by 25-fold leave-one-subject-out cross-validation (LOSO-CV). On the operational 10-class task, the compact dilated CNN achieved 81.96% mean macro-F1 and 85.42% mean accuracy. The depthwise-separable CNN achieved the strongest neural result (84.50% macro-F1), whereas a linear support-vector machine using 66 predefined time-domain features achieved the best overall result (87.98% macro-F1 and 91.05% mean accuracy). Paired fold analysis showed that the linear support-vector machine and constrained random forest outperformed the compact dilated CNN, whereas differences among the tested neural models were not statistically significant after correction. Direct measurements on a 240-MHz ESP32-S3 confirmed valid deployment of the dilated CNN, depthwise-separable CNN, temporal convolutional network, linear support-vector machine, and random forest; the linear model required a 3.2 kB serialized payload and 1.405 ms mean end-to-end classifier time.