Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions

arXiv cs.AI Papers
lightweight-cnn affective-touch classification soft-robotics embedded-ai deep-learning

Summary

This paper presents a lightweight 1D CNN framework for classifying affective touches in soft plush companions, achieving 75% test accuracy with a 13.2k-parameter model and proposing a hybrid heuristic-CNN inference pipeline for embedded deployment.

arXiv:2607.16196v1 Announce Type: new Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect. This study presents a complete open-source MATLAB-based framework for the development and validation of compact deep learning models for affective touch recognition in soft interactive companions. As a primary contribution, a diverse FAIR-compliant dataset of 1326 labelled gesture sequences collected from 25 participants spanning children, teenagers, and adults is made publicly available, providing a reusable resource for future research in affective touch recognition. Through systematic architecture and hyperparameter exploration across 468 CNN models, the study identifies compact dilated one-dimensional convolutional neural networks (1D CNNs) as the most effective solution, with a 13.2k-parameter model achieving 75% test accuracy and 85% mean leave-one-subject-out cross-validation accuracy. Theoretical inference-time analysis shows that quantized deployment requires 3.2 MMAC per window, compatible with 20 Hz real-time operation on the target microcontroller. PC-based real-time simulation with the physical toy streaming sensor data demonstrates that the CNN resolves subtle social touches that the previous heuristic system failed to detect, whereas high-force negative interactions are captured more reliably by trivial threshold-based logic. The resulting hybrid inference pipeline - instantaneous heuristic filtering followed by CNN-based nuanced gesture classification - is proposed as the embedded deployment strategy. The study demonstrates that emotionally meaningful, privacy-preserving touch interpretation is computationally feasible for direct embedding within soft therapeutic companions, with hardware integration addressed in a forthcoming study.
Original Article
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# Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions
Source: [https://arxiv.org/abs/2607.16196](https://arxiv.org/abs/2607.16196)
[View PDF](https://arxiv.org/pdf/2607.16196)

> Abstract:Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect\. This study presents a complete open\-source MATLAB\-based framework for the development and validation of compact deep learning models for affective touch recognition in soft interactive companions\. As a primary contribution, a diverse FAIR\-compliant dataset of 1326 labelled gesture sequences collected from 25 participants spanning children, teenagers, and adults is made publicly available, providing a reusable resource for future research in affective touch recognition\. Through systematic architecture and hyperparameter exploration across 468 CNN models, the study identifies compact dilated one\-dimensional convolutional neural networks \(1D CNNs\) as the most effective solution, with a 13\.2k\-parameter model achieving 75% test accuracy and 85% mean leave\-one\-subject\-out cross\-validation accuracy\. Theoretical inference\-time analysis shows that quantized deployment requires 3\.2 MMAC per window, compatible with 20 Hz real\-time operation on the target microcontroller\. PC\-based real\-time simulation with the physical toy streaming sensor data demonstrates that the CNN resolves subtle social touches that the previous heuristic system failed to detect, whereas high\-force negative interactions are captured more reliably by trivial threshold\-based logic\. The resulting hybrid inference pipeline \- instantaneous heuristic filtering followed by CNN\-based nuanced gesture classification \- is proposed as the embedded deployment strategy\. The study demonstrates that emotionally meaningful, privacy\-preserving touch interpretation is computationally feasible for direct embedding within soft therapeutic companions, with hardware integration addressed in a forthcoming study\.

## Submission history

From: Aleksandrs Vališevskis \[[view email](https://arxiv.org/show-email/83aea057/2607.16196)\] **\[v1\]**Thu, 16 Apr 2026 10:20:05 UTC \(1,133 KB\)

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