{"slug": "design-and-validation-of-a-lightweight-1d-cnn-for-affective-touch-classification", "title": "Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions", "summary": "Researchers developed a lightweight 1D CNN for affective touch classification in soft plush companions, achieving 75% test accuracy and 85% leave-one-subject-out cross-validation accuracy with a 13.2k-parameter model. The framework includes an open-source MATLAB-based pipeline and a FAIR-compliant dataset of 1,326 labeled gesture sequences from 25 participants. The model enables real-time, privacy-preserving touch interpretation on microcontrollers, with a hybrid heuristic-CNN inference pipeline proposed for embedded deployment.", "body_md": "arXiv:2607.16196v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/design-and-validation-of-a-lightweight-1d-cnn-for-affective-touch-classification", "canonical_source": "https://arxiv.org/abs/2607.16196", "published_at": "2026-07-21 04:00:00+00:00", "updated_at": "2026-07-21 04:06:08.116174+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/design-and-validation-of-a-lightweight-1d-cnn-for-affective-touch-classification", "markdown": "https://wpnews.pro/news/design-and-validation-of-a-lightweight-1d-cnn-for-affective-touch-classification.md", "text": "https://wpnews.pro/news/design-and-validation-of-a-lightweight-1d-cnn-for-affective-touch-classification.txt", "jsonld": "https://wpnews.pro/news/design-and-validation-of-a-lightweight-1d-cnn-for-affective-touch-classification.jsonld"}}