2026-07-21 04:00 UTCOriginal source2 min readUpdated: 2026-07-21 08:14 UTC

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

This study presents an open-source MATLAB framework for compact deep learning models for affective touch recognition in soft interactive companions. After exploring 468 CNN models, a 13.2k-parameter dilated 1D CNN achieved 75% test accuracy and 85% leave-one-subject-out cross-validation accuracy. A hybrid pipeline combining heuristic filtering and CNN classification enables real-time 20 Hz operation on a microcontroller, enabling privacy-preserving emotional touch interpretation.

SourcearXiv AIAuthor: Aleksandrs Vali\v{s}evskis, Aleksandrs Okss, Inese T\=i\c{g}ere, Aleksejs Kata\v{s}evs, Dina Bethere, Anete Hofmane, Airisa \v{S}teinberga, Und\=ine Gavri\c{l}enko, Santa Me\c{l}\c{k}e, Lucie Matou\v{s}kov\'a

-->

[Submitted on 16 Apr 2026]

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

View a PDF of the paper titled Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions, by Aleksandrs Vali\v{s}evskis and 9 other authors

View PDF

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.

Comments: 28 pages, 11 figures

Subjects:

Artificial Intelligence (cs.AI)

ACM classes: I.5.1; I.5.4; J.4

Cite as: arXiv:2607.16196 [cs.AI]

(or arXiv:2607.16196v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.16196

arXiv-issued DOI via DataCite

Submission history

From: Aleksandrs Vališevskis [view email] [v1] Thu, 16 Apr 2026 10:20:05 UTC (1,133 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions, by Aleksandrs Vali\v{s}evskis and 9 other authors

View PDF

view license

Current browse context:

cs.AI

new | recent | 2026-07

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)