Logo image
Back
Minimalist Neural Networks for Gesture Recognition on Wearable Capacitive Touch Textiles With Comparative User Study
Conference proceeding   Open access

Minimalist Neural Networks for Gesture Recognition on Wearable Capacitive Touch Textiles With Comparative User Study

Daniel Schwartz, Lev A Saunders, Nathalia Gomez, Yusuf Osmanlioglu, Richard Vallett, Genevieve Dion and Ali Shokoufandeh
Proceedings of the 2025 ACM Symposium on Spatial User Interaction, pp 1-11
10 Nov 2025
DOI: https://doi.org/10.1145/3694907.3765936
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.1145/3694907.3765936View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0,  Open

Abstract

Computing methodologies -- Classification and regression trees Computing methodologies -- Neural networks Computing methodologies -- Supervised learning General and reference -- Evaluation Hardware -- Sensor devices and platforms Hardware -- Tactile and hand-based interfaces Human-centered computing -- Empirical studies in HCI Human-centered computing -- Gestural input Human-centered computing -- Ubiquitous and mobile devices
Smart textiles embedded with capacitive touch sensors offer significant potential for intuitive gesture-based interaction, yet recognizing complex gestures on resource-constrained wearable devices remains challenging. This paper presents a minimalist neural network architecture specifically optimized for knitted capacitive touch interfaces. Our approach efficiently recognizes single and multi-touch gestures including taps, swipes, and pinches with accuracy exceeding 90% on training data and 80% on testing data. To assess real-world usability, we conducted a comparative user study evaluating participant performance with our knitted interface against a conventional trackpad in a gesture-controlled gaming scenario. Results demonstrated comparable overall performance between both interfaces, with participants achieving similar game scores despite the novelty of the textile interface. Statistical analysis revealed rapid user adaptation to the textile interface, with performance stabilizing after initial trials while the standard trackpad showed continuous improvement throughout testing. Quantitative metrics were supplemented by qualitative feedback highlighting the comfort and tactile appeal of the textile interface. This work advances the practical deployment of smart textile gesture recognition systems by addressing both technical performance requirements and usability considerations for real-world applications.

Metrics

13 Record Views

Details

Logo image