Conference proceeding
A Minimal Neural Network for Reproducible Gesture Recognition on Knitted Capacitive Touch Sensors
Reproducible Research in Pattern Recognition, v 15705, pp 46-59
2026
Abstract
Smart textiles with embedded capacitive touch sensors (CTS) hold great promise for intuitive gesture-based human-computer interaction. However, recognizing complex gestures in real-time on resource-constrained wearable devices remains a challenge. This paper presents a novel approach using a minimalist neural network architecture tailored for efficient gesture recognition on knitted CTS. We emphasize reproducibility throughout our work by providing detailed algorithmic implementation, the influence of key parameters on result quality, and the integration of our source code into other frameworks. Our method demonstrates the ability to accurately classify a variety of single- and multi-touch gestures, including taps, swipes, and pinches, with accuracy rates exceeding 90% on both training and testing data. The proposed approach is well-suited for deployment on embedded devices and offers a significant step towards enabling natural and seamless interaction with smart textiles across diverse applications, such as healthcare, accessibility, and fashion. This work not only advances the field of smart textiles but also contributes to the broader goals of reproducibility in pattern recognition research, making it a valuable resource for further scientific exploration and validation. Code is available at https://github.com/dsbuddy/knitted-capacitive-touch-sensor-gesture-recognition.
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Details
- Title
- A Minimal Neural Network for Reproducible Gesture Recognition on Knitted Capacitive Touch Sensors
- Creators
- Daniel Schwartz - Drexel UniversityDario Salvucci - Drexel UniversityYusuf Osmanlioglu - Drexel UniversityRichard Vallett - Drexel UniversityLiam Ostrander - Drexel UniversityGenevieve Dion - Drexel UniversityAli Shokoufandeh - Drexel University
- Contributors
- Bertrand Kerautret (Editor)Federico Bolelli (Editor)Miguel Colom (Editor)Daniel Lopresti (Editor)
- Publication Details
- Reproducible Research in Pattern Recognition, v 15705, pp 46-59
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature ; Cham
- Number of pages
- 14
- Grant note
- U.S. Government: HQ00342190017
Effort sponsored by the U.S. Government under Other Transaction number HQ00342190017 between NextFlex and the Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Pennsylvania Fabric Discovery Center; Electrical and Computer Engineering; Fashion Design; Computer Science
- Web of Science ID
- WOS:001585222700004
- Scopus ID
- 2-s2.0-105015954988
- Other Identifier
- 991022084753404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods