Data Analysis of Textile-Based Sensors Based on AI Algorithm
Main Article Content
Abstract
Textile-based sensors have emerged as key components for wearable healthcare, intelligent sports analytics, and industrial monitoring, yet their practical deployment remains constrained by electromagnetic interference, motion artifacts, nonlinear multimodal coupling, and limited computational efficiency in dynamic environments. This study proposes a hybrid AI framework integrating CNN– Transformer architectures to address these challenges through adaptive wavelet denoising, GAN-based data augmentation, and attention-guided multimodal feature fusion for heterogeneous sensing signals. A lightweight deployment strategy combining model pruning and quantization reduces computational overhead by 70%, enabling real-time inference with latency below 50 ms on edge devices. Experimental validation demonstrates that the proposed framework achieves 95.3% motion recognition accuracy using an 8 × 8 pressure sensor array, attains an AUC of 0.95 for respiratory anomaly detection under low signal-to-noise conditions, and predicts fabric remaining useful life with an RMSE of 6.2 hours through physics-informed learning. By improving robustness against environmental disturbances and enhancing multimodal information extraction, the proposed method provides an effective computational solution for intelligent textile sensing systems and offers methodological support for electromagnetic interference mitigation, wearable sensing networks, and advanced smart sensing platforms involving wireless signal acquisition and integrated electromagnetic environments.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
J. Huang, G. Xie, X. Xu, Z. Geng, and Y. Su, “Degradable multilayer fabric sensor with wide detection range and high linearity,” ACS Applied Materials & Interfaces, vol. 16, no. 43, pp. 58838-58847, 2024.
R. Bhisey, “Smart Textile Market to Expand at a CAGR of 25% from 2021 to 2031,” Open PR Worldwide Public Relations, Available online:https://www.openpr.com/news/2651325/smart-textile-market-to-expand-at-a-cagr-of-25-from-2021-to-2031(accessed on 8 August 2025), 2022.
M. Azeem, M. Shahid, I. Masin, and M. Petru, “Design and development of textile-based wearable sensors for real-time biomedical monitoring; a review,” The Journal of The Textile Institute, vol. 116, no. 1, pp. 80-95, 2025.
E. A. Jafleh, F. A. Alnaqbi, H. A. Almaeeni, S. Faqeeh, M. A. Alzaabi, K. Al Zaman, et al., “The role of wearable devices in chronic disease monitoring and patient care: A comprehensive review,” Cureus, vol. 16, no. 9, Art. no. e68921, 2024.
H. Chen, “Advancements in Human Motion Capture Technology for Sports: A Comprehensive Review,” Sensors & Materials, vol. 7, no. 36, pp. 2705-2726, 2024.
C. Wang, L. Fu, D. S. Ametefe, S. Wang, and D. John, “E-textiles in healthcare: A systematic literature review of wearable technologies for monitoring and enhancing human health,” Neural Computing and Applications, vol. 37, no. 4, pp. 2089-2111, 2025.
K. Yang, S. A. McErlain-Naylor, B. Isaia, A. Callaway, and S. Beeby, “E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities,” Sensors, vol. 24, no. 4, pp. 1058, 2024.
S. Xu, Q. Ma, X. F. Yang, and S. D. Wang, “Design and fabrication of a flexible woven smart fabric based highly sensitive sensor for conductive liquid leakage detection,” RSC Advances, vol. 7, no. 65, pp. 41117-41126, 2017.
W. Zhou, L. Zhang, J. Zhang, and S. Fu, “Textile electrodes for electrocardiogram monitoring,” Advanced Materials Technologies, vol. 10, no. 10, Art. no. 2401279, 2025.
A. Ali, Y. Wei, Y. Elsaboni, J. Tyson, H. Akerman, A. I. R. Jackson, et al., “A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time,” Sensors, vol. 24, no. 20, pp. 6513, 2024.
M. S. Sadi, N. Nahar, and E. Kumpikaitė, “Multifunctional Cu/poplar-PLA conductive nonwoven fabric for electromyography (EMG) sensing, pressure sensing, and thermal heating applications,” Journal of Materials Science, vol. 59, no. 16, pp. 6861-6878, 2024.
L. Yin and X. Sun, “Textile-based sensors for human motion sensing: Recent developments and future perspectives,” Nanocomposites, vol. 11, no. 1, pp. 79-98, 2025.
O. Banos, M. Damas, H. Pomares, and I. Rojas, “On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition,” Sensors, vol. 12, no. 6, pp. 8039-8054, 2012.
S. Huang, Y. Guo, P. Huo, and Q. Li, “Redefining Vibration Sensing: AI-Driven Analytics, Self-Powered Systems, and Multimodal Fusion: A Review,” IEEE Sensors Journal, vol. 25, no. 15, pp. 27922-27941, 2025.
Z. Chen, B. Zhou, M. Xiao, T. Bhowmick, P. Karthick Kannan, L. G. Occhipinti, et al., “Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments,” Science Advances, vol. 10, no. 6, pp. eadk6856, 2024.
C. Hu, Y. Huang, C. Deng, M. Jia, Q. Zhang, P. Wu, et al., “Noise reduction method for partial discharge fluorescence fiber sensors based on optimized empirical wavelet transform,” IEEE Photonics Journal, vol. 16, no. 4, pp. 1-9, 2024.
A. Ma’arif, I. Iswanto, A. A. Nuryono, and R. I. Alfian, “Kalman Filter for Noise Reducer on Sensor Readings,” Signal and Image Processing Letters, vol. 1, no. 2, pp. 50-61, 2019.
S. Li, W. Sun, Q. Liang, J. Sun, and C. Liu, “Multimodal Fabric Defect Classification Using Channel Switching and Multiscale Feature Fusion,” IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-12, 2024.
H. Abdi and L. J. Williams, “Principal component analysis,” Wiley interdisciplinary reviews: Computational statistics, vol. 2, no. 4, pp. 433-459, 2010.
V. Skaramagkas, I. Kyprakis, G. S. Karanasiou, D. I. Fotiadis, and M. Tsiknakis, “A Review on Deep Learning for Quality of Life Assessment through the Use of Wearable Data,” IEEE Open Journal of Engineering in Medicine and Biology, vol. 6, pp. 261-268, 2025.
C. Hu, F. Cheng, Y. Zhao, S. Guo, and L. Ma, “State of charge estimation for lithium-ion batteries based on data augmentation with generative adversarial network,” Journal of Energy Storage, vol. 80, Art. no. 110004, 2024.
W. Li, L. Yang, J. Huang, C. Zheng, Y. Chen, Y. Li, et al., “Progress on fiber engineering for fabric innovation in ecological hydrophobic design and multifunctional applications,” Industrial Chemistry & Materials, vol. 2, no. 3, pp. 393-423, 2024.
H. Nong, M. Jin, C. Pan, H. Zhou, C. Zhang, X. Pan, et al., “Intelligent Sensing Technologies Based on Flexible Wearable Sensors: A Review,” IEEE Sensors Journal, vol. 24, no. 14, pp. 22197-22217, 2024.
S. Ray, J. Park, and S. Bhunia, “Wearables, Implants, and Internet of Things: The Technology Needs in the Evolving Landscape,” IEEE Transactions on Multi-Scale Computing Systems, vol. 2, no. 2, pp. 123-128, 2016.
K. Bayoumy, M. Gaber, A. Elshafeey, O. Mhaimeed, E. H. Dineen, F. A. Marvel, et al., “Smart wearable devices in cardiovascular care: Where we are and how to move forward,” Nature Reviews Cardiology, vol. 18, no. 8, pp. 581-599, 2021.
T. Chen, S. H. Zhang, Q. H. Lin, M. J. Wang, Z. Yang, Y. L. Zhang, et al., “Highly sensitive and wide-detection range pressure sensor constructed on a hierarchical-structured conductive fabric as a human–machine interface,” Nanoscale, vol. 12, no. 41, pp. 21271-21279, 2020.
X. Zhou, C. Hu, X. Lin, X. Han, X. Zhao, and J. Hong, “Polyaniline-coated cotton knitted fabric for body motion monitoring,” Sensors and Actuators A: Physical, vol. 321, Art. no. 112591, 2021.
X. Ye, M. Tian, M. Li, H. Wang, and Y. Shi, “All-Fabric-Based Flexible Capacitive Sensors with Pressure Detection and Non-Contact Instruction Capability,” Coatings, vol. 12, no. 3, pp. 302, 2022.
Y. Zhang, J. Liao, Z. Li, M. Hu, C. Bian, and S. Lin, “All fabric and flexible wearable sensors for simultaneous sweat metabolite detection and high-efficiency collection,” Talanta, vol. 260, Art. no. 124610, 2023.
J. Sun, M. Yao, B. Xu, and P. Bel, “Fabric wrinkle characterization and classification using modified wavelet coefficients and support-vector-machine classifiers,” Textile Research Journal, vol. 81, no. 9, pp. 902-913, 2011.
Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: Analysis, applications, and prospects,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999-7019, 2021.
Q. Zhang, H. Ma, Z. Yu, Y. Liu, Z. Yang, and K. Wang, “Study on heat transfer characteristics of supercritical R134a in square microchannel based on GA-BP neural network,” Thermal Science and Engineering Progress, vol. 51, Art. no. 102654, 2024.