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Journals / Textile and Apparel / 2026 / Volume: 35 Issue: 3

From Textiles to Insights: Evaluating Deep Learning Models on Wearable Sensor Data for Activity Recognition

Mehmet Ali Balcı(MUGLA SITKI KOCMAN UNIVERSITY, FACULTY OF SCIENCE, DEPARTMENT OF MATHEMATICS)0000-0003-1465-7153
Ömer Akgüller(MUGLA SITKI KOCMAN UNIVERSITY, FACULTY OF SCIENCE, DEPARTMENT OF MATHEMATICS)0000-0002-7061-2534
Ozan Kayacan(Dokuz Eylül Üniversitesi Mühendislik Fakültesi Tekstil Mühendisliği Bölümü)0000-0001-9246-0730
Özgür Tamer(DOKUZ EYLUL UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH))0000-0002-5776-6627
Aksel Çelik(DOKUZ EYLUL UNIVERSITY, FACULTY OF SPORTS SCIENCE, DEPARTMENT OF COACH TRAINING)0000-0003-0961-7616
Tolga Akşit(EGE UNIVERSITY, FACULTY OF SPORTS SCIENCE, DEPARTMENT OF COACH TRAINING)0000-0003-0223-4508
Duygu Yavuzkasap Ayakta(YUNSA R&D Dept.)0000-0003-0108-9839
Aysu Ildeniz(YUNSA R&D Dept.)0000-0001-8299-9433
Pages
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Publication Date
2025-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The insights gained from this evaluation guide the development of more robust, user-centric HAR systems, opening new avenues for healthcare, sports analytics, and ergonomic applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Textile-integrated wearable sensors have emerged as transformative tools for unobtrusive, continuous monitoring of human movement and physiology.
📊
Key Findings & Data:In this study, we comprehensively compare the performance of five deep learning methodologies—LSTM, RNN, CNN, DBN, and AE—on a 19-class human activity recognition (HAR) dataset derived from textile-based wearable sensors.
💡
Conclusion & Impact:The insights gained from this evaluation guide the development of more robust, user-centric HAR systems, opening new avenues for healthcare, sports analytics, and ergonomic applications.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Wearable Sensors · Human Activity Recognation · Deep Learning Architectures

Abstract

Textile-integrated wearable sensors have emerged as transformative tools for unobtrusive, continuous monitoring of human movement and physiology. These fabrics, embedded with conductive and functional materials, capture rich, multidimensional data streams during naturally occurring activities. In this study, we comprehensively compare the performance of five deep learning methodologies—LSTM, RNN, CNN, DBN, and AE—on a 19-class human activity recognition (HAR) dataset derived from textile-based wearable sensors. Our findings demonstrate that CNN and LSTM models outperform their counterparts, achieving exceptional accuracy in distinguishing subtle, overlapping movement patterns. This superior performance underscores the potential of advanced neural architectures to leverage complex temporal-spatial information inherent in textile sensor data. While RNN and AE models also prove effective, they require refinement for challenging activities, and DBN shows comparatively lower performance. The insights gained from this evaluation guide the development of more robust, user-centric HAR systems, opening new avenues for healthcare, sports analytics, and ergonomic applications.

🔖

Cite This Article

Balcı, M. A., Akgüller, Ö., Kayacan, O., Tamer, Ö., Çelik, A., Akşit, T., Ayakta, D. Y., Ildeniz, A. (2025). From Textiles to Insights: Evaluating Deep Learning Models on Wearable Sensor Data for Activity Recognition. Textile and Apparel, Volume: 35 Issue: 3. https://doi.org/10.32710/tekstilvekonfeksiyon.1606757

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