FastKAN-DDD: A novel fast Kolmogorov-Arnold network-based approach for driver drowsiness detection optimized for TinyML deployment
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| Publicado en: | PLoS One vol. 20, no. 11 (Nov 2025), p. e0332577 |
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| Autor principal: | |
| Otros Autores: | , , , , , , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3269212627 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0332577 |2 doi | |
| 035 | |a 3269212627 | ||
| 045 | 2 | |b d20251101 |b d20251130 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Essahraui, Siham | |
| 245 | 1 | |a FastKAN-DDD: A novel fast Kolmogorov-Arnold network-based approach for driver drowsiness detection optimized for TinyML deployment | |
| 260 | |b Public Library of Science |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Driver drowsiness is a leading cause of traffic accidents and fatalities, highlighting the urgent need for intelligent systems capable of real-time fatigue detection. Although recent advancements in machine learning (ML) and deep learning (DL) have significantly improved detection accuracy, most existing models are computationally demanding and not well-suited for deployment in resource-limited environments such as microcontrollers. While the emerging domain of TinyML presents promising avenues for such applications, there remains a substantial gap in the development of lightweight, interpretable, and high-performance models specifically tailored for embedded automotive systems. This paper introduces FastKAN-DDD, an innovative driver drowsiness detection model grounded in the Fast Kolmogorov-Arnold Network (FastKAN) architecture. The model incorporates learnable nonlinear activation functions based on radial basis functions (RBFs), facilitating efficient function approximation with a minimal number of parameters. To enhance suitability for TinyML deployment, the model is further optimized through post-training quantization techniques, including dynamic range, float-16, and weight-only quantization. Comprehensive experiments were conducted using the UTA-RLDD dataset—a real-world benchmark for driver drowsiness detection—evaluating the model across various input resolutions and quantization schemes. The FastKAN-DDD model achieved a test accuracy of 99.94%, with inference latency as low as 0.04 ms and a total memory footprint of merely 35 KB, rendering it exceptionally well-suited for real-time inference on microcontroller-based systems. Comparative evaluations further confirm that FastKAN surpasses several state-of-the-art TinyML models in terms of accuracy, computational efficiency, and model compactness. Our code’s are publicly available at: https://github.com/sihamess/driver_drowsiness_detection_TinyML. | |
| 653 | |a Physiology | ||
| 653 | |a Microcontrollers | ||
| 653 | |a Traffic accidents | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Sleepiness | ||
| 653 | |a Real time | ||
| 653 | |a Machine learning | ||
| 653 | |a Driver fatigue | ||
| 653 | |a Vehicles | ||
| 653 | |a Fatigue | ||
| 653 | |a Embedded systems | ||
| 653 | |a Radial basis function | ||
| 653 | |a Failure analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Inference | ||
| 653 | |a Network latency | ||
| 653 | |a Traffic accidents & safety | ||
| 653 | |a Drowsiness | ||
| 653 | |a Latency | ||
| 700 | 1 | |a Ismail Lamaakal | |
| 700 | 1 | |a Maleh, Yassine | |
| 700 | 1 | |a Khalid El Makkaoui | |
| 700 | 1 | |a Mouncef Filali Bouami | |
| 700 | 1 | |a Ouahbi, Ibrahim | |
| 700 | 1 | |a Elmannai, Hela | |
| 700 | 1 | |a Abd El-Latif, Ahmed A | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 11 (Nov 2025), p. e0332577 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3269212627/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3269212627/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3269212627/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |