A TinyML Model for Gesture-Based Air Handwriting Arabic Numbers Recognition
Open accessAbstract
In an era where the demand for efficient and practical machine learning (ML) solutions on resource-constrained devices is evergrowing, the realm of tiny machine learning (TinyML) emerges as a promising frontier. Motivated by the need for lightweight, low-power models that can be deployed on edge devices, this research paper presents an innovative TinyML model tailored to recognize Arabic hand gestures executed in mid-air. With a primary emphasis on the precise classification of Arabic numbers through these expressive hand movements, the paper unveils a comprehensive dataflow architecture. This intricate architecture processes accelerometer and gyroscope data to derive exact 2D gesture coordinates, a fundamental component of the recognition process. The cornerstone of the proposed model is the integration of Convolutional Neural Networks (CNNs), elucidating their exceptional role in achieving an impressive 93.8% accuracy rate in the classification of diverse Arabic Numbers gestures. This remarkable level of precision underscores the model's efficacy and resilience, rendering it an ideal candidate for real-time deployment in various gesture recognition scenarios.
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TLMs are typically created by employing techniques such as knowledge distillation, pruning, quantization, and specialized architectures designed to minimize model size and memory footprint.
“TLMs are typically created by employing techniques such as knowledge distillation, pruning, quantization [17,18,19,20,21,22,23], and specialized architectures [24] designed to minimize model size and memory footprint.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [23]
Cites
A Comprehensive Survey on TinyML
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Self-citationDeep Learning Approaches for Air-Writing Using Single UWB Radar
Hendy, N., Fayek, H. M., & Al‐Hourani, A. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3172727 · Cited by 44 · see citing works
A CNN-LSTM based ensemble framework for in-air handwritten Assamese character recognition
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Air-writing recognition system for Persian numbers with a novel classifier
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Air Writing: Recognizing Multi-Digit Numeral String Traced in Air Using RNN-LSTM Architecture
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Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60 · see citing works
Self-citationLamaakal, I., Yahyati, C., Maleh, Y., El Makkaoui, K., Ouahbi, I., El-Latif, A. A. A., Zomorodi, M., & El-Rahiem, B. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-26297-2 · Cited by 26 · see citing works
Self-citationLamaakal, I., Maleh, Y., Ouahbi, I., El Makkaoui, K., & Abd El-Latif, A. A. (2024) · Lecture Notes in Networks and Systems · doi:10.1007/978-3-031-68675-7_4 · Cited by 20 · see citing works
Self-citationEfficient Gesture-Based Recognition of Tifinagh Characters in Air Handwriting with a TinyDL Model
Lamaakal, I., El Mourabit, N., El Makkaoui, K., Ouahbi, I., & Maleh, Y. (2024) · 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds62089.2024.10756483 · Cited by 14 · see citing works
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- Authors
- Lamaakal, I., Makkaoui, K. E., Ouahbi, I., & Maleh, Y.
- Affiliations
- Mohamed I University (MA)
- Université Sultan Moulay Slimane (MA)
- Corresponding author
- Lamaakal, Ismail
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2024
- Volume
- 236
- Pages
- 589-596
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4399249601
- Cited by
- 33
- References
- 14
- Citations by year
- 2026: 8 · 2025: 23 · 2024: 2
- FWCI
- 10.63
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- TinyML, gesture recognition, air handwriting, mid-air gestures, edge devices, accelerometer and gyroscope data, lightweight convolutional neural network, real-time deployment
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