A TinyML Model for Gesture-Based Air Handwriting Arabic Numbers Recognition

• Published in: Procedia computer science 2024, Vol.236, p.589-596
• Main Authors: Lamaakal, Ismail, Makkaoui, Khalid El, Ouahbi, Ibrahim, Maleh, Yassine
• Format: Article
• Language: English
• Subjects: Arabic Hand Gestures; Arabic Numbers Recognition; Convolutional Neural Networks (CNNs); Deep Learning (DL); Tiny Machine Learning (TinyML)
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2024.05.070

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.