Tiny Deep Learning Models with Hybrid Compression Techniques for Gesture-Based Air Handwriting Recognition of English Alphabets on Edge Device

As touchless interaction becomes increasingly important in wearable and ambient computing, gesture-based air handwriting offers a promising input modality, particularly for low-power embedded devices. While vision-based and radar-based systems have achieved high accuracy in gesture recognition, they are often unsuitable for deployment on microcontrollers due to their computational and energy demands. In contrast, IMU-based systems provide a lightweight and privacy-preserving alternative, yet existing research rarely addresses full alphabet recognition or deployment-ready pipelines for resource-constrained environments. This paper proposes a complete TinyML pipeline for inertial-based air handwriting recognition of English alphabets, integrating structured preprocessing of raw IMU data into 2D rasterized gesture images, followed by training and deployment of four lightweight deep learning models: SqueezeNet, EfficientNet-Lite0, ShuffleNetV2, and FastKAN. The models are evaluated under a unified training configuration and subjected to compression techniques including quantization, pruning, and knowledge distillation. Among them, FastKAN demonstrates significant superiority, achieving a test accuracy of 97.4% with a minimal model size of 120 KB and energy consumption as low as 0.0011J per inference after hybrid compression. This work explicitly targets isolated characters (A–Z, a–z); continuous handwriting and word-level recognition are out of scope and left for future work. Extensive evaluations, including confusion matrix analysis, compression benchmarking, and successful deployment on an Arduino Nano 33 BLE Sense, demonstrate the practicality, efficiency, and robustness of the proposed system for real-time TinyML-based handwriting recognition applications.