<scp>TinyML</scp> ‐Driven On‐Device Sports Command Recognition in Mobile and Dynamic Environments

ABSTRACT In this article, we propose a novel TinyML‐based framework for real‐time sports command recognition under mobile conditions. Unlike conventional Human Activity Recognition (HAR) systems that rely on cloud‐based processing or heavy on‐device models, our method leverages lightweight deep neural networks, personalized transfer learning, and signal augmentation techniques to perform low‐latency and energy‐efficient inference directly on microcontroller‐class devices. The system is designed to recognize a set of critical sports instructions (e.g., “Start Running,” “Jump,” and “Sprint”) in mobile or outdoor environments using only wearable inertial sensors. Extensive experiments demonstrate our method outperforms several state‐of‐the‐art baselines in accuracy (95.8%), model size (14.5 KB), and energy efficiency (0.82 mJ per inference). Compared to prior wearable HAR systems, our method uniquely integrates motion‐aware segmentation and user‐personalized few‐shot adaptation, resulting in a 5.3% accuracy gain and 4× model compression over baseline TinyML frameworks. The proposed method provides an effective balance between model accuracy, generalization, and hardware efficiency, even in scenarios with significant motion noise and environmental variability.