A Review of TinyML for Human Activity Recognition on Edge Devices

Abstract

The integration of Tiny Machine Learning (TinyML) into human activity recognition (HAR) represents a paradigm shift in artificial intelligence, enabling real-time, efficient, and privacy-preserving analysis on resource-constrained edge devices. This paper presents a comprehensive review of TinyML for HAR, covering foundational concepts, methodologies, and applications. the review examines the state of the art of existing works and approaches that combine TinyML and HAR, providing a detailed review and comparison of models, algorithms, and frameworks. This comparison sheds light on the effectiveness and limitations of different methodologies. Key contributions include a systematic taxonomy of HAR systems leveraging TinyML, a detailed analysis of optimization techniques like pruning, quantization, and knowledge distillation, and insights into state-of-the-art frameworks and datasets. Challenges such as scalability, energy efficiency, and generalization to diverse environments are critically examined, alongside solutions like federated learning, multimodal data fusion, the integration of generative AI, neuromorphic hardware, 5G/6G and Internet Of Things (IoT) connectivity, which are highlighted as transformative enablers for advancing HAR applications. This review serves as a foundational resource for researchers and practitioners aiming to harness TinyML’s potential in activity recognition systems.