A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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Ismail Lamaakal, Siham Essahraui, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi, Mouncef Filali Bouami, Ahmed A. Abd El-Latif, May Almousa, Jialiang Peng, Dusit Niyato

Published: 2025, Last Modified: 07 Jan 2026IEEE Internet Things J. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The integration of tiny machine learning (TinyML) with human behavior analysis (HBA) represents a significant advancement in the field of artificial intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This article provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts, and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA.