TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition

Lin, Chih-Yang; Lin, Chia-Yu; Liu, Yu-Tso; Chen, Yi-Wei; Ng, Hui-Fuang; Shih, Timothy K.

doi:10.3390/s25134216

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TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition

by

Chih-Yang Lin

^1,*

,

Chia-Yu Lin

²,

Yu-Tso Liu

^2,*

,

Yi-Wei Chen

²,

Hui-Fuang Ng

³

and

Timothy K. Shih

^2,*

¹

Department of Mechanical Engineering, National Central University, Taoyuan City 32001, Taiwan

²

Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan

³

Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Perak, Malaysia

^*

Authors to whom correspondence should be addressed.

Sensors 2025, 25(13), 4216; https://doi.org/10.3390/s25134216 (registering DOI)

Submission received: 26 May 2025 / Revised: 2 July 2025 / Accepted: 3 July 2025 / Published: 6 July 2025

(This article belongs to the Special Issue Sensors and Sensing Technologies for Object Detection and Recognition)

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Abstract

Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks.

Keywords: human activity recognition; MAML; TCN; wireless sensor networks

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MDPI and ACS Style

Lin, C.-Y.; Lin, C.-Y.; Liu, Y.-T.; Chen, Y.-W.; Ng, H.-F.; Shih, T.K. TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition. Sensors 2025, 25, 4216. https://doi.org/10.3390/s25134216

AMA Style

Lin C-Y, Lin C-Y, Liu Y-T, Chen Y-W, Ng H-F, Shih TK. TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition. Sensors. 2025; 25(13):4216. https://doi.org/10.3390/s25134216

Chicago/Turabian Style

Lin, Chih-Yang, Chia-Yu Lin, Yu-Tso Liu, Yi-Wei Chen, Hui-Fuang Ng, and Timothy K. Shih. 2025. "TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition" Sensors 25, no. 13: 4216. https://doi.org/10.3390/s25134216

APA Style

Lin, C.-Y., Lin, C.-Y., Liu, Y.-T., Chen, Y.-W., Ng, H.-F., & Shih, T. K. (2025). TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition. Sensors, 25(13), 4216. https://doi.org/10.3390/s25134216

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TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition

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