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

## TL;DR

This paper introduces TCN-MAML, a machine learning framework that improves Wi-Fi-based human activity recognition across different individuals with limited data.

## Contribution

The novel integration of TCN and MAML enables efficient cross-subject adaptation for Wi-Fi CSI-based HAR.

## Key findings

- TCN-MAML achieves 99.6% accuracy in cross-subject human activity recognition using Wi-Fi CSI data.
- The framework outperforms baseline methods in generalization and efficiency for data-scarce conditions.
- Results confirm suitability for low-power, real-time deployment in IoT sensor networks.

## 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.