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arXiv · 2410.12819

Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition

Francisco M. Calatrava-Nicolás·Shoko Miyauchi·Oscar Martinez Mozos
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Abstract

We present a new adversarial deep learning framework for the problem of human activity recognition (HAR) using inertial sensors worn by people. Our framework incorporates a novel adversarial activity-based discrimination task that addresses inter-person variability-i.e., the fact that different people perform the same activity in different ways. Overall, our proposed framework outperforms previous approaches on three HAR datasets using a leave-one-(person)-out cross-validation (LOOCV) benchmark. Additional results demonstrate that our discrimination task yields better classification results compared to previous tasks within the same adversarial framework.

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BibTeXRIS

Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Oscar Martinez Mozos. 2024-10-01. Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition. https://arxiv.org/abs/2410.12819

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