Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition

TL;DR

This paper introduces an adversarial deep learning approach with MMD regularization to extract user-independent features for human activity recognition, improving generalization across unseen users and outperforming existing methods.

Contribution

The paper proposes a novel adversarial network with MMD regularization for user-independent HAR, enhancing generalization to unseen subjects and reducing result variance.

Findings

Outperforms state-of-the-art methods on four real-world datasets

Significantly improves user-independent recognition accuracy

Reduces variance in activity recognition results

Abstract

User dependence remains one of the most difficult general problems in Human Activity Recognition (HAR), in particular when using wearable sensors. This is due to the huge variability of the way different people execute even the simplest actions. In addition, detailed sensor fixtures and placement will be different for different people or even at different times for the same users. In theory, the problem can be solved by a large enough data set. However, recording data sets that capture the entire diversity of complex activity sets is seldom practicable. Instead, models are needed that focus on features that are invariant across users. To this end, we present an adversarial subject-independent feature extraction method with the maximum mean discrepancy (MMD) regularization for human activity recognition. The proposed model is capable of learning a subject-independent embedding feature representation from multiple subjects datasets and generalizing it to unseen target subjects. The proposed network is based on the adversarial encoder-decoder structure with the MMD realign the data distribution over multiple subjects. Experimental results show that the proposed method not only outperforms state-of-the-art methods over the four real-world datasets but also improves the subject generalization effectively. We evaluate the method on well-known public data sets showing that it significantly improves user-independent performance and reduces variance in results.