Neural Style Transfer Enhanced Training Support For Human Activity Recognition

TL;DR

This paper enhances human activity recognition in ISAC systems by using style-transfer neural networks to generate realistic synthetic micro-Doppler signatures, improving classification accuracy and environmental robustness.

Contribution

It introduces a style-transfer neural network to augment micro-Doppler data with environmental effects, improving activity recognition performance in ISAC systems.

Findings

5% improvement in classification accuracy with data augmentation

Style-transferred signatures better capture environmental effects than other synthetic datasets

Enhanced signatures qualitatively and quantitatively more realistic

Abstract

This work presents an application of Integrated sensing and communication (ISAC) system for monitoring human activities directly related to healthcare. Real-time monitoring of humans can assist professionals in providing healthy living enabling technologies to ensure the health, safety, and well-being of people of all age groups. To enhance the human activity recognition performance of the ISAC system, we propose to use synthetic data generated through our human micro-Doppler simulator, SimHumalator to augment our limited measurement data. We generate a more realistic micro-Doppler signature dataset using a style-transfer neural network. The proposed network extracts environmental effects such as noise, multipath, and occlusions effects directly from the measurement data and transfers these features to our clean simulated signatures. This results in more realistic-looking signatures qualitatively and quantitatively. We use these enhanced signatures to augment our measurement data and observe an improvement in the classification performance by 5% compared to no augmentation case. Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise. The results indicate that style transferred simulated signatures well captures environmental factors more than any other synthetic dataset.