Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

TLDR

Radar-based activity recognition is a problem that has attracted interest due to applications such as border control, automotive safety, and remote health monitoring. The study aims to demonstrate that micro‑Doppler analysis can distinguish gaits with indistinguishable signatures and introduces a three‑layer deep convolutional autoencoder that uses unsupervised pretraining to initialize its convolutional layers. The authors employ micro‑Doppler analysis and train a three‑layer deep convolutional autoencoder, initializing its convolutional layers via unsupervised pretraining. The deep CAE outperforms other deep learning models and conventional classifiers, achieving a 94.2 % correct‑classification rate for 12 indoor human activities—an improvement of 17.3 % over SVM.

Abstract

Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar-17.3% improvement over SVM.