Denoising UWB Radar Data for Human Activity Recognition Using Convolutional Autoencoders

Abstract

Human Activity Recognition (HAR) is one of the most popular research topics thanks to its usefulness in providing targeted, meaningful assistance to older adults. Because of the aging of the population in first-world countries, it becomes increasingly important to find innovative solutions that reduce risks associated with aging-in-place policies. HAR proposes solutions that are based on Ambient Intelligence (AmI) to alleviate those risks. In this work, we exploited three UWB radars to recognize 14 activities performed by 19 participants in a prototype smart-home apartment. The main contribution of this paper is UWB radar data cleaning on a practical dataset. The UWB radar data has been filtered using an unsupervised deep convolutional autoencoder (CNN-AE) that learns background noise from the data. This filtering method is compared to the unfiltered data using a Convolutional Neural Network (CNN) classifier in a Leave-One-Subject-Out (LOSO) classification. Performances attest that the CNN-AE unsupervised filtering is efficient for HAR. In addition, we tested the generalization potential of this architecture when the dataset is comprised of a lower number of participants (1, 5, 10, and all 19 participants). Generalization in HAR is difficult as the results show the importance of data quantity and number of subjects. We obtained 69.9% top-1 accuracy when using our filtering architecture compared to 48.4% without it. To conclude, we show that an unsupervised CNN-AE can efficiently filter and generalize UWB radar data in a HAR setting while providing easier learning constraints and implementation on a practical dataset.