ArticleSensors (Basel, Switzerland)2023
Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.Scientific reports · 2025Article
- Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks.Sensors (Basel, Switzerland) · 2024Article
- Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.Sensors (Basel, Switzerland) · 2024Article
- Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
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Abstract
Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize network training and improve generalization. In this paper, we present whitening-aided CNN models for classifying human activities with radar sensors. We replace BN layers in a CNN model with whitening layers, which is shown to improve the model's accuracy by not only centering and scaling activations, similar to BN, but also decorrelating them. We also exploit the rotational freedom afforded by whitening matrices to align the whitened activations in the latent space with the corresponding activity classes. Using real data measurements of six different activities, we show that whitening provides superior performance over BN in terms of classification accuracy for a CNN-based classifier. This demonstrates the potential of whitening-aided CNN models to provide enhanced human activity recognition with radar sensors.
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