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

By Mehmet Saygin Seyfioglu, A. Ozbayoglu, S. Gürbüz. IEEE Transactions on Aerospace and Electronic Systems, 2018

309 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/efe6c0b97f4de89dcb4b97d47049816d4f4e92de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jun Liu
2. Qiuhong Ke
3. Moeness G. Amin
4. Hossein Rahmani
5. Zehua Sun
6. S. Gürbüz

In Biomedical Engineering

1. Jun Liu
2. Qiuhong Ke
3. Hossein Rahmani
4. Zehua Sun
5. Moeness G. Amin
6. S. Gürbüz
7. G. Wang
8. Amir Shahroudy

Citations in the corpus

• Radar-Based Human-Motion Recognition With Deep Learning: Promising applications for indoor monitoring (2019, background): “But, in a more difficult problem of classifying 12 aided/unaided human gaits [4], the CNN outperformed SVM by approximately 13%.”
• Human Action Recognition From Various Data Modalities: A Review (2020, background): “The works in [14], [332], [333], [334] directly fed raw micro-Doppler spectrogram images to CNNs for HAR, while [335] designed a three-layer deep convolutional auto-encoder, combining the advantages of CNNs and auto-encoders for radar-based HAR.”
• Deep Learning for Sensor-based Human Activity Recognition (2020, methodology): “Current research mostly adopts spectrograms for representing the Doppler effect and utilizes deep learning methods to process the spectrograms [134, 165].”