GAN-based Synthetic Radar Micro-Doppler Augmentations for Improved Human Activity Recognition

Erol, Barış; Gürbüz, Sevgi Zübeyde; Amin, Moeness G.

doi:10.1109/radar.2019.8835589

Cited by 50 publications

(19 citation statements)

References 15 publications

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“…Non-contact methods for HAR have also been studied recently [ 32 , 33 , 34 , 35 , 36 , 37 ]. These approaches use ambient Wi-Fi signals or radars to track user activities.…”

Section: Related Researchmentioning

confidence: 99%

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Bhat

Tran

Shill³

et al. 2020

Sensors

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Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%.

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“…Non-contact methods for HAR have also been studied recently [ 32 , 33 , 34 , 35 , 36 , 37 ]. These approaches use ambient Wi-Fi signals or radars to track user activities.…”

Section: Related Researchmentioning

confidence: 99%

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Bhat

Tran

Shill³

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…Erol et al [5] also utilized GANs to generate synthetic data. However, instead of the vanilla GAN approach, they conditioned the generator to class labels and train the discriminator to predict the class of the synthetic data given by the generator.…”

Section: B Data Augmentationmentioning

confidence: 99%

Improving Cross-Subject Activity Recognition via Adversarial Learning

Leite

Xiao

2020

IEEE Access

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Deep learning has been widely used for implementing human activity recognition from wearable sensors like inertial measurement units. The performance of deep activity recognition is heavily affected by the amount and variability of the labeled data available for training the deep learning models. On the other hand, it is costly and time-consuming to collect and label data. Given limited training data, it is hard to maintain high performance across a wide range of subjects, due to the differences in the underlying data distribution of the training and the testing sets. In this work, we develop a novel solution that applies adversarial learning to improve cross-subject performance by generating training data that mimic artificial subjects-i.e. through data augmentation-and enforcing the activity classifier to ignore subject-dependent information. Contrary to domain adaptation methods, our solution does not utilize any data from subjects of the test set (or target domain). Furthermore, our solution is versatile as it can be utilized together with any deep neural network as the classifier. Considering the open dataset PAMAP2, nearly 10% higher crosssubject performance in terms of F1-score can be achieved when training a CNN-LSTM-based classifier with our solution. A performance gain of 5% is also observed when our solution is applied to a stateof-the-art HAR classifier composed of a combination of inception neural network and recurrent neural network. We also investigate different influencing factors of classification performance (i.e. selection of sensor modalities, sampling rates and the number of subjects in the training data), and summarize a practical guideline for implementing deep learning solutions for sensor-based human activity recognition.

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“…The work in this paper is developed concurrently with that of [33] [34] and provides, to our knowledge, the first in-depth analysis of GAN-generated synthetic data in terms of kinematic fidelity and diversity. In particular, we propose the utilization of auxillary classifier generative adversarial networks (ACGANs) [35], as opposed to conditional variational autoencoders (CVAEs) [36], for the generation of synthetic micro-Doppler signatures with greater diversity and sharpness.…”

Section: Introductionmentioning

confidence: 99%

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

Erol

Gürbüz

Amin

2020

IEEE Trans. Aerosp. Electron. Syst.

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Deep neural networks (DNNs) have recently received vast attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, and biomedicine. However, acquiring a sufficiently large training dataset remains a daunting task due to the high human costs and resources required for radar data collection. In this paper, an extended approach to adversarial learning is proposed for generation of synthetic radar micro-Doppler signatures that are well-adapted to different environments. The synthetic data is evaluated using visual interpretation, analysis of kinematic consistency, data diversity, dimensions of the latent space, and saliency maps. A principlecomponent analysis (PCA) based kinematic-sifting algorithm is introduced to ensure that synthetic signatures are consistent with physically possible human motions. The synthetic dataset is used to train a 19-layer deep convolutional neural network (DCNN) to classify micro-Doppler signatures acquired from an environment different from that of the dataset supplied to the adversarial network. An overall accuracy 93% is achieved on a dataset that contains multiple aspect angles (0 • , 30 • , and 45 • as well as 60 • ), with 9% improvement as a result of kinematic sifting.B. Erol is currently with Siemens Corporate Technology, Princeton, NJ. Formerly, he was with the Center for Advanced Communications, Villanova University, PA, email: baris.erol@siemens.com.S.Z. Gurbuz is with the

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GAN-based Synthetic Radar Micro-Doppler Augmentations for Improved Human Activity Recognition

Cited by 50 publications

References 15 publications

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Improving Cross-Subject Activity Recognition via Adversarial Learning

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

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