Hierarchical radar data analysis for activity and personnel recognition

Hierarchical radar data analysis for activity and personnel recognition

Li, X. (University of Glasgow; University of Electronic Science and Technology of China)
Li, Zhenghui (University of Glasgow)
Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems)
Yang, Shufan (University of Glasgow)
Romain, Olivier (University of Cergy-Pontoise)
Le Kernec, Julien (University of Glasgow; University of Electronic Science and Technology of China; University of Cergy-Pontoise)

2020

Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.

Activity classification
Gait analysis
Machine learning
Mircro-doppler
Personnel recognition
Radar sensors

http://resolver.tudelft.nl/uuid:c6479ab4-d371-44e6-bf0e-95abcb03624e

https://doi.org/10.3390/rs12142237

2072-4292

Remote Sensing, 12 (14)

Institutional Repository

journal article

© 2020 X. Li, Zhenghui Li, F. Fioranelli, Shufan Yang, Olivier Romain, Julien Le Kernec