Physically-Interpretable Data Augmentation for Multi-Range Hand Gesture Recognition Using FMCW Radar Time Series

In this paper, a robust ac hgr system using a ac fmcw radar and InceptionTime networks on data augmented time series is implemented. The paper proposes multiple data augmentation techniques for radar-based ac hgr. Since a realistic manipulation of raw radar data frames or even range-Doppler maps is a very complex challenge, we instead propose data manipulation on physically interpretable time series of range, azimuth and elevation angles extracted from the data. Due to working on physically interpretable time series data, we can on the one hand make use of well explored existing augmentation techniques for time series and on the other hand do use-case specific interpretable augmentations, such as simulating a different aspect angle or range. To investigate the system, a data recording process covering multiple ranges, angles and types of gestures is carried out. The gain in accuracy from the proposed data augmentation scheme amounts to more than 4 percentage points to reach a global prediction accuracy higher than 95% for a very diverse dataset.