Data augmentation and synthesis of radar signatures for ML-based gesture recognition and activity detection from MoCap trajectories

Indranil Sinharoy; Aditya Dave; Gaurav Duggal; Vutha Va; Lianjun Li; Hao Chen; Abhishek Sehgal

doi:10.1117/12.3013122

7 June 2024 Data augmentation and synthesis of radar signatures for ML-based gesture recognition and activity detection from MoCap trajectories

Indranil Sinharoy, Aditya Dave, Gaurav Duggal, Vutha Va, Lianjun Li, Hao Chen, Abhishek Sehgal

Author Affiliations +

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 130351C (2024) https://doi.org/10.1117/12.3013122
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Recently, there is a growing interest in utilizing wireless signals for human gesture recognition and activity recognition. At the same time, the scarcity and lack of diversity of radar echo signature datasets of human gestures and activities is well recognized. This work demonstrates a framework for synthetically generating a vast and diverse set of radar echo signatures starting from a small set of optical motion capture (MoCap) trajectories. The captured trajectories are perturbed using a pool of composable spatial and temporal transformation functions assembled by a data augmentation pipeline builder. The transformed trajectories, combined with a simple radar cross-section (RCS) modeling process, are used to simulate radar CIR signals. Features extracted from this synthetic dataset show a strong correlation with the features obtained from simultaneously collected real radar data. Furthermore, we demonstrate that the synthetically generated radar echo signals can improve the performance of ML-based wireless gesture and activity recognition systems especially where the availability of real data is limited.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Indranil Sinharoy, Aditya Dave, Gaurav Duggal, Vutha Va, Lianjun Li, Hao Chen, and Abhishek Sehgal "Data augmentation and synthesis of radar signatures for ML-based gesture recognition and activity detection from MoCap trajectories", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 130351C (7 June 2024); https://doi.org/10.1117/12.3013122

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