Contact-free sensors offer important advantages compared to traditional wearables. Radiofrequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.
Bibliographical noteFunding Information:
Funding: This research was funded by the Slovenian Research Agency as part of the PhD study program for young researcher Gašper Slapnicar. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 GPU used in this research.
Acknowledgments: We would like to acknowledge the support of the Slovenian Research Agency and the Department of Intelligent Systems at Jožef Stefan Institute.
- Artificial neural networks
- Contact-free sensing
- Deep learning
- Radar data