Outputs (13)

Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset (2023)
Presentation / Conference Contribution
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023, April). Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset. Presented at IWOCL '23: International Workshop on OpenCL, Cambridge, UK

Machine Learning has potential applications across a wide spectrum of devices. However, current approaches for domain-specific accelerators have encountered difficulties in satisfying the most recent computational demands for machine learning applica... Read More about Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset.

Radar-based Human Activity Recognition with Adaptive Thresholding towards Resource Constrained Platforms (2023)
Journal Article
Li, Z., Le Kernec, J., Abbasi, Q., Fioranelli, F., Yang, S., & Romain, O. (2023). Radar-based Human Activity Recognition with Adaptive Thresholding towards Resource Constrained Platforms. Scientific Reports, 13, Article 3473. https://doi.org/10.1038/s41598-023-30631-x

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms... Read More about Radar-based Human Activity Recognition with Adaptive Thresholding towards Resource Constrained Platforms.

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University (2023)
Journal Article
Yang, S., Kernec, J. L., Romain, O., Fioranelli, F., Cadart, P., Fix, J., Ren, C., Manfredi, G., Letertre, T., Saenz, I. D. H., Zhang, J., Liang, H., Wang, X., Li, G., Chen, Z., Liu, K., Chen, X., Li, J., Wu, X., Chen, Y., & Jin, T. (2023). The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University. IEEE Journal of Biomedical and Health Informatics, 27(4), 1813-1824. https://doi.org/10.1109/jbhi.2023.3240895

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi,... Read More about The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University.