References
- C. Yu, Z. Xu, K. Yan, Y. R. Chien, S. H. Fang, H. C. Wu, "Noninvasive Human Activity Recognition Using Millimeter-Wave Radar," IEEE Systems Journal, Vol. 16, No 2, pp. 3036-3047, 2022. https://doi.org/10.1109/JSYST.2022.3140546
- A. Shastri, N. Valecha, E. Bashirov, H. Trtaria, M. Lentmaier, F. Tufvesson, M. Rossi, P. Casari, "A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications," IEEE Communications Surveys & Tutorials, Vol. 24, No. 3, pp. 1708-1749, 2022. https://doi.org/10.1109/COMST.2022.3177305
- B. Liu, K. Ma, H. Fu, K. Wang, F. Meng, "IRecent Progress of Silicon-Based Millimeter-Wave SoCs for Short-Range Radar Imaging and Sensing," IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69, No. 6, pp. 2667-2671, 2022.
- Y. Rong, I. Lenz, D. W. Bliss, "Vital Signs Detection Based on High-Resolution 3-D mmWave Radar Imaging," 2022 IEEE International Symposium on Phased Array Systems & Technology.
- S. Lyer, L. Zhao, M. P. Mohan, J. Jimenno, M. Y. Siyal, A. Alphones, M. F. Karim, "mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning," Sensors, Vol. 22, No. 9, 2022.
- A. D. Singh, S. S. Sandha, L. Garcia, M. Srivastava, "RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar," Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems, pp. 51-56, 2019.
- H. Arab, I. Ghaffari, L. Chioukh, S. O. Tatu, StevenDufour, "A Convolutional Neural Network for Human Motion Recognition and Classification Using a dxMillimeter-Wave Doppler Radar," IEEE Sensors Journal, Vol. 22, No. 5, pp. 4494-4502, 2022.
- P. P. Ray, "A review on TinyML: State-of-the-art and Prospects," Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 4, pp. 1595-1623, 2022. https://doi.org/10.1016/j.jksuci.2021.11.019
- F. J. Adbu, Y. Zhang, M. Fu, Y. Li, Z. Deng, "Application of Deep Learning on Millimeter-Wave Radar Signals: A Review," Sensors, Vol. 21, No. 6, 2021.
- T. Stadelmayer, M. Standelmayer, A. Santra, R. Weigel, F. Lurz, "Human Activity Classification Using mm-Wave FMCW Radar by Improved Representation Learning," Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems, Vol. 1, No. 1, pp. 1-6, 2020.
- H. Zhou, Y. Zhao, Y. Liu, S. Lu, X. An, Q. Liu, "Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System," Sensors, Vol. 23, No. 10, 2023.
- X. Huang, N. Patel, K. P. Tsoi, "Application of mmWave Radar Sensor for People Identification and Classification," Sensors, Vol. 23, No. 8, 2023.
- S. Bai, J. Z. Kolter, V. Koltun, "An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling," arXiv:1803.01271, 2018.
- C. R. Qi, H. Su, K. Mo, L. J. Guibas, "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652-660, 2017.