The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.
Keywords
Human Identification and Activity Recognition; Dual Spatial Convolution Gated Recurrent Unit; Radar-Based Systems; Frequency Domain; Global Spatial Dependence.
L. Qu, Y. Wang, T. Yang, and Y. Sun, “Human Activity Recognition Based on WRGAN-GP-Synthesized Micro-Doppler Spectrograms,” IEEE Sensors Journal, vol. 22, no. 9, pp. 8960–8973, May 2022, doi: 10.1109/jsen.2022.3164152.
M. M. Rahman, S. Z. Gurbuz, and M. G. Amin, “Physics-Aware Generative Adversarial Networks for Radar-Based Human Activity Recognition,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 3, pp. 2994–3008, Jun. 2023, doi: 10.1109/taes.2022.3221023.
M. Chakraborty, H. C. Kumawat, S. V. Dhavale, and A. A. B. Raj, “DIAT-μ RadHAR (Micro-Doppler Signature Dataset) & μ RadNet (A Lightweight DCNN)—For Human Suspicious Activity Recognition,” IEEE Sensors Journal, vol. 22, no. 7, pp. 6851–6858, Apr. 2022, doi: 10.1109/jsen.2022.3151943.
F. Aziz, O. Metwally, P. Weller, U. Schneider, and M. F. Huber, “A MIMO Radar-Based Metric Learning Approach for Activity Recognition,” 2022 IEEE Radar Conference (RadarConf22), Mar. 2022, doi: 10.1109/radarconf2248738.2022.9764202.
R. Mazzieri, J. Pegoraro and M. Rossi, “Enhanced Attention-Based Unrolling for Sparse Sequential micro-Doppler Reconstruction,” 2023, arXiv preprint arXiv:2306.14233.
S. Yang et al., “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, vol. 27, no. 4, pp. 1813–1824, Apr. 2023, doi: 10.1109/jbhi.2023.3240895.
Y. Zhao, A. Yarovoy, and F. Fioranelli, “Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging Radar and Deep Learning Classifiers,” IEEE Sensors Journal, vol. 22, no. 12, pp. 12173–12182, Jun. 2022, doi: 10.1109/jsen.2022.3175618.
F. J. Abdu, Y. Zhang, and Z. Deng, “Activity Classification Based on Feature Fusion of FMCW Radar Human Motion Micro-Doppler Signatures,” IEEE Sensors Journal, vol. 22, no. 9, pp. 8648–8662, May 2022, doi: 10.1109/jsen.2022.3156762.
X. Feng et al., “Millimeter-Wave Radar Monitoring for Elder’s Fall Based on Multi-View Parameter Fusion Estimation and Recognition,” Remote Sensing, vol. 15, no. 8, p. 2101, Apr. 2023, doi: 10.3390/rs15082101.
M. Chakraborty, H. C. Kumawat, S. V. Dhavale, and A. B. Raj A., “DIAT-RadHARNet: A Lightweight DCNN for Radar Based Classification of Human Suspicious Activities,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022, doi: 10.1109/tim.2022.3154832.
J. Pegoraro, J. O. Lacruz, M. Rossi, and J. Widmer, “SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems,” 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), May 2022, doi: 10.1109/ipsn54338.2022.00014.
Y. Ding, R. Liu, Y. She, B. Jin, and Y. Peng, “Micro-Doppler Trajectory Estimation of Human Movers by Viterbi–Hough Joint Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2022, doi: 10.1109/tgrs.2022.3171208.
X. Qiao, Y. Feng, T. Shan, and R. Tao, “Person Identification With Low Training Sample Based on Micro-Doppler Signatures Separation,” IEEE Sensors Journal, vol. 22, no. 9, pp. 8846–8857, May 2022, doi: 10.1109/jsen.2022.3162590.
S. Abadpour et al., “Angular Resolved RCS and Doppler Analysis of Human Body Parts in Motion,” IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 4, pp. 1761–1771, Apr. 2023, doi: 10.1109/tmtt.2022.3218304.
Y. Zhou, X. Yu, M. Lopez-Benitez, L. Yu, and Y. Yue, “Corruption Robustness Analysis of Radar Micro-Doppler Classification for Human Activity Recognition,” Jan. 2024, doi: 10.36227/techrxiv.24591924.v2.
Y. Zhou, M. López-Benítez, L. Yu, and Y. Yue, “Text2Doppler: Generating Radar Micro-Doppler Signatures for Human Activity Recognition via Textual Descriptions,” Mar. 2024, doi: 10.36227/techrxiv.171073563.32444449/v1.
R. G. Guendel, N. C. Kruse, F. Fioranelli, and A. Yarovoy, “Multipath Exploitation for Human Activity Recognition Using a Radar Network,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–13, 2024, doi: 10.1109/tgrs.2024.3363631.
A. Dey, S. Rajan, G. Xiao, and J. Lu, “Radar-Based Human Activity Recognition Using Multidomain Multilevel Fused Patch-Based Learning,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–14, 2024, doi: 10.1109/tim.2024.3374286.
X. Yang, W. Gao, X. Qu, P. Yin, H. Meng, and A. E. Fathy, “A Lightweight Multiscale Neural Network for Indoor Human Activity Recognition Based on Macro and Micro-Doppler Features,” IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21836–21854, Dec. 2023, doi: 10.1109/jiot.2023.3301519.
A. Alkasimi, A. Pham, C. Gardner, and B. Funsten, “Geolocation tracking for human identification and activity recognition using radar deep transfer learning,” IET Radar, Sonar & Navigation, vol. 17, no. 6, pp. 955–966, Mar. 2023, doi: 10.1049/rsn2.12390.
A. Alkasimi et al., “Dual-Biometric Human Identification Using Radar Deep Transfer Learning,” Sensors, vol. 22, no. 15, p. 5782, Aug. 2022, doi: 10.3390/s22155782.
A. Thirumalraj, V. S. Anusuya, and B. Manjunatha, “Detection of Ephemeral Sand River Flow Using Hybrid Sandpiper Optimization-Based CNN Model,” Advances in Civil and Industrial Engineering, pp. 195–214, Nov. 2023, doi: 10.4018/979-8-3693-1194-3.ch010.
J. B. Odili and J. O. Fatokun, “The Mathematical Model, Implementation and the Parameter-Tuning of the African Buffalo Optimization Algorithm,” 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), Mar. 2020, doi: 10.1109/icmcecs47690.2020.240886.
F. Luo, E. Bodanese, S. Khan, and K. Wu, “Spectro-Temporal Modeling for Human Activity Recognition Using a Radar Sensor Network,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–13, 2023, doi: 10.1109/tgrs.2023.3270365.
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Joseph Michael Jerard V
Joseph Michael Jerard V
School of Computer Science & Engineering, Presidency University, Bangalore, India.
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Cite this article
Joseph Michael Jerard V, Sarojini Yarramsetti, Vennira Selvi G and Natteshan N V S, “Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit", pp. 441-449, April 2024. doi: 10.53759/7669/jmc202404042.
