Zhengjie Wang
ABSTRACT
Frequency modulated continuous wave (FMCW) radar is widely studied in automotive, medicine, communication, and other fields due to its small size, high precision, and high bandwidth. In recent years, human behavior recognition has become increasingly popular in smart homes, health monitoring, entertainment, and other fields. FMCW radar, as a typical radar, has attracted more attention in the applications of human behavior recognition. This paper investigates the specific applications of human behavior recognition using FMCW radar and makes a detailed analysis from various aspects. Firstly, this paper introduces human behavior recognition based on FMCW radar. Secondly, this paper explains the general framework of behavior recognition based on FMCW radar, including data collection, signal processing, and behavior recognition methods. Then, according to the amplitude of behavioral activity, the application is divided into three granularities: signal-based, action-based, and activity-based recognition. Then this paper analyzes the application of behavior recognition from experimental scenarios, participants, classification methods, samples, experimental precision, etc. Finally, the various challenges of behavior recognition are presented by summarizing the existing literature.
- F. Gu, M.-H. Chung, M. Chignell, S. Valaee, B. Zhou, and X. Liu. 2021. A Survey on Deep Learning for Human Activity Recognition. ACM Comput. Surv. 54, 8(November 2022),1-34. https://doi.org/10.1145/3472290.Google Scholar
Digital Library
- Z. Yang and X. Zheng. 2021. Hand Gesture Recognition Based on Trajectories Features and Computation-Efficient Reused LSTM Network. J. IEEE Sensors. 21, 15(May 2021), 16945-16960. https://doi.org/10.1109/JSEN.2021.3079564.Google ScholarCross Ref
- M. Alizadeh, G. Shaker, J. C. M. D. Almeida, P. P. Morita, and S. Safavi-Naeini. 2019. Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Rada. IEEE Access. 7,(April 2019), 54958-54968. https://doi.org/10.1109/ACCESS.2019.2912956.Google ScholarCross Ref
- L. Sun 2020. Remote Measurement of Human Vital Signs Based on Joint-Range Adaptive EEMD. IEEE Access. 8(April 2020), 68514-68524. https://doi.org/10.1109/ACCESS.2020.2985286.Google ScholarCross Ref
- M. Chmurski, M. Zubert, K. Bierzynski, and A. Santra. 2021. Analysis of Edge-Optimized Deep Learning Classifiers for Radar-Based Gesture Recognition. IEEE Access. 9(May 2021), 74406-74421. https://doi.org/10.1109/ACCESS.2021.3081353.Google ScholarCross Ref
- S. Ryu, J. Suh, S. Baek, S. Hong, and J. Kim. 2018. Feature-Based Hand Gesture Recognition Using an FMCW Radar and its Temporal Feature Analysis. J. IEEE Sensors. 18, 18(July 2018 ), 7593-7602. https://doi.org/10.1109/JSEN.2018.2859815.Google ScholarCross Ref
- Y. Sun, T. Fei, X. Li, A. Warnecke, E. Warsitz, and N. Pohl. 2020. Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform. J. IEEE Sensors. 20, 18(May 2020),10706-10716. https://doi.org/10.1109/JSEN.2020.2994292.Google ScholarCross Ref
- Z. Xia, Y. Luomei, C. Zhou, and F. Xu. 2021. Multidimensional Feature Representation and Learning for Robust Hand-Gesture Recognition on Commercial Millimeter-Wave Radar. IEEE Transactions on Geoscience and Remote Sensing. 59, 6(July 2020), 4749-4764. https://doi.org/10.1109/TGRS.2020.3010880.Google ScholarCross Ref
- Z. Zhang, Z. Tian, and M. Zhou. 2018. Latern: Dynamic Continuous Hand Gesture Recognition Using FMCW Radar Sensor. J . IEEE Sensors. 18, 8(February 2018),3278-3289. https://doi.org/10.1109/JSEN.2018.2808688.Google ScholarCross Ref
- G. Li, Z. Zhang, H. Yang, J. Pan, D. Chen, and J. Zhang. 2020. Capturing Human Pose Using mmWave Radar. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).IEEE, Austin, TX, USA, 1-6. https://doi.org/10.1109/PerComWorkshops48775.2020.9156151.Google ScholarCross Ref
- J. Weiß, R. Pérez, and E. Biebl. 2020. Improved People Counting Algorithm for Indoor Environments using 60 GHz FMCW Radar. In Proceedings of the 2020 IEEE Radar Conference (RadarConf20). IEEE, Florence, Italy,1-6. https://doi.org/10.1109/RadarConf2043947.2020.9266607.Google ScholarCross Ref
- C. Y. Aydogdu, S. Hazra, A. Santra, and R. Weigel. 2020. Multi-Modal Cross Learning for Improved People Counting using Short-Range FMCW Radar. In Proceedings of the 2020 IEEE International Radar Conference (RADAR). IEEE, Washington, DC, USA,250-255. https://doi.org/ 10.1109/RADAR42522.2020.9114871.Google ScholarCross Ref
- C. Will, P. Vaishnav, A. Chakraborty, and A. Santra. 2019. Human Target Detection, Tracking, and Classification Using 24-GHz FMCW Radar. J.IEEE Sensors. 19, 17(May 2019),7283-7299. https://doi.org/10.1109/JSEN.2019.2914365.Google ScholarCross Ref
- P. Zhao 2019. mID: Tracking and Identifying People with Millimeter Wave Radar. In Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).IEEE, Santorini, Greece, 33-40. https://doi.org/10.1109/DCOSS.2019.00028.Google ScholarCross Ref
- J. W. Choi, S. J. Ryu, and J. H. Kim. 2019. Short-Range Radar Based Real-Time Hand Gesture Recognition Using LSTM Encoder. IEEE Access. 7(March 2019), 33610-33618. https://doi.org/10.1109/ACCESS.2019.2903586.Google ScholarCross Ref
- P. Wang 2020. A Gesture Air-Writing Tracking Method that Uses 24 GHz SIMO Radar SoC. IEEE Access. 8(August 2020), 152728-152741. https://doi.org/10.1109/ACCESS.2020.3017869.Google ScholarCross Ref
- Y. Wang, S. Wang, M. Zhou, Q. Jiang, and Z. Tian. 2019. TS-I3D Based Hand Gesture Recognition Method With Radar Sensor. IEEE Access. 7(February 2019),22902-22913. https://doi.org/10.1109/ACCESS.2019.2897060.Google ScholarCross Ref
- C. Ding 2019. Continuous Human Motion Recognition With a Dynamic Range-Doppler Trajectory Method Based on FMCW Radar. IEEE Transactions on Geoscience and Remote Sensing. 57, 9(April 2019), 6821-6831. https://doi.org/10.1109/TGRS.2019.2908758.Google ScholarCross Ref
- E. Tavanti, A. Rizik, A. Fedeli, D. D. Caviglia, and A. Randazzo. 2021. A Short-Range FMCW Radar-based Approach for Multi-Target Human-Vehicle Detection. IEEE Transactions on Geoscience and Remote Sensing. 60(December 2021), 1-16. https://doi.org/ 10.1109/TGRS.2021.3138687.Google ScholarCross Ref
- X. Qiao, G. Li, T. Shan, and R. Tao. 2021. Human Activity Classification Based on Moving Orientation Determining Using Multistatic Micro-Doppler Radar Signals. IEEE Transactions on Geoscience and Remote Sensing. 60(August 2021),1-15. https://doi.org/10.1109/TGRS.2021.3100482.Google ScholarCross Ref
- H. Chang, C. Lin, Y. Lin, W. Chung, and T. Lee. 2020. DL-Aided NOMP: a Deep Learning-Based Vital Sign Estimating Scheme Using FMCW Radar. In Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).IEEE, Antwerp, Belgium, 1-7. https://doi.org/10.1109/VTC2020-Spring48590.2020.9128552.Google ScholarCross Ref
- J. Yan, G. Zhang, H. Hong, H. Chu, C. Li, and X. Zhu. 2019. Phase-Based Human Target 2-D Identification With a Mobile FMCW Radar Platform. IEEE Transactions on Microwave Theory and Techniques. 67,12(September 2019), 5348-5359, https://doi.org/ 10.1109/TMTT.2019.2939523.Google ScholarCross Ref
- M. Shargorodskyy and R. Herschel. 2021. Localisation of trapped victims using spatially distributed, synchronised FMCW radar sensors. In Proceedings of the 2021 15th European Conference on Antennas and Propagation (EuCAP). IEEE. Dusseldorf, Germany, 1-5. https://doi.org/10.23919/EuCAP51087.2021.9411017.Google ScholarCross Ref
- P. Nallabolu, L. Zhang, H. Hong, and C. Li. 2021. Human Presence Sensing and Gesture Recognition for Smart Home Applications With Moving and Stationary Clutter Suppression Using a 60-GHz Digital Beamforming FMCW Radar. IEEE Access. 9(May 2021 ),72857-72866. https://doi.org/10.1109/ACCESS.2021.3080655.Google ScholarCross Ref
- Y. Wang, A. Ren, M. Zhou, W. Wang, and X. Yang. 2020. A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar. IEEE Access. 8(September 2020),167264-167275. https://doi.org/10.1109/ACCESS.2020.3023187.Google ScholarCross Ref
- S. Kim and K. Lee. 2018. Low-Complexity Joint Extrapolation-MUSIC-Based 2-D Parameter Estimator for Vital FMCW Radar. J.IEEE Sensors, 19, 6(October 2018),2205-2216. https://doi.org/10.1109/JSEN.2018.2877043.Google ScholarCross Ref
- H. Lee, B. Kim, J. Park, S. W. Kim, and J. Yook. 2019. A Resolution Enhancement Technique for Remote Monitoring of the Vital Signs of Multiple Subjects Using a 24 Ghz Bandwidth-Limited FMCW Radar. IEEE Access, 8(December 2019),1240-1248. https://doi.org/10.1109/ACCESS.2019.2961130.Google ScholarCross Ref
- M. Mercuri, I. R. Lorato, Y.-H. Liu, F. Wieringa, C. V. Hoof, and T. Torfs. 2019. Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor. Nature Electronics. 2, 6(June 2019),252-262. https://doi.org/10.1038/s41928-019-0258-6.Google ScholarCross Ref
- A. Prat, S. Blanch, A. Aguasca, J. Romeu, and A. Broquetas. 2019. Collimated Beam FMCW Radar for Vital Sign Patient Monitoring. IEEE Transactions on Antennas and Propagation, 67, 8(December 2018),5073-5080. https://doi.org/10.1109/TAP.2018.2889595.Google ScholarCross Ref
- M. Arsalan, A. Santra, and C. Will. 2020. Improved Contactless Heartbeat Estimation in FMCW Radar via Kalman Filter Tracking. IEEE Sensors Letters. 4, 5(March 2020),1-4. https://doi.org/10.1109/LSENS.2020.2983706.Google ScholarCross Ref
- F. Wang, P. Juan, D. Chian, and C. Wen. 2020. Multiple Range and Vital Sign Detection Based on Single-Conversion Self-Injection-Locked Hybrid Mode Radar With a Novel Frequency Estimation Algorithm. IEEE Transactions on Microwave Theory and Techniques. 68, 5(February 2020),1908-1920. https://doi.org/10.1109/TMTT.2020.2967372.Google ScholarCross Ref
- Y. Wang, H. Liu, K. Cui, A. Zhou, W. Li, and H. Ma. 2021. m-Activity: Accurate and Real-Time Human Activity Recognition Via Millimeter Wave Radar. In Proceedings of the ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).IEEE, Toronto, ON, Canada, 8298-8302. https://doi.org/10.1109/ICASSP39728.2021.9414686.Google ScholarCross Ref
- X. Shuai, Y. Shen, Y. Tang, S. Shi, L. Ji, and G. Xing. 2021. milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection. In the Proceedings of the International Conference on Internet-of-Things Design and Implementation. ACM, Charlottesvle, VA, USA,145-157. https://doi.org/10.1145/3450268.3453532.Google ScholarDigital Library
- T. Li, L. Fan, M. Zhao, Y. Liu, and D. Katabi. 2019. Making the Invisible Visible: Action Recognition Through Walls and Occlusions. In Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Seoul, Korea (South), 872-881. https://doi.org/10.1109/ICCV.2019.00096.Google ScholarCross Ref
- A. Sengupta, F. Jin, R. Zhang, and S. Cao. 2020. mm-Pose: Real-Time Human Skeletal Posture Estimation Using mmWave Radars and CNNs. J. IEEE Sensors, 20, 17(May 2020),10032-10044. https://doi.org/10.1109/JSEN.2020.2991741.Google ScholarCross Ref
- X. Li, Y. He, and X. Jing. 2019. A Survey of Deep Learning-Based Human Activity Recognition in Radar. J. Remote Sensing, 11, 9, 1068. https://doi.org/10.3390/rs11091068.Google ScholarCross Ref
- Z. Wang, B. Guo, Z. Yu, and X. Zhou. 2018. WiFi CSI-Based Behavior Recognition: From Signals and Actions to Activities. IEEE Communications Magazine. 56, 5(May 2018),109-115. https://doi.org/10.1109/MCOM.2018.1700144.Google ScholarCross Ref
- H. Tang and J. Cai. 2022. A Survey on Human Action Recognition based on Attention Mechanism. In Proceedings of the 2022 7th International Conference on Intelligent Information Technology. ACM, Foshan, China, 46-51. https://doi.org/10.1145/3524889.3524897.Google ScholarDigital Library
- J. Déziel 2021. PixSet: An Opportunity for 3D Computer Vision to Go Beyond Point Clouds With a Full-Waveform LiDAR Dataset. In Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, Indianapolis, IN, USA, 2987-2993. https://doi.org/10.1109/ITSC48978.2021.9565047.Google ScholarDigital Library
- A. D. Singh, S. S. Sandha, L. Garcia, and M. Srivastava. 2019. RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar. In Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems. ACM, Los Cabos, Mexico,51-56. https://doi.org/10.1145/3349624.3356768.Google ScholarDigital Library
Index Terms
- A Survey on Human Behavior Recognition Applications Using Frequency Modulated Continuous Wave Radar
Index terms have been assigned to the content through auto-classification.
Recommendations
3D Point Cloud Generation with Millimeter-Wave Radar
Emerging autonomous driving systems require reliable perception of 3D surroundings. Unfortunately, current mainstream perception modalities, i.e., camera and Lidar, are vulnerable under challenging lighting and weather conditions. On the other hand, ...
Fuzzy system based human behavior recognition by combining behavior prediction and recognition
Our method can show plural recognition results, i.e. person is waving while walking.We propose behavior recognition fusing current recognition and prediction of behavior.For the fusion, the fuzzy system based classifier of behaviors is adopted. With the ...
Processing of Synthetic Aperture Radar Data Using Frequency Modulated Signals
The main objective of Synthetic Aperture Radar (SAR) processing is converting the raw acquired data into pixels in the final processed image. The signal is spread in the range and azimuth directions, and the signal migrates to other range cells. Range ...
Comments