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Deep Learning-Based Activity Monitoring for Smart Environment Using Radar

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Challenges and Solutions for Sustainable Smart City Development

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Monitoring the environment plays a significant role in the evolution of a smart city with enhanced safety, comfort, and security. One promising technology for smart environment monitoring is the radar-based moving object tracking and classification. Radar-based technology is known to provide better solutions compared to vision-based systems as the latter is prone to the adverse effects of lighting and weather conditions. Radars operating in mm-wave range are preferred due to small size, good angular resolution, small apertures, and low cost. The signature waveform received from the moving object is used to estimate the range and the velocity of multiple targets based on Doppler drift. Also, the micro-Doppler effect on the return signal that occurs due to the micromotion dynamics such as vibrations or rotations can be used to identify the specific type of the target. This book chapter provides an overview of different types of radars and their respective signal processing aspects used for detecting the human and animal activity. Moreover, this chapter explains the deep learning algorithms used for detection and classification of human and animal activity. Thus the intelligent radar systems add greater value to the smart environment compared to other non-radar methods.

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References

  1. T.V. Kumar, Smart environment for smart cities, in Smart Environment for Smart Cities, (Springer, Singapore, 2020), pp. 1–53

    Google Scholar 

  2. P. Sharma, S. Rajput (eds.), Sustainable Smart Cities in India: Challenges and Future Perspectives (Springer, Berlin, 2017)

    Google Scholar 

  3. V. Angelakis, E. Tragos, H. C. Pöhls, A. Kapovits, A. Bassi (eds.), Designing, Develo**, and Facilitating Smart Cities: Urban Design to IoT Solutions (Springer, 2016)

    Google Scholar 

  4. M. Won, Intelligent traffic monitoring systems for vehicle classification: a survey. IEEE Access 8, 73340–73358 (2020)

    Article  Google Scholar 

  5. V.K. Kukkala, J. Tunnell, S. Pasricha, T. Bradley, Advanced driver-assistance systems: a path toward autonomous vehicles. IEEE Consum. Electron. Mag. 7(5), 18–25 (2018)

    Article  Google Scholar 

  6. D.J. Cook, G. Duncan, G. Sprint, R.L. Fritz, Using smart city technology to make healthcare smarter. Proc. IEEE 106(4), 708–722 (2018)

    Article  Google Scholar 

  7. M. Uddin, W. Khaksar, J. Torresen, Ambient sensors for elderly care and independent living: a survey. Sensors 18(7), 2027 (2018)

    Article  Google Scholar 

  8. S.K. Singh, F. Carpio, A. Jukan, Improving animal-human cohabitation with machine learning in fiber-wireless networks. J. Sens. Actuator Netw. 7(3), 35 (2018)

    Article  Google Scholar 

  9. J. Guerrero-Ibáñez, S. Zeadally, J. Contreras-Castillo, Sensor technologies for intelligent transportation systems. Sensors 18(4), 1212 (2018)

    Article  Google Scholar 

  10. A.P. Sligar, Machine learning-based radar perception for autonomous vehicles using full physics simulation. IEEE Access 8, 51470–51476 (2020)

    Article  Google Scholar 

  11. G. Diraco, A. Leone, P. Siciliano, A radar-based smart sensor for unobtrusive elderly monitoring in ambient assisted living applications. Biosensors 7(4), 55 (2017)

    Article  Google Scholar 

  12. P. Wang, Y. Zhang, Y. Ma, F. Liang, Q. An, H. Xue, J. Wang, Method for distinguishing humans and animals in vital signs monitoring using IR-UWB radar. Int. J. Environ. Res. Public Health 16(22), 4462 (2019)

    Article  Google Scholar 

  13. S. Pisa, E. Pittella, E. Piuzzi, A survey of radar systems for medical applications. IEEE Aerosp. Electron. Syst. Mag. 31(11), 64–81 (2016)

    Article  Google Scholar 

  14. R.P. Sam, U.M. Govindaswamy, Antenna selection and adaptive power allocation for IA-based underlay CR. IET Signal Proc. 11(6), 734–742 (2017)

    Article  Google Scholar 

  15. S.M. Patole, M. Torlak, D. Wang, M. Ali, Automotive radars: a review of signal processing techniques. IEEE Signal Process. Mag. 34(2), 22–35 (2017)

    Article  Google Scholar 

  16. Y.T. Im, J.H. Lee, S.O. Park, A pulse-Doppler and fmcw radar signal processor for surveillance, in 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) (2011), pp. 1–4

    Google Scholar 

  17. M. Reznicek, P. Bezousek, Commercial CW Doppler radar design and application, in 2017 27th International Conference Radioelektronika (RADIOELEKTRONIKA) (2017), pp. 1–5

    Google Scholar 

  18. C. Alabaster, Pulse Doppler radar: principles, technology, applications, vol. 2, IET (2012)

    Google Scholar 

  19. B. Çağlıyan, C. Karabacak, S.Z. Gürbüz, Indoor human activity recognition using BumbleBee radar, in 2014 22nd Signal Processing and Communications Applications Conference (SIU) (2014), pp. 1055–1058

    Google Scholar 

  20. M.A.A.H. Khan, R. Kukkapalli, P. Waradpande, S. Kulandaivel, N. Banerjee, N. Roy, R. Robucci, RAM: radar-based activity monitor, in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (2016), pp. 1–9

    Google Scholar 

  21. P. Reba, G. Umamaheswari, G. Suchitra, Performance investigation of interference alignment techniques for underlay MIMO cognitive radio networks, in 2018 15th IEEE India Council International Conference (INDICON) (2018), pp. 1–5

    Google Scholar 

  22. P.D. Beasley, G. Binns, R.D. Hodges, R.J. Badley, Tarsier: a millimetre wave radar for airport runway debris detection, in First European Radar Conference, 2004. EURAD (2004), pp. 261–264

    Google Scholar 

  23. K.B. Cooper, R.J. Dengler, G. Chattopadhyay, E. Schlecht, J. Gill, A. Skalare, P.H. Siegel, A high-resolution imaging radar at 580 GHz. IEEE Microwave Wireless Compon. Lett. 18(1), 64–66 (2008)

    Article  Google Scholar 

  24. C.Y. Du, X.H. Wang, Z.X. Yuan, Y. Xu, Design of gesture recognition system based on 77GHz millimeter wave radar, in 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT) (2019), pp. 1–3

    Google Scholar 

  25. M. Alizadeh, G. Shaker, J.C.M. De Almeida, P.P. Morita, S. Safavi-Naeini, Remote monitoring of human vital signs using mm-Wave FMCW radar. IEEE Access 7, 54958–54968 (2019)

    Article  Google Scholar 

  26. N. Techaphangam, M. Wongsaisuwan, Obstacle avoidance using mmWave radar imaging system, in 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (2020), pp. 466–469

    Google Scholar 

  27. J. Moll, M. Mälzer, N. Scholz, V. Krozer, M. Dürr, D. Pozdniakov, M. Scholz, Radar-based detection of birds near wind energy plants: first experiences from a field study, in 2016 German Microwave Conference (GeMiC) (2016), pp. 239–242

    Google Scholar 

  28. P. Molchanov, S. Gupta, K. Kim, K. Pulli, Short-range FMCW monopulse radar for hand-gesture sensing, in 2015 IEEE Radar Conference (RadarCon) (2015), pp. 1491–1496

    Google Scholar 

  29. M.E. Yanik, M. Torlak, Near-field MIMO-SAR millimeter-wave imaging with sparsely sampled aperture data. IEEE Access 7, 31801–31819 (2019)

    Article  Google Scholar 

  30. A.G. Yarovoy, P. van Genderen, L.P. Ligthart, Ultra-wideband ground penetrating impulse radar, in Ultra-Wideband, Short-Pulse Electromagnetics, vol. 5, (Springer, Boston, 2002), pp. 183–189

    Chapter  Google Scholar 

  31. A. Kılıç, İ. Babaoğlu, A. Babalık, A. Arslan, Through-wall radar classification of human posture using convolutional neural networks. Int. J. Antennas Propag. 2019, 7541814 (2019)

    Article  Google Scholar 

  32. L. Sakkila, A. Rivenq, F. Boukour, C. Tatkeu, Y. El Hillali, J.M. Rouvaen, Collision avoidance radar system using UWB waveforms signature for road applications, in 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST) (2009), pp. 223–226

    Google Scholar 

  33. M. Zenaldin, R.M. Narayanan, Radar micro-Doppler based human activity classification for indoor and outdoor environments, in Radar Sensor Technology XX, vol. 9829 (International Society for Optics and Photonics, 2016), p. 98291B

    Google Scholar 

  34. F. Qi, H. Lv, F. Liang, Z. Li, X. Yu, J. Wang, MHHT-based method for analysis of micro-Doppler signatures for human finer-grained activity using through-wall SFCW radar. Remote Sens. 9(3), 260 (2017)

    Article  Google Scholar 

  35. K.A. Smith, C. Csech, D. Murdoch, G. Shaker, Gesture recognition using mm-wave sensor for human-car interface. IEEE Sens. Lett. 2(2), 1–4 (2018)

    Article  Google Scholar 

  36. K. Gunaseelan, P. Reba, A. Kandaswamy, Block diagonalisation with adaptive resource allocation algorithm for multi-user MIMO-OFDM systems. Int. J. Collab. Enterp. 1(1), 58–66 (2009)

    Google Scholar 

  37. Y. Kim, Detection of eye blinking using Doppler sensor with principal component analysis. IEEE Antennas Wirel. Propag. Lett. 14, 123–126 (2014)

    Article  Google Scholar 

  38. G. Manfredi, J.P. Ovarlez, L. Thirion-Lefevre, Features Extraction of the Doppler frequency signature of a human walking at 1 GHz, in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (2019), pp. 2260–2263

    Google Scholar 

  39. M. Baratchi, N. Meratnia, P.J. Havinga, A.K. Skidmore, B.A. Toxopeus, Sensing solutions for collecting spatio-temporal data for wildlife monitoring applications: a review. Sensors 13(5), 6054–6088 (2013)

    Article  Google Scholar 

  40. I. Orović, S. Stanković, M. Amin, A new approach for classification of human gait based on time-frequency feature representations. Signal Process. 91(6), 1448–1456 (2011)

    Article  Google Scholar 

  41. G.E. Smith, K. Woodbridge, C.J. Baker, Naïve Bayesian radar micro-Doppler recognition, in 2008 International Conference on Radar (2008), pp. 111–116

    Google Scholar 

  42. A. Subasi, D.H. Dammas, R.D. Alghamdi, R.A. Makawi, E.A. Albiety, T. Brahimi, A. Sarirete, Sensor based human activity recognition using adaboost ensemble classifier. Procedia Comput. Sci. 140, 104–111 (2018)

    Article  Google Scholar 

  43. P. Reba, G.U. Maheswari, M.S. Babu, Multiple antenna selection for underlay cognitive radio systems with interference constraint. Wirel. Pers. Commun. 98(1), 1505–1520 (2018)

    Article  Google Scholar 

  44. Z. Zhao, Y. Song, F. Cui, J. Zhu, C. Song, Z. Xu, K. Ding, Point cloud features-based kernel SVM for human-vehicle classification in millimeter wave radar. IEEE Access 8, 26012–26021 (2020)

    Article  Google Scholar 

  45. A. Eryildirim, I. Onaran, Pulse Doppler radar target recognition using a two-stage SVM procedure. IEEE Trans. Aerosp. Electron. Syst. 47(2), 1450–1457 (2011)

    Article  Google Scholar 

  46. F. Luo, S. Poslad, E. Bodanese, Human activity detection and coarse localization outdoors using micro-Doppler signatures. IEEE Sensors J. 19(18), 8079–8094 (2019)

    Article  Google Scholar 

  47. J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  48. Y. Kim, T. Moon, Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13(1), 8–12 (2015)

    Article  Google Scholar 

  49. F.J. Ordóñez, D. Roggen, Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)

    Article  Google Scholar 

  50. Z. Zhang, Z. Tian, M. Zhou, Latern: dynamic continuous hand gesture recognition using FMCW radar sensor. IEEE Sensors J. 18(8), 3278–3289 (2018)

    Article  Google Scholar 

  51. M.S. Seyfioğlu, S.Z. Gürbüz, Deep neural network initialization methods for micro-Doppler classification with low training sample support. IEEE Geosci. Remote Sens. Lett. 14(12), 2462–2466 (2017)

    Article  Google Scholar 

  52. M.S. Seyfioğlu, A.M. Özbayoğlu, S.Z. Gürbüz, Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Trans. Aerosp. Electron. Syst. 54(4), 1709–1723 (2018)

    Article  Google Scholar 

  53. Y. Yang, C. Hou, Y. Lang, D. Guan, D. Huang, J. Xu, Open-set human activity recognition based on micro-Doppler signatures. Pattern Recogn. 85, 60–69 (2019)

    Article  Google Scholar 

  54. Y. Lang, Q. Wang, Y. Yang, C. Hou, D. Huang, W. **ang, Unsupervised domain adaptation for micro-Doppler human motion classification via feature fusion. IEEE Geosci. Remote Sens. Lett. 16(3), 392–396 (2018)

    Article  Google Scholar 

  55. G. Santhanamari, J.V. Viveka, B. Purushothaman, U. Shanthini, M. Vanitha, A new image denoising algorithm based on adaptive threshold and fourth order partial diffusion equation, in 2012 IEEE International Conference on Computational Intelligence and Computing Research (2012), pp. 1–4

    Google Scholar 

  56. B. Jokanovic, M. Amin, F. Ahmad, Radar fall motion detection using deep learning, in 2016 IEEE radar conference (RadarConf) (2016), pp. 1–6

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India

    N. Susithra, G. Santhanamari, M. Deepa & P. Reba

  2. Department of EEE, Sri Krishna College of Engineering and Technology, Coimbatore, India

    K. C. Ramya

  3. Department Computer Information Systems, Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

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  1. N. Susithra
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  2. G. Santhanamari
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  3. M. Deepa
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  4. P. Reba
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  5. K. C. Ramya
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  6. Lalit Garg
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Editor information

Editors and Affiliations

  1. VIT Bhopal University, Bhopal, India

    R. Maheswar

  2. Saveetha Institute of Medical And Technical Sciences (SIMATS), Chennai, India

    M. Balasaraswathi

  3. University of Bisha, Bisha, Saudi Arabia

    Ravi Rastogi

  4. VIT Bhopal University, Bhopal, India

    A. Sampathkumar

  5. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

    G. R. Kanagachidambaresan

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Susithra, N., Santhanamari, G., Deepa, M., Reba, P., Ramya, K.C., Garg, L. (2021). Deep Learning-Based Activity Monitoring for Smart Environment Using Radar. In: Maheswar, R., Balasaraswathi, M., Rastogi, R., Sampathkumar, A., Kanagachidambaresan, G.R. (eds) Challenges and Solutions for Sustainable Smart City Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-70183-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-70183-3_5

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  • Online ISBN: 978-3-030-70183-3

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