Radar Based Secure Contactless Fall Detection Using Hybrid Optimizer with Convolution Neural Network
DOI:
https://doi.org/10.56294/sctconf2024.1119Keywords:
Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radarAbstract
Introduction: senior citizens can lead to severe injuries. Existing wearable fall-alert sensors are often ineffective as seniors tend to avoid using them, highlighting the need for non-contact sensor applications in smart homes. This study proposes a CNN-based fall detection system using time-frequency analyses. A unique hybrid optimizer, GWO-ABC, combining Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO), is employed to optimize CNN architectures. Radar return signals are transformed into spectrograms and binary images for training the HOCNN with fall and non-fall data.
Methods: Radar signals are processed using short-time Fourier transformation to create time-frequency spectrograms, converted into binary images. These images are fed into a CNN optimized by the GWO-ABC algorithm. The CNN is trained on labelled fall and non-fall instances, focusing on high-level feature extraction.
Results: The HOCNN showed superior accuracy in fall detection, successfully extracting critical high-level features from radar signals. Performance metrics, including precision, recall, and F1-score, demonstrated significant improvements over traditional methods.
Conclusion: This study introduces a non-contact, automatic fall detection system for smart homes using GWO-ABC optimized CNNs, offering a promising solution for enhancing geriatric care and ensuring senior citizen safety. Index Terms—Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radar
References
1. United Nations Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Highlights.
2. United Nations Department of Economic and Social Affairs, Population Division. World Population Ageing 2019: Highlights.
3. World Health Organization. Global report on falls prevention in older age. WHO; 2020.
4. Little L, Briggs P. Pervasive healthcare: the elderly perspective.
5. Edfors E, Westergren A. Home-living elderly people’s views on food and meals. J Aging Res, 2012, pp. 761291. https://doi.org/10.1155/2012/761291.
6. Corbishley P, Rodriguez-Villegas E. Breathing detection: Towards a miniaturized, wearable, battery-operated monitoring system. IEEE Trans Biomed Eng. 55(1), pp. 196–204.https://doi.org/10.1109/TBME.2007.912639.
7. Chen VC, Tahmoush D, Miceli WJ. Radar Micro-Doppler Signatures: Processing and Applications. Radar, Sonar, Navigation and Avionics. London: Institution of Engineering and Technology.
8. Bryan JD, Kwon J, Lee N, Kim Y. Application of ultra-wideband radar for classification of human activities. IET Radar Sonar Navig, 6(3), pp. 172–9. https://doi.org/10.1049/iet-rsn.2011.0165.
9. Hazelhoff L, Han J, de-with PHN. Video-based fall detection in the home using principal component analysis. In: Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 298–309.
10. Ding C, Wang L, Jiang H, Wang Q. Non-contact human motion recognition based on UWB radar. IEEE Trans Emerg Sel Topics Circuits Syst, 8(2), pp. 306–15. https://doi.org/10.1109/JETCAS.2018.2843262.
11. Abdelhedi S, Bourguiba R, Mouine J, Baklouti M. Development of a two-threshold-based fall detection algorithm for elderly health monitoring. In: Proceedings of the IEEE International Conference on Research Challenges in Information Science, pp. 1–5. https://doi.org/10.1109/RCIS.2016.7549358.
12. Bjorklund S, Petersson H, Hendeby G. Features for micro-Doppler based activity classification. IET Radar Sonar Navig, 9(9), pp. 1181–7. https://doi.org/10.1049/iet-rsn.2014.0254.
13. Wu Q, Zhang YD, Tao W, Amin M. Radar-based fall detection based on Doppler time-frequency signatures for assisted living. IET Radar Sonar Navig, 9(2), pp. 164–72. https://doi.org/10.1049/iet-rsn.2014.0231.
14. Han J, Zhang D, Cheng G, Liu N, Xu D. Advanced deep-learning techniques for salient and category-specific object detection: A survey. IEEE Signal Process Mag, 35(1), pp. 84–100. https://doi.org/10.1109/MSP.2017.2766795.
15. Jokanovic B, Amin M, Ahmad F. Radar fall motion detection using deep learning. In: Proceedings of the IEEE Radar Conference, pp. 1–6. https://doi.org/10.1109/RADAR.2016.7485164.
16. Wagner D, Kalischewski K, Velten J, Kummert A. Activity recognition using inertial sensors and a 2D convolutional neural network. In: Proceedings of the International Workshop on Multidimensional Systems, pp. 1–6.
17. Lang Y, Hou C, Yang Y, Huang D, He Y. Convolutional neural network for human micro-Doppler classification. In: Proceedings of the European Microwave Conference, pp. 1–4. https://doi.org/10.23919/EuMC.2017.8230918.
18. Maitre J, Bouchard K, Gaboury S. Fall Detection with UWB Radars and CNN-LSTM Architecture. IEEE J Biomed Health Inform, 25(5), pp. 1273–83. https://doi.org/10.1109/JBHI.2021.3062385.
19. Saho K, Hayashi S, Tsuyama M, Meng L, Masugi M. Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements. Sensors, 22(5), pp. 1721. https://doi.org/10.3390/s22051721.
20. Yang T, Cao J, Guo Y. Placement selection of millimeter wave FMCW radar for indoor fall detection. In: Proceedings of the 2018 IEEE MTT-S International Wireless Symposium (IWS), pp. 1–3. https://doi.org/10.1109/IEEE-IWS.2018.8400908.
21. Mager B, Patwari N, Bocca M. Fall detection using RF sensor networks. In: Proceedings of the 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 3472–6. https://doi.org/10.1109/PIMRC.2013.6666700.
22. Cameiro SA, da Silva GP, Leite GV, Moreno R, Guimaraes SJF, Pedrini H. Multi-stream deep convolutional network using high-level features applied to fall detection in video sequences. In: Proceedings of the International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 293–8. https://doi.org/10.1109/IWSSIP.2019.8787290.
23. Casilari E, Lora-Rivera R, García-Lagos F. A study on the application of convolutional neural networks to fall detection evaluated with multiple public datasets. Sensors. 20(5), pp. 1466. https://doi.org/10.3390/s20051466.
24. Novelda’s XeThru X4M03. 2018.
25. Stankovic L, Dakovic M, Thayaparan T. Time-Frequency Signal Analysis with Applications. Norwood, MA: Artech House.
26. Erol B, Francisco M, Ravisankar A, Amin M. Realization of radar-based fall detection using spectrograms. In: Compressive Sensing VII: From Diverse Modalities to Big Data Analytics. https://doi.org/10.1117/12.2306892.
27. Gonzalez RC, Woods RE. Digital Image Processing. 3rd ed. Englewood Cliffs, NJ: Prentice-Hall.
28. Karthiga M, Santhi V, Sountharrajan S. Hybrid optimized convolutional neural network for efficient classification of ECG signals in healthcare monitoring. Biomed Signal Process Control, 76, pp. 103731. https://doi.org/10.1016/j.bspc.2021.103731.
29. Li JQ, Pan QK, Xie SX, Wang S. A hybrid artificial bee colony algorithm for flexible job shop scheduling problems. Int J Comput Commun Control, 6(2), pp. 286–96. https://doi.org/10.15837/ijccc.2011.2.2499.
30. Liu L, Popescu M, Skubic M, Rantz M, Yardibi T, Cuddihy P. Automatic fall detection based on Doppler radar motion signature. In: Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare, pp. 222–5. https://doi.org/10.4108/icst.pervasivehealth.2011.246050.
31. Yu M, Rhuma A, Naqvi SM, Wang L, Chambers J. A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans Inf Technol Biomed, 16(6), pp. 1274–86. https://doi.org/10.1109/TITB.2012.2203313
Published
Issue
Section
License
Copyright (c) 2024 Nester Jeyakumar M, Jasmine Samraj, Bennet Rajesh M (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
