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Wi-Fi signal-based human action acknowledgement using channel state information with CNN-LSTM: a device less approach

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Abstract

Human action acknowledgment is an abundant and significant area for machine learning-based researchers due to the level of accuracy in identifying human actions. Due to the rapid growth of technologies in the machine and deep learning techniques, wireless sensors, handy Internet of Things (IoT) devices, and Wireless Fidelity (Wi-Fi), the activity recognition process is made effective with higher accuracy. By using those booming technologies and preserving the privacy of the test person we propose a novel human action recognition model that uses the channel state information (CSI) from Wi-Fi and the most prominent machine learning model, CNN with LSTM. Initially, CSI is introduced, the changes in CSI signals are assessed, and the obtained data samples are made as input to the CNN-LSTM model. To make the recognition more accurate, we also incorporated Kalman filters for noise removal and smoothed the data sample. Furthermore, we have used an image segmentation procedure to identify the initial and end times of all the activities considered and to fragment the image obtained, which is further fed as input to the CNN-LSTM model. Getting a dataset for the experiment is a herculean task. Hence a self-collected dataset is used to assess, or model proposed. Finally, the results obtained are verified and validated for their correctness with appropriate machine learning metrics and parameters like accuracy, F1 score, etc. Our proposed model affords the accuracy of 98.96% for all the considered activities. The model can adapt itself even for a minimum sampling rate and subcarriers found in the test bed.

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The data used in this research is available on reasonable request.

References

  1. Virmani, A et al (2017) “Position and orientation agnostic gesture recognition using Wi-Fi”. In Proceedings of the 15th Annual Internl. Conf. on Mobile Systems, Applications, and Services, Niagara Falls, NY, USA, 19–23. 252–264.

  2. Yang et al (2018) “Fine-grained adaptive location-independent activity recognition using commodity Wi-Fi”. In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15–18; pp. 1–6.

  3. Wang X et al (2019) Towards Location Independent Gesture Recognition with Commodity Wi-Fi Devices. Electronics 8:1069. https://doi.org/10.3390/electronics8101069

    Article  Google Scholar 

  4. Zheng et al, (2019) “Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi”. In Proceedings of the 17th Annual Internl. Conf. on Mobile Systems, Applications and Services (MobiSys ’19), Seoul, Korea, 17–21

  5. Jie et al (2018) Towards cross-site and large-scale Wi-Fi sensing”. In Proceedings of the 24th Annual Internl. Conf. on Mobile Computing and Networking (MobiSys ’18), New Delhi, India, 29 October–November. pp. 305–320

  6. Wenchao et al (2018) Human Activity Recognition through the Wall with Commodity Wi-Fi Devices. IEEE Trans Vehicular Technol. https://doi.org/10.1109/TVT.2018.2878754

    Article  Google Scholar 

  7. Zhou Q, Xing J, Yang Q (2020) Device-free occupant activity recognition in smart offices using intrinsic Wi-Fi components. Build. Environ. 172:106737

    Article  Google Scholar 

  8. Jiang et al (2018) “Towards environment independent device free human activity recognition”. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (MobiSys ’18), New Delhi, India, 29 October–2 November. pp. 289–304

  9. Liu J, Wang L, Fang J, Guo L, Lu B, Shu L (2018) Multi target intense human motion analysis and detection using channel state information. Sensors 18:10

    Google Scholar 

  10. Zhang R, Jing X, Wu S, Jiang C, Yu FR (2020) Device-free wireless sensing for human detection: the deep learning perspective. IEEE Int Things J 8:2517–2539

    Article  Google Scholar 

  11. Moore et al (2013) “SCPL: indoor device-free multi-subject counting and localization using radio signal and the strength,” in Proceedings of the 12th Internl. Conference on Information Processing in Sensor Networks IPSN ’13, Philadelphia, PA, USA

  12. Chen et al (2017) “WiCount: a deep learning approach for crowd counting using Wi-Fi signals,” in Proceedings of the 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), Guangzhou, China

  13. K. Wu, J. Xiao, Y. Yi, M. Gao, and L. M. Ni, (2012) “FILA: fine-grained indoor localization,” in Proceedings of the IEEE INFOCOM, pp. 2210–2218, Orlando, FL, USA

  14. LM Ni et al (2014) “WiFall: device-free fall detection by wireless networks,” in Proceedings of the IEEE INFOCOM 2014 IEEE Conference on Computer Communications, Toronto, Canada

  15. K Ling, S Lu et al (2017) “Device free human activity recognition using commercial Wi-Fi devices.”IEEE J Select Areas Comm, 35(5): 1118–1131

  16. Bing Xian L et al (2018) HuAc: human activity recognition using crowd sourced Wi-Fi signals and skeleton data. Wireless Comm Mobile Comput 6163475:15

    Google Scholar 

  17. Podpora M et al (2015) Method for EEG Signals Pattern Recognition in Embedded Systems. Elektron, Elektrotech. 21:3–9. https://doi.org/10.5755/j01.eee.21.3.9918

    Article  Google Scholar 

  18. Cadusch PJ et al (1996) “Spatial sampling and filtering of EEG with spline Laplacians to estimate cortical potentials. Brain Topogra 1996(8):355–366. https://doi.org/10.1007/BF01186911

    Article  Google Scholar 

  19. Abdel-Rahman DH et al (2012) “Mathematical Modeling of Tumor Cell Growth and Immune Systems Interactions. Int J Mod Phy Conf Ser 9:95–111. https://doi.org/10.1142/S2010194512005156

    Article  Google Scholar 

  20. Yucel MA et al (2018) “Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and savitzky-golay filtering. Neuro photonics. 5:015003. https://doi.org/10.1117/1.NPh.5.1.015003

    Article  Google Scholar 

  21. Taghizadeh M (2018) Predicting the moisture content and textural characteristics of roasted pistachio kernels using Vis/NIR reflectance spectroscopy and PLSR analysis. J Food Measure Charact 12(1):346–355. https://doi.org/10.1007/s11694-017-9646-7

    Article  Google Scholar 

  22. Pander T et al (2019) EEG signal improvement with cascaded filter based on owa operator. Signal Image Video Process. 13:1165–1171. https://doi.org/10.1007/s11760-019-01458-9

    Article  Google Scholar 

  23. Kirsanov D et al (2018) Signal Smoothing with PLS Regression. Anal. Chem. 90:5959–5964. https://doi.org/10.1021/acs.analchem.8b01194

    Article  Google Scholar 

  24. Yao Yuan, Yun Fu (2014) Contour model-based hand-gesture recognition using the Kinect sensor. IEEE Trans Circuits Syst Video Technol 24(11):1935–1944

    Article  Google Scholar 

  25. Bian Zhen-Peng, Hou Junhui, Chau Lap-Pui, Thalmann Nadia Magnenat- (2014) Fall detection based on body part tracking using a depth camera. IEEE J Biomed Health Inform 19(2):430–439

    Article  Google Scholar 

  26. Atas Musa (2017) Hand tremor based biometric recognition using leap motion device. IEEE Access 5:23320–23326

    Article  Google Scholar 

  27. Yang Zheng, Zhou Zimu, Liu Yunhao (2013) From RSSI to CSI: Indoor localization via channel response. ACM Comput Surv 46(2):1–32. https://doi.org/10.1145/2543581.2543592

    Article  MATH  Google Scholar 

  28. Wetherall et al (2010) Predictable 802.11 packet delivery from wireless channel measurements. ACM Sigcomm Comput Commun Rev 40:159–170

    Article  Google Scholar 

  29. Yang Shuangming, Tan Jiangtong, Chen Badong (2022) Robust spike-based continual meta-learning improved by restricted minimum error entropy criterion. Entropy 24(4):455. https://doi.org/10.3390/e24040455

    Article  MathSciNet  Google Scholar 

  30. Yang Shuangming, Gao Tian, Wang Jiang, Deng Bin, Azghadi Mostafa Rahimi, Lei Tao, Linares-Barranco Bernabe (2022) SAM: a unified self-adaptive multicompartmental spiking neuron model for learning with working memory. Front Neurosci. https://doi.org/10.3389/fnins.2022.850945

    Article  Google Scholar 

  31. Yang S et al (2020) Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans Neural Netw Learn Syst 31(1):148–162. https://doi.org/10.1109/TNNLS.2019.2899936

    Article  Google Scholar 

  32. Yang Shuangming, Wang Jiang, Deng Bin, Azghadi Mostafa Rahimi, Linares-Barranco Bernabe (2021) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3084250

    Article  Google Scholar 

  33. Tahat A et al (2016) A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers. IEEE Access 4:6652–6680

    Article  Google Scholar 

  34. Yang Jieming, Liu Yanming, Liu Zhiying, Yun Wu, Li Tianyang, Yang Yuehua (2021) A Framework for Human Activity Recognition Based on Wi-Fi CSI Signal Enhancement. Int J Antennas Propagat 6654752:18. https://doi.org/10.1155/2021/6654752

    Article  Google Scholar 

  35. Wang Shu, Wang Aiguo, Ran Mengyuan, Liu Li, Peng Yuxin, Liu Ming, Guoxin Su, Alhudhaif Adi, Alenezi Fayadh, Alnaim Norah (2022) Hand gesture recognition framework using a lie group based Spatio-temporal recurrent network with multiple hand-worn motion sensors. Information Sciences 606:722–741

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

    V. Dhilip Kumar & P Rajesh

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey

    Kemal Polat

  3. Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia

    Fayadh Alenezi

  4. Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia

    Sara A Althubiti

  5. Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj, 11942, Saudi Arabia

    Adi Alhudhaif

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  1. V. Dhilip Kumar
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  2. P Rajesh
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  3. Kemal Polat
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  4. Fayadh Alenezi
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Correspondence to Kemal Polat.

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Kumar, V.D., Rajesh, P., Polat, K. et al. Wi-Fi signal-based human action acknowledgement using channel state information with CNN-LSTM: a device less approach. Neural Comput & Applic 34, 21763–21775 (2022). https://doi.org/10.1007/s00521-022-07630-6

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  • DOI: https://doi.org/10.1007/s00521-022-07630-6

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