Skip to main content
SpringerLink
Log in

A novel fuzzy clustering-based method for human activity recognition in cloud-based industrial IoT environment

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

With the advancement of technology such as video monitoring, Internet-of-things, cloud, and machine learning, Industry 4.0 is working continuously to ensure the security of workers. The workers are equipped with sensors to analyze their activities. In general, the recognition of human activities in cloud-based industrial scenario is leveraged to monitor the safety of the workers. This paper introduced a new optimal clustering method for the activity recognition of workers in industry using cloud based IoT environment. The proposed method uses the temporal and spatial features of human workers in industry. The proposed method is tested on publicly available dataset of different activities maintained into three groups, namely movement, gestures, and object handling, in the context of the medium and small industrial environment. The experimental findings validate that the proposed method achieves \(80.2\%\), \(81.05\%\) and \(80.19\%\) of average accuracy for movement, gesture, and object handling activities, which clearly outperformed the fuzzy c-means, particle-swarm optimization, and HMM-based activity recognition methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets analysed during the current study are available from the second author on reasonable request.

References

  1. Abbasi, M., Tahouri, R., & Rafiee, M. (2019). Enhancing the performance of the aggregated bit vector algorithm in network packet classification using gpu. PeerJ Computer Science, 5, e185.

    Article  Google Scholar 

  2. Abbasi, M., Najafi, A., Rafiee, M., et al. (2020). Efficient flow processing in 5g-envisioned sdn-based internet of vehicles using gpus. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5283–5292.

    Article  Google Scholar 

  3. Ahmed, I., Zhang, Y., & Jeon, G., et al. A blockchain-and artificial intelligence-enabled smart iot framework for sustainable city. International Journal of Intelligent Systems.

  4. Chen, J., Sun, Y., & Sun, S. (2021). Improving human activity recognition performance by data fusion and feature engineering. Sensors, 21(3), 692.

    Article  Google Scholar 

  5. Chen, K., Zhang, D., Yao, L., et al. (2021). Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities. ACM Computing Surveys (CSUR), 54(4), 1–40.

    Google Scholar 

  6. Chowdhury, A. K., Tjondronegoro, D., Chandran, V., et al. (2017). Physical activity recognition using posterior-adapted class-based fusion of multi-accelerometers data. IEEE Journal of Biomedical and Health Informatics, 99, 1–1.

    Google Scholar 

  7. Dallel, M., Havard, V., Baudry, D., et al. (2020). Inhard-industrial human action recognition dataset in the context of industrial collaborative robotics. In 2020 IEEE International Conference on Human-Machine Systems (ICHMS), IEEE (pp. 1–6).

  8. Dang, L. M., Min, K., Wang, H., et al. (2020). Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition, 108(107), 561.

    Google Scholar 

  9. Eltaeib, T., & Mahmood, A. (2018). Differential evolution: A survey and analysis. Applied Sciences, 8(10), 1945.

    Article  Google Scholar 

  10. Hu, J., Pan, Y., Li, T., et al. (2020). Tw-co-mfc: Two-level weighted collaborative fuzzy clustering based on maximum entropy for multi-view data. Tsinghua Science and Technology, 26(2), 185–198.

    Article  Google Scholar 

  11. Khosravi, M. R., & Samadi, S. (2019). Reliable data aggregation in internet of visar vehicles using chained dual-phase adaptive interpolation and data embedding. IEEE Internet of Things Journal, 7(4), 2603–2610.

    Article  Google Scholar 

  12. Khosravi, M. R., & Samadi, S. (2021). Bl-alm: A blind scalable edge-guided reconstruction filter for smart environmental monitoring through green iomt-uav networks. IEEE Transactions on Green Communications and Networking, 5(2), 727–736.

    Article  Google Scholar 

  13. Kilany, M., Hassanien, AE., & Badr, A. (2015). Accelerometer-based human activity classification using water wave optimization approach. In 2015 11th International Computer Engineering Conference (ICENCO), IEEE (pp. 175–180).

  14. Liao, X., Zheng, D., & Cao, X. (2021). Coronavirus pandemic analysis through tripartite graph clustering in online social networks. Big Data Mining and Analytics, 4(4), 242–251.

    Article  Google Scholar 

  15. Liu, Y., Pei, A., Wang, F., et al. (2021). An attention-based category-aware gru model for the next poi recommendation. International Journal of Intelligent Systems, 36(7), 3174–3189.

    Article  Google Scholar 

  16. Liu, Y., Li, D., Wan, S., et al. (2022). A long short-term memory-based model for greenhouse climate prediction. International Journal of Intelligent Systems, 37(1), 135–151.

    Article  Google Scholar 

  17. Maitre, J., Bouchard, K., & Gaboury, S. (2021). Alternative deep learning architectures for feature-level fusion in human activity recognition. Mobile Networks and Applications 1–11.

  18. Mittal, H., & Saraswat, M. (2019). An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm and Evolutionary Computation, 45, 15–32.

    Article  Google Scholar 

  19. Mittal, H., & Saraswat, M. (2020). A new fuzzy cluster validity index for hyper-ellipsoid or hyper-spherical shape close clusters with distant centroids. IEEE Transactions on Fuzzy Systems.

  20. Mittal, H., Pandey, A. C., Pal, R., et al. (2021). A new clustering method for the diagnosis of covid19 using medical images. Applied Intelligence, 51(5), 2988–3011.

    Article  Google Scholar 

  21. Nandy, S., Adhikari, M., Khan, M. A., et al. (2021). An intrusion detection mechanism for secured iomt framework based on swarm-neural network. IEEE Journal of Biomedical and Health Informatics.

  22. Nunes, U. M., Faria, D. R., & Peixoto, P. (2017). A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier. Pattern Recognition Letters, 99, 21–31.

    Article  Google Scholar 

  23. Nweke, H. F., Teh, Y. W., Al-Garadi, M. A., et al. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233–261.

    Article  Google Scholar 

  24. Pal, R., Mittal, H., & Saraswat, M. (2019). Optimal fuzzy clustering by improved biogeography-based optimization for leukocytes segmentation. In 2019 Fifth International Conference on Image Information Processing (ICIIP), IEEE (pp. 74–79).

  25. Pal, R., Saraswat, M., & Mittal, H. (2021). Improved bag-of-features using grey relational analysis for classification of histology images. Complex & Intelligent Systems, 7(3), 1429–1443.

    Article  Google Scholar 

  26. Pandey, AC., Tripathi, AK., Pal, R., et al. (2019). Spiral salp swarm optimization algorithm. In 2019 4th International Conference on Information Systems and Computer Networks (ISCON), IEEE (pp. 722–727).

  27. Pant, M., Zaheer, H., Garcia-Hernandez, L., et al. (2020). Differential evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence, 90(103), 479.

    Google Scholar 

  28. Price, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: A practical approach to global optimization. Springer.

  29. Raju, P., Subash, Y., Rishabh, K., et al. (2020). Eewc: Energy-efficient weighted clustering method based on genetic algorithm for hwsns. Complex & Intelligent Systems, 6(2), 391–400.

    Article  Google Scholar 

  30. Roitberg, A., Perzylo, A., Somani, N., et al. (2014). Human activity recognition in the context of industrial human-robot interaction. Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 1–10). IEEE: Asia-Pacific.

    Google Scholar 

  31. Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244.

    Article  Google Scholar 

  32. Saraswat, M., Arya, K., & Sharma, H. (2013). Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm and Evolutionary Computation, 11, 46–54.

    Article  Google Scholar 

  33. Singh, G., & Singh, A. (2020). A hybrid algorithm using particle swarm optimization for solving transportation problem. Neural Computing and Applications, 32(15), 11699–11716.

    Article  Google Scholar 

  34. Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    Article  MathSciNet  Google Scholar 

  35. Tripathi, A. K., Sharma, K., & Bala, M. (2018). Dynamic frequency based parallel k-bat algorithm for massive data clustering (dfbpkba). International Journal of System Assurance Engineering and Management, 9(4), 866–874.

    Google Scholar 

  36. Tripathi, A. K., Sharma, K., & Bala, M. (2018). A novel clustering method using enhanced grey wolf optimizer and mapreduce. Big Data Research, 14, 93–100.

    Article  Google Scholar 

  37. Tripathi, A. K., Sharma, K., & Bala, M. (2019). Parallel hybrid bbo search method for twitter sentiment analysis of large scale datasets using mapreduce. International Journal of Information Security and Privacy (IJISP), 13(3), 106–122.

    Article  Google Scholar 

  38. Tripathi, A. K., Sharma, K., Bala, M., et al. (2020). A parallel military-dog-based algorithm for clustering big data in cognitive industrial internet of things. IEEE Transactions on Industrial Informatics, 17(3), 2134–2142.

    Article  Google Scholar 

  39. Tripathi, A. K., Mittal, H., Saxena, P., et al. (2021). A new recommendation system using map-reduce-based tournament empowered whale optimization algorithm. Complex & Intelligent Systems, 7(1), 297–309.

    Article  Google Scholar 

  40. Tu, P., Li, J., Wang, H., et al. (2021). Non-linear chaotic features-based human activity recognition. Electronics, 10(2), 111.

    Article  Google Scholar 

  41. Weiss, G. M., & Lockhart, J. (2012). The impact of personalization on smartphone-based activity recognition. In Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence.

  42. Xue, Z., & Wang, H. (2021). Effective density-based clustering algorithms for incomplete data. Big Data Mining and Analytics, 4(3), 183–194.

    Article  Google Scholar 

  43. Zappi, P., Lombriser, C., Stiefmeier, T., et al. (2008). Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In European Conference on Wireless Sensor Networks (pp. 17–33). Springer.

  44. Zdravevski, E., Lameski, P., Trajkovik, V., et al. (2017). Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering. IEEE Access, 5, 5262–5280.

    Article  Google Scholar 

  45. Zhang, H., Babar, M., Tariq, M. U., et al. (2020). Safecity: Toward safe and secured data management design for iot-enabled smart city planning. IEEE Access, 8, 145256–145267.

    Article  Google Scholar 

  46. Zheng, X., Wang, M., & Ordieres-Meré, J. (2018). Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors, 18(7), 2146.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jaypee Institute of Information Technology, Noida, India

    Himanshu Mittal & Raju Pal

  2. Malaviya National Institute of Technology, Jaipur, India

    Ashish Kumar Tripathi

  3. Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

    Avinash Chandra Pandey

  4. College of Engineering, University of Bahrain, Isa-Town, Bahrain

    P. Venu

  5. SCMS School of Engineering and Technology, Ernakulam, India

    Varun G. Menon

Authors
  1. Himanshu Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashish Kumar Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Avinash Chandra Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Venu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Varun G. Menon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Raju Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raju Pal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mittal, H., Tripathi, A.K., Pandey, A.C. et al. A novel fuzzy clustering-based method for human activity recognition in cloud-based industrial IoT environment. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-03011-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-022-03011-y

Keywords

Search

Navigation