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Human fall detection using neuro-fuzzy models based on ensemble learning

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Abstract

Human falling may be due to a violent act, a heart attack or perhaps physical illness. Every year, many old people are being treated for injuries or even die in hospitals which caused by falling. From there, there is a long-standing need for a timely and inexpensive system that automatically identifying a falling person and then alert, which reduces the death rate and increases the likelihood of survival. Due to the low accuracy and a lot of faults in the recognition of the systems released in past years, this paper presented human falling detection by using neuro-fuzzy models and ensemble learning algorithms which presented to solve these problems. This showed the influence of the ensemble learning on performance of neuro-fuzzy models. However, it should be noted that feature selection and extraction methods in processing the dataset have their own impact. In this case, five kinds of feature selection/extraction algorithms are used. Five neuro-fuzzy models are used in this research: normalized radial basis function (NRBF) network, radial basis function (RBF) network, adaptive neuro-fuzzy inference system (ANFIS), local linear model trees (LOLIMOT) and generalized regression neural networks (GRNN). LOLIMOT model, by using correlation based features selection algorithm, reached the highest answer with the accuracy of 0.796. The results of these models are entered into the two ensemble learning algorithms, single majority vote and weighted majority vote, which weighted majority vote algorithm reached an accuracy of 0.87917 by using principal component analysis algorithm, and was the highest answer among of all the models used in this paper.

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References

  1. Fan, Y., Levine, M.D., Wen, G., Qiu, S.: A deep neural network for real-time detection of falling humans in naturally occurring scenes. Neurocomputing 260, 43–58 (2017)

    Article  Google Scholar 

  2. Rashidpour, M., Mohammadi, F., Fathi, A.: Fall detection using adaptive neuro-fuzzy inference system. Int. J. Multimedia Ubiquitous Eng. 11(4), 91–106 (2016)

    Article  Google Scholar 

  3. Zhao, H., Zhao, Y., Zhong, H.: Development of a wearable-sensor-based fall detection system. Int. J. Telemed. Appl. (2015).

  4. Charlon, Y., Fourty, N., Bourennane, W., Campo, E.: Design and evaluation of a device worn for fall detection and localization: application for the continuous monitoring of risks incurred by dependents in an Alzheimer’s care unit. Expert Syst. Appl. 40(18), 7316–7330 (2013)

    Article  Google Scholar 

  5. Huynh, Q., Nguyen, U., Irazabal, L., Ghassemian, N., Tran, B.: Optimization of an accelerometer and gyroscope-based fall detection algorithm. J. Sensors 2015 (2015).

  6. Michal, B.K.: Improving fall detection by the use of depth sensor and accelerometer. Neurocomputing 168, 637–645 (2015)

    Article  Google Scholar 

  7. Vallabh, P., Malekian, R., Ye, N., Bogatinoska, D.C.: Fall detection using machine learning algorithms. In: 2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–9 (2016).

  8. Oliver, Sh., Anuradha, M., Jean Justus, J., Maheshwari, N.: Optimized low computational algorithm for elderly fall detection based on machine learning techniques. Int. J. Med. Sci. 29(20) (2018).

  9. Rodriguesa, T., Salgadoa, D., Cordeiroc, M., Osterwald, K., Filhod, T., Lucena, V., Naves, E., Murraya, N.: Fall detection system by machine learning framework for public health. Procedia Comput. Sci. 141, 358–365 (2018)

    Article  Google Scholar 

  10. Serpen, G., Hasan Khan, R.: Real-time detection of human falls in progress: machine learning approach. Procedia Comput. Sci. 140, 238–247 (2018)

    Article  Google Scholar 

  11. Kim, T., Choi, A., Heo, H., Kim, K., Lee, K., Mun, J.: Machine learning-based pre-impact fall detection model to discriminate various types of fall. ASME J. Biomech. Eng. 141(8), 081010 (2019)

    Article  Google Scholar 

  12. Anishchenko, L., Zhuravlev, A., Chizh, M.: Fall detection using multiple bioradars and convolutional neural networks. Sensors (Basel) 19(24), 55–69 (2019)

    Article  Google Scholar 

  13. Yacchirema, D., de Puga, J.S., Palau, C., Esteve, M.: Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers. Ubiquit Comput. 23, 801–817 (2019)

    Article  Google Scholar 

  14. Chelli, A., Pätzold, M.: A machine learning approach for fall detection and daily living activity recognition. IEEE Access 7, 38670–38687 (2019)

    Article  Google Scholar 

  15. Shrivastava, R., Pandey, M.: Human fall detection using efficient kernel and eccentric approach. IJEHMC 12(1), 62–80 (2020)

    Google Scholar 

  16. Md Salleh, S., Mohd Yusoff, A.H., Ngadimon, K., Koh, C.Z.: Neural network algorithm-based fall detection modelling. IJIE 12(3), 138–150 (2020)

    Google Scholar 

  17. Broomhead, D.S., Lowe, D.: Multi-variale functional interpolation and adaptive networks. Complex Syst. 2, 321–355 (1990)

    MATH  Google Scholar 

  18. Vavoulas, G., Pediaditis, M., Spanakis, E.G., Tsiknakis, M.: The MobiFall dataset: an initial evaluation of fall detection algorithms using smartphones. In: 13th IEEE International Conference on BioInformatics and BioEngineering (pp. 1–4) (2013).

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Shirin Kordnoori, Arash Sharifi & Hamed Shah-Hosseini

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  1. Shirin Kordnoori
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  2. Arash Sharifi
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  3. Hamed Shah-Hosseini
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Correspondence to Arash Sharifi.

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Kordnoori, S., Sharifi, A. & Shah-Hosseini, H. Human fall detection using neuro-fuzzy models based on ensemble learning. Prog Artif Intell 11, 219–232 (2022). https://doi.org/10.1007/s13748-022-00276-4

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  • DOI: https://doi.org/10.1007/s13748-022-00276-4

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