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Change detection and convolution neural networks for fall recognition

  • S.I. : Emerging applications of Deep Learning and Spiking ANN
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Abstract

Accurate fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for motion tracking, allowing immediate detection of high-risk falls via a machine learning framework. Toward this direction, accelerometer devices are widely used for the assessment of fall risk. Although there exist a plethora of studies under this perspective, several challenges still remain, such as dealing simultaneously with extremely demanding data management, power consumption and prediction accuracy. In this work, we propose a complete methodology based on the cooperation of deep learning for signal classification along with a lightweight control chart method for change detection. Our basic assumption is that it is possible to control computational resources by selectively allowing the operation of a relatively heavyweight, but very efficient classifier, when it is truly required. The proposed methodology was applied to real experimental data providing the reliable results that justify the original hypothesis.

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Notes

  1. https://www.who.int/en/news-room/fact-sheets/detail/falls/.

  2. https://userweb.cs.txstate.edu/~hn12/data/SmartFallDataSet/.

  3. https://sites.google.com/up.edu.mx/challenge-up-2019/data.

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Acknowledgements

This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under Grant Agreement No 1901. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.

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Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Georgios I. Mallis, Aristidis G. Vrahatis & Vassilis P. Plagianakos

  2. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Ilias G. Maglogiannis

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  1. Spiros V. Georgakopoulos
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Correspondence to Sotiris K. Tasoulis.

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Georgakopoulos, S.V., Tasoulis, S.K., Mallis, G.I. et al. Change detection and convolution neural networks for fall recognition. Neural Comput & Applic 32, 17245–17258 (2020). https://doi.org/10.1007/s00521-020-05208-8

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