Abstract
Early 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 daily-life activities tracking, able to detect immediately a high-risk fall via a machine learning framework. Towards this direction, accelerometer devices are used widely for the assessment of fall risk. Although there is a plethora of studies under this perspective with promising results, several challenges still remain such as the extremely demanding data and power management as well as the discovery of false positive falls. In this work we propose a complete methodology based on the combination of the computationally demanding convolutional neural networks along with a lightweight change detection method. Our basic assumption is that it is possible to control computational resources for the operation of a classifier, suffice to be activated when a strong change in user’s movements is identified. The proposed methodology was applied to real experimental data providing reliable results that justify the original hypothesis.
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Acknowledgments
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.
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Tasoulis, S.K., Mallis, G.I., Georgakopoulos, S.V., Vrahatis, A.G., Plagianakos, V.P., Maglogiannis, I.G. (2019). Deep Learning and Change Detection for Fall Recognition. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_22
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DOI: https://doi.org/10.1007/978-3-030-20257-6_22
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