A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

Sensors
Eduardo CasilariFrancisco García-Lagos

Abstract

Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset.

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Citations

Dec 31, 2020·Journal of Healthcare Engineering·Eduardo CasilariJosé M Cano-García
Aug 13, 2021·Health Informatics Journal·Mikael Ange Mousse, Béthel Atohoun
Aug 27, 2021·Biosensors·José Antonio Santoyo-RamónJosé Manuel Cano-García
Aug 28, 2021·Frontiers in Medicine·Jean-Jacques LemaireFrançois Berry
Aug 31, 2021·Journal of Imaging·Abderrazak IazziRachid Oulad Haj Thami

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MATLAB
MATLAB Toolbox TM
SisFall
Deep Learning

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