RT journal article
T1 Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning
A1 Sajid Butt, Fatima
A1 La Blunda, Luigi
A1 Wagner, Matthias F.
A1 Schäfer, Jörg
A1 Medina Bulo, María Inmaculada
A1 Gómez-Ullate Oteiza, David
A2 Ingeniería Informática
K1 electrocardiogram (ECG)
K1 wavelet transform
K1 signal processing
K1 transfer learning
K1 human activity recognition
K1 neural network
AB Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel," but also presents a lightweight solution to otherwise computationally heavy problems.
PB MDPI
SN 2078-2489
YR 2021
FD 2021-02
LK http://hdl.handle.net/10498/24747
UL http://hdl.handle.net/10498/24747
LA eng
NO This research was funded by the research support program of Fb2, Frankfurt University of Applied Sciences. The research of D.G.-U. has been supported in part by the Spanish MICINN under grants PGC2018-096504-B-C33 and RTI2018-100754-B-I00, the European Union under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (project FEDER-UCA18-108393). The research of I.M.-B. has been supported in part by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33].
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026