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

Departamento de Tecnología Electrónica, Universidad de Málaga, ETSI Telecomunicación, 29071 Málaga, Spain
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Sensors 2020, 20(5), 1466; https://doi.org/10.3390/s20051466
Received: 5 February 2020 / Revised: 4 March 2020 / Accepted: 5 March 2020 / Published: 6 March 2020
(This article belongs to the Special Issue Healthcare Monitoring and Management with Artificial Intelligence)
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. View Full-Text
Keywords: fall detection system; accelerometers; body sensor networks; classification algorithms; convolutional neural networks; machine learning; wearable sensors fall detection system; accelerometers; body sensor networks; classification algorithms; convolutional neural networks; machine learning; wearable sensors
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Casilari, E.; Lora-Rivera, R.; García-Lagos, F. A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets. Sensors 2020, 20, 1466.

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