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Open AccessFeature PaperArticle

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

Departamento de Tecnología Electrónica, ETSI Telecomunicación, Universidad de Málaga, 29071 Málaga, Spain
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Symmetry 2020, 12(4), 649; https://doi.org/10.3390/sym12040649
Received: 23 March 2020 / Revised: 13 April 2020 / Accepted: 15 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Information Technologies and Electronics)
Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users. View Full-Text
Keywords: fall detection system; inertial sensors; wearable; accelerometer; gyroscope; convolutional neural networks fall detection system; inertial sensors; wearable; accelerometer; gyroscope; convolutional neural networks
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MDPI and ACS Style

Casilari, E.; Álvarez-Marco, M.; García-Lagos, F. A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems. Symmetry 2020, 12, 649. https://doi.org/10.3390/sym12040649

AMA Style

Casilari E, Álvarez-Marco M, García-Lagos F. A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems. Symmetry. 2020; 12(4):649. https://doi.org/10.3390/sym12040649

Chicago/Turabian Style

Casilari, Eduardo; Álvarez-Marco, Moisés; García-Lagos, Francisco. 2020. "A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems" Symmetry 12, no. 4: 649. https://doi.org/10.3390/sym12040649

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