A comprehensive study on the use of artificial neural networks in wearable fall detection systems

Casilari, E.; Garcìa-Lagos, Francisco

doi:10.1016/j.eswa.2019.07.028

Cited by 33 publications

(18 citation statements)

References 60 publications

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“…For example, the study by Casilari and Garcia-Lagos in [18], which reviews in detail all those FDSs that are based on artificial neural networks, reveals that up to 11 out of the 59 revised proposals utilize the angular velocity captured by the gyroscope to obtain some of the input features of the neural architecture that classifies the movements. To extend that bibliographical analysis, Table 1 summarizes those works that describe a system intended for fall detection in which the triaxial components of the angular velocity (or some parameters derived from them) are employed as inputs of the classifying algorithm.…”

Section: Related Workmentioning

confidence: 99%

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

2020

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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.

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Section: Related Workmentioning

confidence: 99%

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

2020

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“…The main requirement for an ML algorithm is the availability of the data to train the models. Some ML techniques such as deep neural networks are especially dependent on the availability of large amounts of data [13]. Focusing on fall detection, the problem is not only related to the data gathering, but also with the technology and methodology used to retrieve the fall information.…”

Section: Related Workmentioning

confidence: 99%

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

et al. 2020

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People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

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“…Moreover, the smartphone battery life and battery consumption may impede usage. (Casilari-Perez and Garcia-Lagos [ 17 ] also stated that there are several drawbacks given that fall detection solution must coexist with the complex heterogeneous application running concurrently on a smartphone.…”

Section: Introductionmentioning

confidence: 99%

IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Han

Kang

Choi

2020

Sensors

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Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

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A comprehensive study on the use of artificial neural networks in wearable fall detection systems

Cited by 33 publications

References 60 publications

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

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

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

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