scholarly journals Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1644 ◽  
Author(s):  
Guto Santos ◽  
Patricia Endo ◽  
Kayo Monteiro ◽  
Elisson Rocha ◽  
Ivanovitch Silva ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Fog Computing ◽  
Open Data ◽  
Fall Detection ◽  
Public Health Issue ◽  
Global Public Health ◽  
Severe Injuries ◽  
Using Data ◽  
Sensitivity Specificity

Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain.

scholarly journals A Smart Contract-based Access Control Architecture for Telemedicine During Covid-19

2021 ◽  
Author(s):  
hideyat zerga ◽  
Asma AMRAOUI ◽  
badr BENMAMMAR
Keyword(s):  
Access Control ◽  
Fog Computing ◽  
Physiological Data ◽  
Public Health Issue ◽  
Global Public Health ◽  
Distributed Services ◽  
Patient Health ◽  
Set Up ◽  
Fundamental Requirement ◽  
Do So

Abstract In the fight against the COVID-19 epidemic that is currently a major global public health issue, social distancing has been imposed to prevent the massive transmission, thus doctors in hospitals have turned to telemedicine in order to be able to monitor their patient notably those suffering from chronic diseases. To do so, patients need to share their physiological data with doctors. In order to share this data safely, prevent malicious users from tampering with it and protect the privacy of patients, access control becomes a fundamental requirement. In order to set up a real-time (Internet of Thing) IoT enabled healthcare system (HS) scenario like telemedicine, Fog computing (FC) seems to be the best solution comparing to Cloud computing since it provides low latency, highly mobile and geo-distributed services and temporary storage. In this paper, the focus is on access control in the telemedicine systems. Our proposal is based, on one hand, the concept of Fog computing to ensure the distributed aspect needed in the monitoring of patient health remotely; and on the other hand Blockchain (BC) smart contracts, in order to provide a dynamic, optimized and self-adjusted access control.

scholarly journals Image editing-based data augmentation for illumination-insensitive background subtraction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dimitrios Sakkos ◽  
Edmond S. L. Ho ◽  
Hubert P. H. Shum ◽  
Garry Elvin
Keyword(s):  
Deep Learning ◽  
Pilot Study ◽  
Background Subtraction ◽  
Data Augmentation ◽  
Point Of View ◽  
Content Type ◽  
Illumination Changes ◽  
Image Appearance ◽  
Using Data ◽  
Limited Training Samples

PurposeA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.Design/methodology/approachIn our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.FindingsExperimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.Originality/valueSuch data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation

scholarly journals Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6126
Author(s):  
Tae Hyong Kim ◽  
Ahnryul Choi ◽  
Hyun Mu Heo ◽  
Hyunggun Kim ◽  
Joung Hwan Mun
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Fall Detection ◽  
Learning Model ◽  
Measurement Unit ◽  
Percentage Error ◽  
Loss Of Balance ◽  
Impact Acceleration ◽  
Deep Learning Model

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall’s impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

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