scholarly journals Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1354 ◽  
Author(s):  
Gaojing Wang ◽  
Qingquan Li ◽  
Lei Wang ◽  
Yuanshi Zhang ◽  
Zheng Liu
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Research Problem ◽  
Wearable Devices ◽  
The Past ◽  
Machine Learning Methods ◽  
Open Research ◽  
Different Types

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

scholarly journals eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4565 ◽  
Author(s):  
Fabián Riquelme ◽  
Cristina Espinoza ◽  
Tomás Rodenas ◽  
Jean-Gabriel Minonzio ◽  
Carla Taramasco
Keyword(s):  
Older Adults ◽  
Young People ◽  
Elderly Care ◽  
Fall Detection ◽  
The Elderly ◽  
Research Area ◽  
Wearable Devices ◽  
Learning Approaches ◽  
Negative Consequences ◽  
Public Datasets

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

scholarly journals Online Fall Detection using Recurrent Neural Networks on Smart Wearable Devices

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Mirto Musci ◽  
Daniele De Martini ◽  
Nicola Blago ◽  
Tullio Facchinetti ◽  
Marco Piastra
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Fall Detection ◽  
Wearable Devices ◽  
Smart Wearable

scholarly journals Image Compression Based on Deep Learning: A Review

2021 ◽  
pp. 62-76
Author(s):  
Hajar Maseeh Yasin ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Compression ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Review Paper ◽  
High Accuracy ◽  
Machine Learning Methods ◽  
Digital Era ◽  
Different Types

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

scholarly journals Heterogeneous Network Representation Learning

2020 ◽  
Author(s):  
Yuxiao Dong ◽  
Ziniu Hu ◽  
Kuansan Wang ◽  
Yizhou Sun ◽  
Jie Tang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Heterogeneous Network ◽  
Representation Learning ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Relational Properties ◽  
Open Research ◽  
Different Types ◽  
Graph Neural Networks

Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations. The goal of this problem is to automatically project objects, most commonly, vertices, in an input heterogeneous network into a latent embedding space such that both the structural and relational properties of the network can be encoded and preserved. The embeddings (representations) can be then used as the features to machine learning algorithms for addressing corresponding network tasks. To learn expressive embeddings, current research developments can fall into two major categories: shallow embedding learning and graph neural networks. After a thorough review of the existing literature, we identify several critical challenges that remain unaddressed and discuss future directions. Finally, we build the Heterogeneous Graph Benchmark to facilitate open research for this rapidly-developing topic.

scholarly journals A Review of Soft Computing Techniques for Gene Prediction

ISRN Genomics ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Neelam Goel ◽  
Shailendra Singh ◽  
Trilok Chand Aseri
Keyword(s):  
Soft Computing ◽  
Genome Annotation ◽  
Gene Prediction ◽  
Research Problem ◽  
Future Research ◽  
Research Directions ◽  
The Past ◽  
Open Research ◽  
Soft Computing Techniques ◽  
Future Research Directions

In the past decade, various genomes have been sequenced in both plants and animals. The falling cost of genome sequencing manifests a great impact on the research community with respect to annotation of genomes. Genome annotation helps in understanding the biological functions of the sequences of these genomes. Gene prediction is one of the most important aspects of genome annotation and it is an open research problem in bioinformatics. A large number of techniques for gene prediction have been developed over the past few years. In this paper a theoretical review of soft computing techniques for gene prediction is presented. The problem of gene prediction, along with the issues involved in it, is first described. A brief description of soft computing techniques, before discussing their application to gene prediction, is then provided. In addition, a list of different soft computing techniques for gene prediction is compiled. Finally some limitations of the current research and future research directions are presented.

A REVIEW ON STATE-OF-THE-ART FACE RECOGNITION APPROACHES

Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750025 ◽  
Author(s):  
ZAHID MAHMOOD ◽  
NAZEER MUHAMMAD ◽  
NARGIS BIBI ◽  
TAUSEEF ALI
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Rate ◽  
Research Problem ◽  
Critical Discussion ◽  
The Past ◽  
Open Research ◽  
Comprehensive Survey ◽  
Facial Images ◽  
Enormous Number

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

scholarly journals Deep Learning and Uniform LBP Histograms for Position Recognition of Elderly People with Privacy Preservation

2021 ◽  
Vol 16 (5) ◽  
Author(s):  
Monia Hamdi ◽  
Heni Bouhamed ◽  
Abeer AlGarni ◽  
Hela Elmannai ◽  
Souham Meshoul
Keyword(s):  
Neural Networks ◽  
Health Informatics ◽  
Independent Living ◽  
Privacy Preservation ◽  
Local Binary Pattern ◽  
Computational Cost ◽  
The Elderly ◽  
Video Images ◽  
Complex Architectures ◽  
Current Context

For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs.

Smart Wheelchair with Voice Control for Physically Challenged People

2021 ◽  
Vol 6 (7) ◽  
pp. 97-102
Author(s):  
Md Abdullah Al Rakib ◽  
Salah Uddin ◽  
Md. Moklesur Rahman ◽  
Shantanu Chakraborty ◽  
Fysol Ibna Abbas
Keyword(s):  
The Elderly ◽  
The Past ◽  
Smart Wheelchairs ◽  
Personal Effort ◽  
Voice Control ◽  
Different Types ◽  
Wheel Chair ◽  
Handicapped People ◽  
Physically Challenged ◽  
Physically Handicapped

A wheel chair is a mechanically operated device that allows the user to move about independently. This minimizes the user's personal effort and force required to move the wheelchair wheels. Furthermore, it allows visually or physically handicapped people to go from one location to another. Voice commands and button controls can be used to operate wheelchairs. In recent years, there has been a lot of interest in smart wheelchairs. These gadgets are very handy while traveling from one location to another. The devices can also be utilized in nursing homes where the elderly have difficulties moving about. For individuals who have lost their mobility, the gadgets are a godsend. Different types of smart wheelchairs have been created in the past, but new generations of wheelchairs are being developed and utilized that incorporate the use of artificial intelligence and therefore leave the user with a little to tamper with. The project also intends to develop a comparable wheel chair that has some intelligence and so assists the user in his or her mobility.

scholarly journals Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 351
Author(s):  
Renato Bellotti ◽  
Romana Boiger ◽  
Andreas Adelmann
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimal Operation ◽  
Particle Accelerator ◽  
Data Driven ◽  
Particle Accelerators ◽  
Linear Dynamics ◽  
Multi Objective ◽  
Scientific Exploration ◽  
Different Types

Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future online and real-time multi-objective optimisation of particle accelerators.

Study of the Molecular Architecture of Keratin Filaments by Electron Microscopy

1982 ◽  
Vol 40 ◽  
pp. 118-119
Author(s):  
U. Aebi ◽  
P. Rew ◽  
T.-T. Sun
Keyword(s):  
Electron Microscopy ◽  
Structural Organization ◽  
Cell Types ◽  
Structural Features ◽  
Molecular Architecture ◽  
The Past ◽  
Keratin Filaments ◽  
Different Types ◽  
10 Nm Filaments ◽  
Different Cell Types

Various types of intermediate-sized (10-nm) filaments have been found and described in many different cell types during the past few years. Despite the differences in the chemical composition among the different types of filaments, they all yield common structural features: they are usually up to several microns long and have a diameter of 7 to 10 nm; there is evidence that they are made of several 2 to 3.5 nm wide protofilaments which are helically wound around each other; the secondary structure of the polypeptides constituting the filaments is rich in ∞-helix. However a detailed description of their structural organization is lacking to date.

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