Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments

Kunhoth, Jayakanth; Karkar, AbdelGhani; Al-Maadeed, Somaya; Al-Attiyah, Asma

doi:10.1186/s12942-019-0193-9

Cited by 34 publications

(12 citation statements)

References 48 publications

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“…The convolutional layers are responsible to extract significant feature maps from the inputs by computing the weighted sum (51,52). The extracted feature maps are then passed through the activation functions, and a bias is added to obtain an output.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19

Hassan

Albahli

Javed

et al. 2022

Front. Public Health

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Covid-19 has become a pandemic that affects lots of individuals daily, worldwide, and, particularly, the widespread disruption in numerous countries, namely, the US, Italy, India, Saudi Arabia. The timely detection of this infectious disease is mandatory to prevent the quick spread globally and locally. Moreover, the timely detection of COVID-19 in the coming time is significant to well cope with the disease control by Governments. The common symptoms of COVID are fever as well as dry cough, which is similar to the normal flu. The disease is devastating and spreads quickly, which affects individuals of all ages, particularly, aged people and those with feeble immune systems. There is a standard method employed to detect the COVID, namely, the real-time polymerase chain reaction (RT-PCR) test. But this method has shortcomings, i.e., it takes a long time and generates maximum false-positive cases. Consequently, we necessitate to propose a robust framework for the detection as well as for the estimation of COVID cases globally. To achieve the above goals, we proposed a novel technique to analyze, predict, and detect the COVID-19 infection. We made dependable estimates on significant pandemic parameters and made predictions of infection as well as potential washout time frames for numerous countries globally. We used a publicly available dataset composed by Johns Hopkins Center for estimation, analysis, and predictions of COVID cases during the time period of 21 April 2020 to 27 June 2020. We employed a simple circulation for fast as well as simple estimates of the COVID model and estimated the parameters of the Gaussian curve, utilizing a parameter, namely, the least-square parameter curve fitting for numerous countries in distinct areas. Forecasts of COVID depend upon the potential results of Gaussian time evolution with a central limit theorem of data the Covid prediction to be justified. For gaussian distribution, the parameters, namely, extreme time and thickness are regulated using a statistical Y2 fit for the aim of doubling times after 21 April 2020. Moreover, for the detection of COVID-19, we also proposed a novel technique, employing the two features, namely, Histogram of Oriented Gradients and Scale Invariant Feature Transform. We also designed a CNN-based architecture named COVIDDetectorNet for classification purposes. We fed the extracted features into the proposed COVIDDetectorNet to detect COVID-19, viral pneumonia, and other lung infections. Our method obtained an accuracy of 96.51, 92.62, and 86.53% for two, three, and four classes, respectively. Experimental outcomes illustrate that our method is reliable to be employed for the forecast and detection of COVID-19 disease.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19

Hassan

Albahli

Javed

et al. 2022

Front. Public Health

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“…CamNav, a vision-based navigation system captures images to recognize the current location of users without any indoor localization devices installed. To improve the accuracy of localization a combination of scale-invariant feature transform (SIFT) and multi-scale local binary pattern (MSLBP) features were used (11).…”

Section: Wearable Devices Based On Various Sensorsmentioning

confidence: 99%

Sensor Based Wearable Devices for Road Navigation

Tyagi¹,

Singh

2022

ECS Trans.

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Internet of Things (IoT)-based applications play a significant role and are highly influenced by the global positioning system (GPS). GPS has a wide range of applications and benefits in tracking and route finding. A GPS tracker can help to explore numerous possibilities in various domains such as the health field, safety (to protect various assets), pattern recognition, logistics, in business models by using navigation systems embedded with sensors, and many more. Smart IoT wearable devices are the next step of evolution in the field of navigation using visual, auditory, ultrasonic, and other technologies. This paper introduces the various IoT sensors used in navigation systems and some prominent existing applications. This paper also provides a survey of existing wearable devices and concludes that the correct choice of sensors can easily overcome the challenges of navigation.

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“…Immediately after the input layer, convolutional layers are defined with the number of filters, filter window size, stride, padding and activation as the parameters. Convolutional layers are used to extract meaningful feature maps for the input location by calculating the weighted sum [ 17 , 18 ]. Then, each feature map is passed through an activation function, and bias is added to form the output.…”

Section: Introductionmentioning

confidence: 99%

A review of deep learning-based detection methods for COVID-19

Subramanian

Elharrouss

Al‐Maadeed

et al. 2022

Computers in Biology and Medicine

Self Cite

101

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COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.

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Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments

Cited by 34 publications

References 48 publications

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19

Sensor Based Wearable Devices for Road Navigation

A review of deep learning-based detection methods for COVID-19

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