COVID-19: Challenges and its Technological Solutions using IoT

Ajaz, Farhana; Naseem, Mohammad; Sharma, Sparsh; Shabaz, Mohammad; Dhiman, Gaurav

doi:10.2174/1573405617666210215143503

Cited by 40 publications

(28 citation statements)

References 30 publications

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“…We are going to use the invisible watermarking technique on standard images and medical images. COVID-19 has brought many challenges in forging medical records [ 3 ], so a robust method is needed to combat the problems in particular in various fields of diagnosis of neurodegenerative disorders, heart diseases, and lung issues. Neurodegenerative disorders are disorders that impact the lifestyle of humans that cause issues in breathing, movement, and talking and heart-related issues.…”

Section: Introductionmentioning

confidence: 99%

A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications

Sharma

Bagga

Sobti

et al. 2021

Computational and Mathematical Methods in Medicine

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The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder’s intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this paper, a new method of watermarking is proposed on both standard and medical images. The paper addresses the use of digital rights management in medical field applications such as embedding the watermark in medical images related to neurodegenerative disorders, lung disorders, and heart issues. The various quality parameters are used to figure out the evaluation of the developed method. In addition, the testing of the watermarking scheme is done by applying various signal processing attacks.

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Section: Introductionmentioning

confidence: 99%

A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications

Sharma

Bagga

Sobti

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Experiments demonstrate that this approach works under a variety of load conditions. Additionally, it obtains a high degree of accuracy [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Capsule Network Optimization Hierarchical Convolution Algorithm for Mental Health Recognition

Bhowmik¹,

Bhusnurmath

Sahu

et al. 2022

Computational and Mathematical Methods in Medicine

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To address the shortcomings of standard convolutional neural networks (CNNs), the model structure is complex, the training period is lengthy, and the data processing technique is single. A modified capsule network is presented to optimize hierarchical convolution—the algorithm for identifying mental health conditions. To begin, two types of data processing are performed on the original vibration data: wavelet noise reduction and wavelet packet noise reduction; this retains more valuable information for mental health identification in the original signal; secondly, the CNN employs the concept of hierarchical convolution, and three distinct scaled convolution kernels are utilized to extract features from numerous angles; ultimately, the convolution kernel’s extracted features are fed into the pruning strategy’s capsule network for mental health diagnosis. The enhanced capsule network has the potential to significantly speed up mental health identification while maintaining accuracy. It is time to address the issue of the CNN structure being too complex and the recognition impact being inadequate. The experimental findings indicate that the suggested algorithm achieves a high level of recognition accuracy while consuming a small amount of time.

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“…Stroke, on the other hand, is considered to affect the autonomic nervous system, resulting in cardiovascular responses [4,5]. In the days following an ischemic stroke, heart attack, cardiac arrest, congestive heart failure, and other abnormal heart rhythms such as atrial fibrillation are all considered to be one of the leading causes of morbidity and even mortality [6].…”

Section: Introductionmentioning

confidence: 99%

An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

Almarzouki

Alsulami

Rizwan

et al. 2021

Journal of Healthcare Engineering

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In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study’s aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3–10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3–10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha ( p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta ( p < 0.001), alpha ( p < 0.01), and beta-1 ( p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.

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COVID-19: Challenges and its Technological Solutions using IoT

Cited by 40 publications

References 30 publications

A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications

A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications

Improved Capsule Network Optimization Hierarchical Convolution Algorithm for Mental Health Recognition

An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

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