Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Mamdiwar, Shwetank Dattatraya; Akshith, R; Shakruwala, Zainab; Chadha, Utkarsh; Srinivasan, Kathiravan; Chang, Chuan‐Yu

doi:10.3390/bios11100372

Cited by 122 publications

(72 citation statements)

References 109 publications

(160 reference statements)

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“…Industrial control, health monitoring, traffic control, and environmental monitoring are among the applications of WSNs [ 33 , 36 , 37 ]. In addition, some of the developed technologies such as ZigBee, LoRA, and LoRaWAN can be included in the WSN: LoRa (long-range): For the IoT, the low power wide area network (LoRaWAN) protocol provides a long-range, low-power, low-bitrate communication method [ 38 ]. If you have a battery-powered device, this method works well.…”

Section: Background and Present Research On The Iotmentioning

confidence: 99%

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

Salih

Rashid

Radovanović

et al. 2022

Sensors

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There is no doubt that new technology has become one of the crucial parts of most people’s lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.

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Section: Background and Present Research On The Iotmentioning

confidence: 99%

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

Salih

Rashid

Radovanović

et al. 2022

Sensors

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“…Blockchain is engaged in offering services pertaining to finance, voting, supply chain monitoring, and smart contracts. It can be deployed to secure data generated by IoT applications [ 145 , 146 ].…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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“…Attique Khan et al, 2021d;Mamdiwar et al, 2021;Srinivasan et al, 2021). AD is a neurological disorder identified through brain imaging, and there are many works focused on classifying AD through brain images with the help of machine learning or deep learning techniques.…”

Section: Alzheimer's Disease and Machine Learning Algorithmsmentioning

confidence: 99%

“…Artificial Intelligent models have been widely deployed in genetics research ( Mahendran et al, 2020 ). Deep learning approaches remove certain data pre-processing, which is usually deployed in machine learning ( Srinivasan et al, 2017 ; Agarwal et al, 2018 ; Chakriswaran et al, 2019 ; Khan et al, 2021a ; Khan et al, 2021b ; Khan et al, 2021c ) ( Sanchez-Riera et al, 2018 ; Srinivasan et al, 2020 ; Afza et al, 2021 ; Ashwini et al, 2021 ; Attique Khan et al, 2021 ; Khan et al, 2021d ; Mamdiwar et al, 2021 ; Srinivasan et al, 2021 ). AD is a neurological disorder identified through brain imaging, and there are many works focused on classifying AD through brain images with the help of machine learning or deep learning techniques.…”

Section: Background and Motivationmentioning

confidence: 99%

Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

et al. 2021

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Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.

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Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Cited by 122 publications

References 109 publications

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

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