Assessment of factors affecting implementation of IoT based smart skin monitoring systems

Johri, Amar; Bhadula, Shuchi; Sharma, Sachin; Shukla, Amal Shankar

doi:10.1016/j.techsoc.2022.101908

Cited by 18 publications

(4 citation statements)

References 15 publications

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“… Patient monitoring : DL can be used to monitor patients remotely and detect changes in health status that may require intervention 49 . For example, DL can analyze data from wearable devices to predict the risk of falls in elderly patients 50 or to monitor patients with chronic diseases such as asthma, 51 and heart failure 52–56 Drug discovery : DL can be used to predict the efficacy and safety of new drugs.…”

Section: Approaches For Personalized Health Monitoringmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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Section: Approaches For Personalized Health Monitoringmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…* Patient monitoring: DL can be used to monitor patients remotely and detect changes in health status that may require intervention [45]. For example, DL can analyze data from wearable devices to predict the risk of falls in elderly patients [46] or to monitor patients with chronic diseases such as asthma [47], and heart failure [48][49][50][51][52].…”

Section: Deep Learning For Predicting Health Outcomesmentioning

confidence: 99%

Deep Learning for Personalized Health Monitoring and Prediction: A Review

Damaševicˇius,

Jagatheesaperumal

et al. 2023

Preprint

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Personalized health monitoring and prediction have become essential for improving health- care delivery, especially with the growing prevalence of chronic diseases and an aging population. Deep learning (DL) has emerged as a promising approach for developing personalized health mon- itoring systems that can predict health outcomes accurately and efficiently. With the increasing availability of personal health data, DL-based methods have emerged as a promising approach to improve healthcare delivery by providing accurate and timely predictions of health outcomes. This article provides a comprehensive review of the recent developments in the application of DL for personalized health monitoring and prediction. It summarizes various DL architectures and their applications for personalized health monitoring, including wearable devices, electronic health records, and social media data. Furthermore, the article also explores the challenges and future directions for the application of DL in personalized health monitoring. valuable insights into the potential of DL for personalized health monitoring and prediction.

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“…The key technology to accurate identification is reliable material identification technology. Moreover, the material identification technology is also an indispensable part of smart skin which can be employed in human body monitoring [3].…”

Section: Introductionmentioning

confidence: 99%

A novel material identification method for different thermal conductivities materials based on the micro thermoelectric generator

Chen,

Liu,

Shan

et al. 2023

Meas. Sci. Technol.

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An Artificial Intelligence (AI) technical system is the foundation of the Internet of Things (IoT). While in the technical system, the material identification technology is the key point. Material identification technology can be applied to AI robots and smart skin. However, most of the traditional material identification technologies have limitations such as high cost and complicated operation, while the low-cost ones have the problem of low accuracy. To accurately, efficiently, and simply identify objects is a bottleneck that researchers urgently need to overcome. Based on the thermoelectric effect, a material identification theory is proposed and a prototype is fabricated in this paper. A series of experiments are carried out to test the performance of the prototype. According to the results, the prototype can precisely identify metal materials and nonmetal materials less than 3 s. There are many merits of the prototype, such as high identification accuracy, low cost, and low maintenance difficulty. Besides, considering complex surface of materials may decrease the identification accuracy, this paper proposes a kind of flexible thermal conductive material to improve identification accuracy. It is verified that the identification accuracy is improved with the flexible thermal conductive material.

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Assessment of factors affecting implementation of IoT based smart skin monitoring systems

Cited by 18 publications

References 15 publications

Deep learning for personalized health monitoring and prediction: A review

Deep learning for personalized health monitoring and prediction: A review

Deep Learning for Personalized Health Monitoring and Prediction: A Review

A novel material identification method for different thermal conductivities materials based on the micro thermoelectric generator

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