Internet of Things and Personalized Healthcare Systems

Gurumoorthy, Sasikumar; Krishna, P. Venkata; Obaidat, Mohammad S.

doi:10.1007/978-981-13-0866-6

Cited by 8 publications

(8 citation statements)

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“…e methodology used with IoT improves the performance and throughput of the system. e percent error of the results is calculated by using the formula shown in (6). Here, the accepted value is required accuracy, and experimental accuracy is achieved accuracy of the experiments.…”

Section: Resultsmentioning

confidence: 99%

“…In IoT healthcare system, there exit wireless systems in which different applications and sensors are attached to patients, information is obtained, and this information is forwarded to a doctor or specialist through an expert system [5]. Medical devices for Internet of things (MD-IoT) are remotely accessed, where devices are connected to the Internet and sensors, actuators, and other communication devices can monitor patient health [6]. rough these devices, the patient information and data are transmitted by the expert system via gateway onto a secured cloud based platform where the information is stored and can be analyzed.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks

Hameed

Bajwa

Ramzan

et al. 2020

Scientific Programming

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Healthcare facilities in modern age are key challenge especially in developing countries where remote areas face lack of high-quality hospitals and medical experts. As artificial intelligence has revolutionized various fields of life, health has also benefited from it. The existing architecture of store-and-forward method of conventional telemedicine is facing some problems, some of which are the need for a local health center with dedicated staff, need for medical equipment to prepare patient reports, time constraint of 24–48 hours in receiving diagnosis and medication details from a medical expert in a main hospital, cost of local health centers, and need for Wi-Fi connection. In this paper, we introduce a novel and intelligent healthcare system that is based on modern technologies like Internet of things (IoT) and machine learning. This system is intelligent enough to sense and process a patient’s data through a medical decision support system. This system is low-cost solution for the people of remote areas; they can use it to find out whether they are suffering from a serious health issue and cure it accordingly by contacting near hospitals. The results of the experiments also show that the proposed system is efficient and intelligent enough to provide health facilities. The results presented in this paper are the proof of the concept.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks

Hameed

Bajwa

Ramzan

et al. 2020

Scientific Programming

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“…Sensitivity, also referred to as true positive rate ( TPR ), is used to evaluate the number of annotated nerves detected. Sensitivity [ 38 ] is given by, where TP m indicates the number of true positives detected from the manual annotations and FN indicates the number of false negatives.…”

Section: Methodsmentioning

confidence: 99%

“…Precision, also referred to as positive predictive value ( PPV ), is used to evaluate the ability of an approach to detect nerves. Precision [ 38 ] is given by, where TP indicates the total number of true positives, TP a indicates the number of true positives in the additional detections, i.e. nerves missed by the experts in the manual annotations, and FP indicates the number of false positives.…”

Section: Methodsmentioning

confidence: 99%

Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning

et al. 2022

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Accurate quantification of nerves in cancer specimens is important to understand cancer behaviour. Typically, nerves are manually detected and counted in digitised images of thin tissue sections from excised tumours using immunohistochemistry. However the images are of a large size with nerves having substantial variation in morphology that renders accurate and objective quantification difficult using existing manual and automated counting techniques. Manual counting is precise, but time-consuming, susceptible to inconsistency and has a high rate of false negatives. Existing automated techniques using digitised tissue sections and colour filters are sensitive, however, have a high rate of false positives. In this paper we develop a new automated nerve detection approach, based on a deep learning model with an augmented classification structure. This approach involves pre-processing to extract the image patches for the deep learning model, followed by pixel-level nerve detection utilising the proposed deep learning model. Outcomes assessed were a) sensitivity of the model in detecting manually identified nerves (expert annotations), and b) the precision of additional model-detected nerves. The proposed deep learning model based approach results in a sensitivity of 89% and a precision of 75%. The code and pre-trained model are publicly available at https://github.com/IA92/Automated_Nerves_Quantification.

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“…Rectangular, triangular, and trapezoidal cantilevers have been analyzed with optical fiber, which contributed more toward minimum pressure detectable limit of triangular profile. 3 Analysis was carried out for geometrical modification of different MEMS microcantilevers for sensitivity enhancement and reestablished the sensitivity of cantilever with modified dimensions. 4 Si-based cantilever with photonic crystal resonator has been analyzed for air and water medium.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of optomechanical‐based microcantilever sensor with various geometrical shapes

Upadhyaya

Srivastava

Sharan³

2020

Micro & Optical Tech Letters

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This paper presents a performance analysis of different microcantilever shapes integrated with the optical MEMS system in different fluid mediums. Microcantilevers such as rectangular, trapezoidal, and triangle profile are coupled with optical sensing layers. Here, the concept of integration of optical sensing layer with different shapes of microcantilever is novel. The cantilever is designed and developed in CAD tools. Numerical analysis of different shapes of microcantilever was carried out with the help of Ansys Workbench. Optimal design of the regular microcantilever is considered during the analysis. The pressure is applied to the free end of the cantilever in the range of 100 to 250 kPa. The complete photonic sensing layer is analyzed with the help of an finite difference time domain (FDTD) tool called MIT Electromagnetic Equation Propagation (MEEP). The transmission spectrum is obtained for each microcantilever model. The pressure‐induced refractive index is calculated for the equivalent maximum stress generated. The result shows that a remarkable Q factor was obtained for rectangular, trapezoidal, and triangular profile microcantilevers with an optical system. Triangular and rectangular profiles have shown remarkable contribution over quality factor for air mediums such as 10 120, 1300, respectively. High pressure sensitivity of 1.92 nm/kPa was obtained for rectangular microcantilever in air. Least sensitivity of 0.16 nm/kPa was obtained for triangle microcantilever in the water medium. The proposed work successfully distinguishes various shapes of microcantilever in terms of sensitivity and Q factor. It is having tremendous application in sensing biofluids and in device miniaturization.

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Internet of Things and Personalized Healthcare Systems

Cited by 8 publications

References 0 publications

An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks

An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks

Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning

Performance analysis of optomechanical‐based microcantilever sensor with various geometrical shapes

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