An Alternative Body Temperature Measurement Solution: Combination of a Highly Accurate Monitoring System and a Visualized Public Health Cloud Platform

Jiang, Joe‐Air; Wang, Jen-Cheng; Hsieh, Chao-Liang; Tseng, King Jet; Ye, Zheng-Wei; Su, Lin-Kuei; Sun, Chih‐Hong; Wen, Tzai‐Hung; Juang, Jehn‐Yih

doi:10.1109/jiot.2020.3034024

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…At present, the research on mobile medical service systems based on cloud platforms is still relatively few and is in its infancy. Through the cloud platform, it can complete the storage, calculation, and screening of relevant data in the medical system, provide the management of patients' daily health records, and complete the medical care of patients in a more real-time and effective manner [ 22 ]. After users complete the measurement of physical indicators, they store the health indicator data on the cloud platform and read it selectively at any time through the mobile terminal device.…”

Section: Related Workmentioning

confidence: 99%

Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection

2022

Computational Intelligence and Neuroscience

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The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor–patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system.

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Section: Related Workmentioning

confidence: 99%

Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection

2022

Computational Intelligence and Neuroscience

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“…In their study, Jiang et al [22] suggested the need for isolating suspected cases in order to control epidemics. To achieve this, they proposed an IoT-based architecture that includes a body temperature monitoring device with thermography.…”

Section: Inertial Sensor Yes Yesmentioning

confidence: 99%

An IoT-Based Framework for Personalized Health Assessment and Recommendations Using Machine Learning

Jagatheesaperumal¹,

Sujatha²,

Suresh³

et al. 2023

Mathematics

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To promote a healthy lifestyle, it is essential for individuals to maintain a well-balanced diet and engage in customized workouts tailored to their specific body conditions and health concerns. In this study, we present a framework that assesses an individual’s existing health conditions, enabling people to evaluate their well-being conveniently without the need for a doctor’s consultation. The framework includes a kit that measures various health indicators, such as body temperature, pulse rate, blood oxygen level, and body mass index (BMI), requiring minimal effort from nurses. To analyze the health parameters, we collected data from a diverse group of individuals aged 17–24, including both men and women. The dataset consists of pulse rate (BPM), blood oxygen level (SpO2), BMI, and body temperature, obtained through an integrated Internet of Things (IoT) unit. Prior to analysis, the data was augmented and balanced using machine learning algorithms. Our framework employs a two-stage classifier system to recommend a balanced diet and exercise based on the analyzed data. In this work, machine learning models are utilized to analyze specifically designed datasets for adult healthcare frameworks. Various techniques, including Random Forest, CatBoost classifier, Logistic Regression, and MLP classifier, are employed for this analysis. The algorithm demonstrates its highest accuracy when the training and testing datasets are divided in a 70:30 ratio, resulting in an average accuracy rate of approximately 99% for the mentioned algorithms. Through experimental analysis, we discovered that the CatBoost algorithm outperforms other approaches in terms of achieving maximum prediction accuracy. Additionally, we have developed an interactive web platform that facilitates easy interaction with the implemented framework, enhancing the user experience and accessibility.

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“…The front-end network and back-end network each have two switches, and the two switches at the same end form the active standby relationship. The HSRP protocol is used to ensure the active/standby working relationship in a normal environment and complete the active/standby switching when necessary [17][18].…”

Section: Network Monitoring Management Systemmentioning

confidence: 99%

Cloud Monitoring Data Analysis and Simulation Based on Machine Learning

2022

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With the rapid development of Internet technology, cloud computing has gradually been widely used in people's lives. However, due to the huge and complex data volume, limited storage space and maintenance difficulties, there are still some problems in cloud monitoring data analysis. This paper intends to use the combination of machine learning to study the analysis and experimental simulation of cloud monitoring data, in order to improve the data processing ability of the monitoring system. This paper mainly uses the experimental analysis method and information entropy to study the cloud monitoring system and its data processing and analysis capabilities. The experimental data show that the error of computer tools is less than 6%, which shows that the method is feasible and worthy of application.

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An Alternative Body Temperature Measurement Solution: Combination of a Highly Accurate Monitoring System and a Visualized Public Health Cloud Platform

Cited by 7 publications

References 34 publications

Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection

Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection

An IoT-Based Framework for Personalized Health Assessment and Recommendations Using Machine Learning

Cloud Monitoring Data Analysis and Simulation Based on Machine Learning

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