Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks

Song, Chunhe; Zeng, Peng; Wang, Zhongfeng; Zhang, Hai; Huang, Yu

doi:10.1109/tii.2018.2793905

Cited by 46 publications

(26 citation statements)

References 24 publications

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“…The basic principle of using neural network algorithm to identify bad data is to train the network with training samples. At present, the widely used neural networks mainly include BP neural network, Kohonen neural network and RBF neural network [16]. Although the method of identifying bad data by using neural network algorithm can explain the complex relationship between input vectors and output vectors of neural network, the training process of the network has a great influence on the identification effect of the algorithm [2].…”

Section: Identification Methods Based On Neural Network Algorithmmentioning

confidence: 99%

A Novel Power System Anomaly Data Identification Method Based on Neural Network and Affine Propagation

Shen

Song

et al. 2019

Lecture Notes in Computer Science

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Identification of anomaly data is very important for power system state estimation. In this paper, a method of power system anomaly data identification based on neural network and affine propagation is proposed. In this first step, a 3-layer neural network is trained as a predictor using normal data. In the second step, data to be detected is preprocessed using the trained neural network, and predicted residuals are obtained. In the third step, these predicted residuals are clustered using the affine propagation clustering algorithm, and in the final step, anomaly data is identified based on the clustering results. As the neural network training process is easy to fall into local minimum, which reduces the prediction accuracy of the neural network, in this paper a novel chaotic particle swarm optimization algorithm is proposed to train the neural network. From the experimental results it can be seen that, compared with previous anomaly data identification method using the BP neural network and the gap statistic algorithm or the K-mean clustering algorithm, the proposed method can effectively improve the accuracy of anomaly data identification.

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Section: Identification Methods Based On Neural Network Algorithmmentioning

confidence: 99%

A Novel Power System Anomaly Data Identification Method Based on Neural Network and Affine Propagation

Shen

Song

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…However, the weakness of LM35 and DS18B20 sensors for fever screening COVID-19 is that there must be contact with the sensor. For non-contact human body temperature, the MLX906 sensor is being used by many researchers [17] , [18] , [19] , [20] . It can measure a wide temperature range: -40 to 125°C for sensor temperature and -70 to 380°C for object temperature [21] .…”

Section: Hardware Descriptionmentioning

confidence: 99%

Infrared thermometer on the wall (iThermowall): An open source and 3-D print infrared thermometer for fever screening

Abuzairi¹,

Sumantri²,

Irfan³

et al. 2021

HardwareX

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“…Song et al [ 41 ] proposed a wearable system based on multiple artificial neural networks which monitors the body temperature very precisely with shorter reaction time. Liu et al [ 42 ] proposed a wearable device as a physiological monitoring system that monitors body temperature, ECG, blood glucose, blood pressure, and some other physiological parameters.…”

Section: Supportive Wearable Devices For Covid-19 Patientsmentioning

confidence: 99%

Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19)

et al. 2020

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Wearable technology plays a significant role in our daily life as well as in the healthcare industry. The recent coronavirus pandemic has taken the world’s healthcare systems by surprise. Although trials of possible vaccines are underway, it would take a long time before the vaccines are permitted for public use. Most of the government efforts are currently geared towards preventing the spread of the coronavirus and predicting probable hot zones. The essential and healthcare workers are the most vulnerable towards coronavirus infections due to their required proximity to potential coronavirus patients. Wearable technology can potentially assist in these regards by providing real-time remote monitoring, symptoms prediction, contact tracing, etc. The goal of this paper is to discuss the different existing wearable monitoring devices (respiration rate, heart rate, temperature, and oxygen saturation) and respiratory support systems (ventilators, CPAP devices, and oxygen therapy) which are frequently used to assist the coronavirus affected people. The devices are described based on the services they provide, their working procedures as well as comparative analysis of their merits and demerits with cost. A comparative discussion with probable future trends is also drawn to select the best technology for COVID-19 infected patients. It is envisaged that wearable technology is only capable of providing initial treatment that can reduce the spread of this pandemic.

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Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks

Cited by 46 publications

References 24 publications

A Novel Power System Anomaly Data Identification Method Based on Neural Network and Affine Propagation

A Novel Power System Anomaly Data Identification Method Based on Neural Network and Affine Propagation

Infrared thermometer on the wall (iThermowall): An open source and 3-D print infrared thermometer for fever screening

Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19)

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