Self-Oxygen Regulator System for COVID-19 Patients Based on Body Weight, Respiration Rate, and Blood Saturation

Irawati, Indrarini Dyah; Hadiyoso, Sugondo; Alfaruq, Akhmad; Novianti, Atik; Rizal, Achmad

doi:10.3390/electronics11091380

Cited by 2 publications

(1 citation statement)

References 15 publications

(16 reference statements)

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“…Research in [ 36 ] contemplates the best places to measure anatomical temperature using wearable sensors to measure this variable. These studies conclude that the most accurate areas to measure temperature are the forehead and the ear because they are more closely related to body temperature.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

Mata-Romero,

Simental-Martínez,

Guerrero-Osuna

et al. 2024

Sensors

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The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

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Section: Related Workmentioning

confidence: 99%

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

Mata-Romero,

Simental-Martínez,

Guerrero-Osuna

et al. 2024

Sensors

View full text Add to dashboard Cite

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LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition

Wang,

Satapathy,

Wang

et al. 2023

Mobile Netw Appl

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In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

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Self-Oxygen Regulator System for COVID-19 Patients Based on Body Weight, Respiration Rate, and Blood Saturation

Cited by 2 publications

References 15 publications

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition

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