Design of Online Vitals Monitor by Integrating Big Data and IoT

Banu, E. Afreen; Rajamani, V.

doi:10.32604/csse.2023.021332

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…21(a) -(j) illustrates the heart rate and body temperatures of ten volunteers which consists of data obtained from the healthcare monitoring system using the reference antenna and the proposed active antennas, without and with a PIN diode. From the figures, it can be observed that the heart rate of the volunteers ranges from 72 to 96 BPM which shows that despite variations in heart rate, all the recorded values remain within the normal range for an adult, which is between 60 and 100 BPM [61]. Moreover, the collection of body temperature data is carried out to assess the patient's body temperature condition.…”

Section: B Performance Evaluation Of the Systemmentioning

confidence: 99%

“…A rise in body temperature correlates with an increase in heart rate, while a decrease in body temperature corresponds to a decrease in heart rate. The temperature ranges from 30.20 to 37.80 °C, indicating a healthy range for an adult which is between 35 and 38 °C [61]. However, it is worth noting that some values might fall below or slightly above this range, which could be due to factors like individual variability, recent activities, or environmental conditions.…”

Section: B Performance Evaluation Of the Systemmentioning

confidence: 99%

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Design and Implementation of Active Antennas for IoT-Based Healthcare Monitoring System

Musa,

Shah,

Majid

et al. 2024

IEEE Access

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This work presents the design and implementation of active antennas as a part of a healthcare monitoring system that is based on the Internet of Things (IoT). The monitoring system comprises a SEN11547 pulse sensor and an LM35 temperature sensor for measuring heart rate in Beats Per Minute (BPM) and body temperature in Degree Celsius (°C). This data is then sent to the ThingSpeak IoT platform, which necessitates the integration with the NodeMCU ESP-32S Wi-Fi module to ensure the availability of data. Two dual-band (2.4 GHz and 5.8 GHz) microstrip patch antennas, one with a PIN diode and one without, are fabricated using Rogers Duroid RO3003™ substrate. Both antennas have dimensions of 41 × 44 mm². In order to achieve a dual-band operation at 2.4 GHz, a slot in the shape of an inverted letter U is introduced, to the existing patch which generates a 5.8 GHz frequency band. By controlling the PIN diode's ON and OFF state, the active antenna can switch between a single band of 5.8 GHz and a dual-band of 2.4 GHz and 5.8 GHz. At both frequencies, the measured radiation patterns exhibit bidirectional and directional characteristics in the E-plane, whereas an omnidirectional pattern can be observed in the H-plane. In terms of nonlinear characteristics of the antenna, the third-order intermodulation distortion products (IMD3) frequencies are generated within an input power range of 0 to 20 dBm from the two-tone nonlinear measurements. Specifically, the IMD3 at 2.4 GHz is measured at -36.18 dBm and -47.19 dBm at 5.8 GHz. Additionally, the measurement showed that the 1-dB gain compression point (P1-dB) was not detected at 2.4 GHz, indicating linear behavior within the RF input power range. However, at 5.8 GHz, the P1-dB was observed at an RF input power level of 13.8 dBm, suggesting linear functionality up to this power level. The experimental data are obtained from ten participants with ages ranging between 18 and 40 years old for 10-minutes duration with a 1-minute step size which implies 10 samples. For comparison and validation, the measurements are compared with the commercially available Laird Connectivity 2.4GHz/5.8GHz dipole antenna. It can be observed that the heart rate ranges from 85 BPM to 92 BPM for the active antenna whereas for the reference antenna, the values range from 84 to 90 BPM, which implies a good agreement. On the other hand, the body temperature ranges from 29 to 37°C for the active antenna and from 30 to 36°C for the reference antenna, which infers a good agreement as well. Therefore, it is shown that the proposed dual-band active antennas in this work can be effectively integrated into the IoT-based healthcare monitoring system.INDEX TERMS IoT platform, healthcare monitoring, patch antenna, PIN diode, vital sign, sensor

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Section: B Performance Evaluation Of the Systemmentioning

confidence: 99%

Section: B Performance Evaluation Of the Systemmentioning

confidence: 99%

Design and Implementation of Active Antennas for IoT-Based Healthcare Monitoring System

Musa,

Shah,

Majid

et al. 2024

IEEE Access

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“…In the works presented in [ 29 , 30 , 31 , 32 , 33 ], the continuous monitoring of vital signs such as breathing and heartbeat temperatures is carried out; these variables play an essential role in predicting diseases. The results highlight the significance of assessing these devices for telemonitoring and clinical decision making, illustrating the potential of wearable technology in real-time health monitoring.…”

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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