Design and Development of IOT based Blood Pressure Measurement system

Hiwale, Ashish; Chaskar, Uttam

doi:10.1109/icccnt54827.2022.9984292

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…In accordance with what was done by Waskito, where the digital blood pressure monitoring system connected via Android via Bluetooth using the MPX5050 pressure sensor had a reading error of 2% [23]. In the module that has been created, an average reading error value of 1.88% is obtained.…”

Section: Resultsmentioning

confidence: 73%

“…In 2021, an Android-based ABPM with a printout was made by Waskito where this digital blood pressure monitoring system using an MPX2100 pressure sensor has obtained systole data has the largest error value of 1.2%, diastole data has the largest error value of 1.2%. Disadvantages of this tool cannot be used during activities [22] [23][24]- [26]. In some of the above studies, not many have utilized the NodeMCU ESP32 for wireless networks which can send the latest information about the patient's blood pressure which will be sent to the doctor via Smartphone.…”

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confidence: 99%

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Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Sari,

C. N.,

Mak’ruf

et al. 2024

j.teknokes

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Ambulatory blood pressure monitoring or ABPM is a non-invasive method to determine the average blood pressure for at least 24 hours, not only when medical checkup. ABPM is often found in cardiac examinations and monitoring of catlab preoperative patients. This study aims to analyze the performance of the ABPM tool that can measure blood pressure continuously with a specified time interval connected to IoT so that can make it easier to get test results. The contribution of this research is a 24-hour monitoring system with delivery via IoT. The experiment was conducted 10 times with Prosim comparison at each point to assess the level of reading accuracy and effectiveness of IoT viewers. At 120/80 mmHg systole accuracy 98.42%, diastole 97.25%. While at 150/100 mmHg systole accuracy is 99.67%, Diastole is 98.1%. At 200/160 mmHg point Systole accuracy 98.35%, Diastole 98.25%. The SPSS test states that the reading data collection is acceptable and has an average commensurate with the test. The difference in viewer time on the TFT and IoT layers is 3.8 seconds and the test data value is 0% loss. The results from making this module, concluding by utilizing the sensor MPX5050 obtained sufficient accuracy, the use of ESP32 as a microcontroller processes the sensor readings which will be converted into systole-diastole values and displays on IoT so that it can slightly help analyze the patient's condition, and this module can read the simulator tool well at pressures of 120/80 mmHg, 150/100 mmHg, and 200/160 mmHg. The device showed good accuracy and reliability in measuring blood pressure at different levels compared to a vital signs simulator. The device can be used for 24-hour monitoring of hypertension patients and provide useful information for diagnosis and treatment.

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Section: Resultsmentioning

confidence: 73%

mentioning

confidence: 99%

Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Sari,

C. N.,

Mak’ruf

et al. 2024

j.teknokes

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“…The process is then subjected to detailed analysis to understand the model's properties and performance. To establish the experimental pressure sensor setup [72]- [74], a differential pressure sensor (MPX5100DP) was connected to both the controller board and pump [75], [76], [77], as depicted in Fig. 3 (research diagram) and Fig.…”

Section: Cupping Suction Experimentsmentioning

confidence: 99%

Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model

Suresh,

Ghazali,

Ahmad

2023

Journal of Robotics and Control

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The use of cupping therapy for various health benefits has increased in popularity recently. Potential advantages of cupping therapy include pain reduction, increased circulation, relaxation, and skin health. The increased blood flow makes it easier to supply nutrients and oxygen to the tissues, promoting healing. Nevertheless, the effectiveness of this technique greatly depends on the negative pressure's ability to create the desired suction effect on the skin. This research paper suggests a method to detect the cupping suction model by employing the Hammerstein model and utilizing the Safe Experimentation Dynamics (SED) algorithm. The problem is that the cupping suction system experiences pressure leaks and is difficult to control. Although, stabilizing the suction pressure and developing an effective controller requires an accurate model. The research contribution lies in utilizing the SED algorithm to tune the parameters of the Hammerstein model specifically for the cupping suction system and figure out the real system with a continuous-time transfer function. The experimental data collected for cupping therapy exhibited nonlinearity attributed to the complex dynamics of the system, presenting challenges in developing a Hammerstein model. This work used a nonlinear model to study the cupping suction system. Input and output data were collected from the differential pressure sensor for 20 minutes, sampling every 0.1 seconds. The single-agent method SED has limited exploration capabilities for finding optimum value but excels in exploitation. To address this limitation, incorporating initial values leads to improved performance and a better match with the real experimental observations. Experimentation was conducted to find the best model parameters for the desired suction pressure. The therapy can be administered with greater precision and efficacy by accurately identifying the suction pressure. Overall, this research represents a promising development in cupping therapy. In particular, it has been demonstrated that the proposed nonlinear Hammerstein models improve accuracy by 84.34% through the tuning SED algorithm.

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Design and Development of IOT based Blood Pressure Measurement system

Cited by 2 publications

References 12 publications

Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model

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