Identification of different respiratory rate by a piezo polymer based nasal sensor

Manjunatha, Roopa G.; Ranjith, N.; Meghashree, Y. V.; Rajanna, K.; Mahapatra, D. Roy

doi:10.1109/icsens.2013.6688479

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…Then, the feature map extracted from the last convolution layer is connected to the fully-connected layer by randomly selecting only the specified proportion of features through dropout after being serialized. Dropout is a technique to select only a part of the entire feature at random, which improves performance over all features [33,34].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The 1D CNN method enables various pattern recognition of heart beat signals of ECG sensor. This 1D CNN method shows much higher recognition rate results than SVM or MLP which are traditional machine learning algorithms [33,34]. The structure of the neural network shown in the existing 1D CNN-based signal pattern recognition research uses two or three convolutional layers and fully-connected layers, and shows the classification results in the output layer.…”

Section: Related Researchmentioning

confidence: 99%

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A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Kim

Geem

Han

2019

Sensors

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Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal data using Ultra-wideband Radar and proposed 1D CNN (1-Dimension Convolutional Neural Network) model that can classify and recognize five respiration patterns (Eupnea, Bradypnea, Tachypnea, Apnea, and Motion) from the signal data. Also, in the proposed model, we find the optimum parameter range through the recognition rate experiment on the combination of parameters (layer depth, size of kernel, and number of kernels). The average recognition rate of five breathing patterns experimented by applying the proposed method was 93.9%, which is about 3%~13% higher than that of conventional methods (LDA, SVM, and MLP).

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Kim

Geem

Han

2019

Sensors

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“…At the average breathing rate reported in healthy adults at rest is usually administered as 12 breaths per minute (12⋅60 Hz) but estimates vary between sources for example, 12-20 breaths per minute, 10-14, between 16-18, etc [9]. RR that leads to bradypnea (RR <12) or even apnea (respiratory cessation for an indence period) [10]. There are two kinds of breathing mechanisms, namely chest breathing and abdominal breathing [11].…”

Section: Introductionmentioning

confidence: 99%

Low Cost Health Monitoring Sytem Based on Internet Of Things Using Email Notification

Wisana

Utomo

Amrinsani

et al. 2021

Indones.J.electronic.electromed.med.inf

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Monitoring activities are needed if there are symptoms of a disease that require quick action so that the patient's condition does not get worse, for that we need a system that can notify doctors so they can take action. The patient monitoring system in hospitals is generally still carried out conventionally, among others, nurses or doctors come to the patient's room to check on the progress of the patient's condition, this will be a problem, if the number of medical personnel and facilities is insufficient to monitor. Patients who need special attention for patient care, such as monitoring the patient's breathing rate. The use of the internet of things (IOT), as a device that can work without the help of people, can perform tasks and provide easier and real time data, so that they can access output directly. The purpose of this research is to design an inexpensive health monitoring tool based on the Internet of Things (Respiration Parameters) using a piezoelectric sensor and an ESP32 Wi-Fi module. From the results of the module design taken from 10 respondents, obtained that the average measurement high accuracy (17.76 + 0.61) and the average level of stability of the design has a magnitude of 0.4 so that it can be concluded that using a piezoelectric sensor in this series can obtain good accuracy. This the design can be used to monitor a person's respiration in real-time

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“…This sensor has advantages such as a small sensor shape, and easy use. This sensor has also been applied to detect and even monitor respiration values [15] [16]. In 2018 the Measurement of Respiratory Rate Using Piezoelectric Sensor was made, by Shankar N, et al detects breathing placed on the patient's chest.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Based Respiratory Signal monitoring and Apnea detection Via Bluetooth Comunication

Ramadhani¹,

Wisana²,

Nugraha³

2021

j.teknokes

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Patients with sleep apnea (sleep apnea) are increasing, almost more than 80% of people with this disorder are undiagnosed. Symptoms of sleep apnea are stopping breathing for more than 10 seconds. The purpose of this study was to design an apnea monitor device in order to detect symptoms of sleep apnea. The contribution in this study is a monitoring system or remote monitoring so that other people can monitor the patient's condition even though they are not accompanying him. In order to facilitate the process of monitoring and diagnosing patients, a Apnea Monitor Based on Bluetooth with Signal Display in Android with a delivery system via a bluetooth network that displays respiratory signals on Android so that patients can be treated quickly when breathing stops (apnea) . The design of this device uses a piezoelectric sensor to detect breathing which is placed on the patient's abdomen. The sensor output in the form of voltage is then conditioned on the PSA circuit. Using the ESP32 microcontroller as a signal processing which is formed by the PSA circuit and processed into a signal and respiration value. The respiration signal and value are then sent to the android device using the Bluetooth network. When a respiratory arrest is detected for more than 10 seconds, the device will turn on the indicator and buzzeer on the device and also send a warning to the Android or Roboremo application in the form of a notification "Apnea!" and a beep sound as a reminder when there is apnea in the patient so that the user can immediately take action on the patient. The test in this study there are 5 respondents who have been tested on this module by comparing the respiration rate per minute with the Patient Monitor, and the test results in this study obtained the measurement and calculation results, the lowest error value was 1.58% and the highest error value was 2.9%, the module can also transmit data well and without data loss with a distance of 10 meters in the room and 5 meters in different rooms. This module can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders. the module can also transmit data well and without data loss with a distance of 10 meters in the room and 5 meters in different rooms. This module can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders. the module can also transmit data well and without data loss with a distance of 10 meters in the room and 5 meters in different rooms. This module can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders.

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Identification of different respiratory rate by a piezo polymer based nasal sensor

Cited by 11 publications

References 12 publications

A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Low Cost Health Monitoring Sytem Based on Internet Of Things Using Email Notification

Smartphone Based Respiratory Signal monitoring and Apnea detection Via Bluetooth Comunication

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