Real-time monitoring of the respiration rate in everyday life enables the early detection of various diseases and disorders that can cause a life-threatening incident. In this article, a passive sensor for real-time monitoring of the human respiration rate is proposed. The sensor is deployed on a chest strap to accurately acquire the respiration rate data and display it on a smartphone through Bluetooth communication. The sensor consists of a stretchable fabric substrate impregnated with silver Nano-particles through drop-casting at ambient conditions. The proposed passive sensor provides a change in electrical resistance against applied strain due to shrinkage of the conducting threads and resets to the initial resistance value when released. The resistance modulation phenomenon is exploited for the respiration sensing application. The demonstrated sensor is $4\times 20$ mm2, however, the dimensions can be changed according to the application and requirements. At rest position, resistance is $180\Omega $ and at 16% stretching, the resistance goes down to $70\Omega $ . The proposed device is characterized by mechanical, electrical, and surface morphology. The proposed sensor can be a good candidate for the respiration sensing application in wearable electronics.

Developing Conductive Fabric Threads for Human Respiratory Rate Monitoring

Wireless network technology-based internet of things (IoT) has increased significantly and exciting to study, especially vital sign monitoring (body temperature, heart rate, and blood pressure). Vital sign monitoring is crucial to carry out to strengthen medical diagnoses and the continuity of patient health. Vital sign monitoring conducted by medical personnel to diagnose the patient's health condition is still manual. Medical staff must visit patients in each room, and the equipment used is still cable-based. Vital sign examination like this is certainly not practical because it requires a long time in the process of diagnosis. The proposed vital sign monitoring system design aims to assist medical personnel in diagnosing the patient's illness. Vital sign monitoring system uses HRM-2511E sensor for heart detection, DS18b20 sensor for body temperature detection, and MPX5050DP sensor for blood pressure detection. Vital sign data processing uses a raspberry pi as a data delivery media-based internet of things (IoT). Based on the results of the vital sign data retrieval shows that the tool designed functioning correctly. The accuracy of the proposed device for body temperature is 99.51%, heart rate is 97.90%, and blood pressure is 97.69%.

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Internet of things–based vital sign monitoring system

Vital Sign Monitor is a tool used to diagnose a patient who needs intensive care to know the condition of the patient. Parameters used in monitoring the patient's condition include body temperature and respiration. The contribution of this research designed a vital sign monitoring tool with IoT-based notifications so that remote monitoring can be done by utilizing web Thinger.io, LCD, RGB LEDs as a display of the results of the study and notify telegrams if it becomes abnormal to the patient's condition. Therefore, in order to produce accurate data in the process of data retrieval, a relaxed position of the patient is required and the stability of the wi-fi network so that monitoring is not hampered. The study used the DS18B20 digital temperature sensor placed on the axilla and the piezoelectric sensor placed on the abdomen of the patient. The results of the study were obtained by taking data on patients. The resulting temperature value will be compared to the thermometer, which produces the highest error value of 0.56%, which is still possible because the tolerance limit is 1oC. and for the collection of respiration values that have been compared to the patient monitor obtained the highest error value of 6.2%, which is still feasible because the tolerance limit is 10%. In this study, there is often a crash library between the temperature sensor and other sensors, so for further research, recommend to replacing the temperature sensor

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Vital Sign Monitor Device Equipped with a Telegram Notifications Based on Internet of Thing Platform

The lungs are the main organs in the respiratory system that have a function as a place for exchange of oxygen and carbon dioxide. Due to the importance of lung function, indications of lung disorders must be detected and diagnosed early. Research on the classification of lung conditions generally uses chest x-ray image data. Where a time-consuming procedure is needed to obtain the data. In this research, an embedded system to diagnose lung conditions was designed. The system was made to be easy to use independently and provides real-time examination results. This system uses parameters of body temperature, oxygen saturation, fingernail color and lung volume in classifying lung conditions. There are three conditions that can be classified by the system, that is healthy lungs, pneumonia and tuberculosis. The k-nearest neighbor method was used in the classification process in the designed system. The dataset used was 51 data obtained from the hospital. Each data already has a label in the form of lung condition based on the doctor’s diagnosis. The proposed system has an accuracy of 88.24% in classifying lung conditions.

https://beei.org/index.php/EEI/article/download/3033/2169

Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system

Background: As the mobile environment has developed recently, there have been studies on continuous respiration monitoring. However, it is not easy for general users to access the sensors typically used to measure respiration. There is also random noise caused by various environmental variables when respiration is measured using noncontact methods in a mobile environment.
Objective: In this study, we aimed to estimate the respiration rate using an accelerometer sensor in a smartphone.
Methods: First, data were acquired from an accelerometer sensor by a smartphone, which can easily be accessed by the general public. Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns.
Results: From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95% limits of agreement.
Conclusions: There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment.

/pdf/respiration-rate-estimation-based-on-independent-component-33hzinesom.pdf

Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study.

Vital signs are a measurement of the most basic functions of the body which to find out the clinical signs. It is useful for diagnosing disease and determining the appropriate medical treatment plan. The respiratory rate is one of the most critical parameters because the respiratory rate can provide valuable information related to the condition of the heart, nerves, and lungs pulmonary. Patients who are in critical condition are generally monitored by measuring their respiratory rate. Heart rate is an important parameter in the human cardiovascular system. When the heart rate is irregular, it can be a critical sign. The objective of this study is to design the respiration rate and heart rate monitor. So the author designed an apparatus to detect the respiration by using a flex sensor and the heart rate monitor using the SEN11574 module. The results of these sensors are then sent to the Internet via ESP32 microcontroller. The parameters were displayed to the Web using the Internet of Things (IoT) based on ThingSpeak application. After the comparison between design and standard, the results showed that the error is 2.3% and 0.57% for respiration rate and heart rate, respectively.

The Measuring of Vital Signs Using Internet Of Things Technology (Heart Rate And Respiration)

People with disabilities find it difficult to operate wheelchairs especially those with disabilities who do not have hands and feet or are disabled. The aim of this research is to improve safety control for electric wheelchairs by detecting obstacles and detecting areas that minimize collisions against obstacles. The contribution of this research is the use of the HC-SR04 ultrasound sensors to avoid a wheelchair colliding with an obstacle using the Pulse Width Modulation (PWM) method which is set based on the distance of the obstacle and the detection of free areas and corridor areas using the PWM method which is adjusted based on following the wall. The HC-SR04 ultrasound sensor is used as input and is processed on the Arduino nanomicrocontroller series to produce a value of PWM. Measuring the HC-SR04 ultrasound sensor against obstacles obtained the largest deviation of 0.72 and average 148,6 cm at a distance of 150 cm and the smallest deviation at a distance of less than 50 cm and the wheelchair can move at slow, medium and fast speeds with the measurement results on Duty Cycles of 71%, 82% and 94%. The results showed that the final distance of the wheelchair against obstacles was less than 50 cm and the wheelchair moved at a distance of 34 - 53 cm following the wall. The results of this study can control motor speed based on obstacles that can be implemented in electric wheelchairs to improve safety and ease of operation for people with disabilities.

Obstacles and Areas Detection Based on Pulse Width Modulation Method for Electric Wheelchair Safety Using Ultrasound Sensors

The paralysis that occurs in the human limbs can be caused by strokes, injuries, age problems, and ligament damage. The purpose of this study is to design a hand exoskeleton as rehabilitation in patients who have had a stroke in hand. The contribution of this research is to design a hand exoskeleton with a control system to control mechanical movements using voice command so that it can be used by patients who have a stroke. To be used by patients who have a stroke, the researcher designed a control system using voice pattern recognition so that patients who have weak myoelectric signals can control the mechanics easily. This device uses the voice recognition module V3 as a voice command to control open and control close mechanical movements. This device is capable of recording and running commands directly by using the push button, which consists of a start, save, reset, open command record, and close command record. In the open command obtained an accuracy value of 97%, the close command obtained an accuracy value of 93%. The results showed that the voice commands given had an average accuracy rate of 95%. The results of this study can be implemented as a rehabilitation device for people who have had a stroke to try to mimic human hand movements.

Design of Hand Exoskeleton for Paralysis with Voice Pattern Recognition Control

Infusion pumps and syringe pumps are devices used to administer liquid medicines to patients. The frequency of using the infusion pump and syringe pump in the long term will affect the accuracy of the tool. Accuracy is very important in dosing to patients in critical conditions who require intensive care to prevent fluid balance in the body. Therefore, periodic calibration of medical devices is required at least once a year. Calibration according to Permenkes No. 54 of 2015 is a calibration activity to determine the correctness of a tool. The purpose of this research is to make an Infusion Device Analyzer (IDA) with a TFT LCD displaying a graph of flowrate parameters. The method used is to analyze the flowrate value using an infrared photodiode sensor and can see the stability of the flowrate graph on a 7-inch TFT LCD from the use of 2 brands of syringes and an infusion set. The results obtained can be stored on the SD Card. The measurement results show that the error in the performance of the syringe and infusion pump read by the module on Channel 1 with the Terumo syringe is 0.15 (10 ml/h), 0.1 (50ml/h) and 0.03 (100ml/h). . On Channel 2 it is 0.02(10ml/hour), 0.03 (50ml/hour) and 0.04(100ml/hour). When using the B-Braun Channel 1 syringe, 0.25 (10ml/h), 009(50ml/h) and 0.08(100ml/h) are used. And on Channel 2 it is 0.62 (10ml/h), 0.15 (50ml/h), and 7.3 (100ml/h). When using the Terumo Channel 1 brand infusion set at 0.05 (10ml/h), 0.3(50ml/h), and 0.04(100ml/h). On Channel 2 it is 0.14(10ml/hour), 0.02 (50ml/hour) and 0.18 (100 ml/hour). When using the B-Braun Channel 1 Infusion Set, it is 0.07(10ml/h), 0.02(50 ml/h), and 0.03 (100ml/h). Then on Channel 2 0.07 (10ml/hour), 0.02(50 ml/hour), and 0.1(100ml/hour). The conclusion of this study is that the use of 2 different infusion sets greatly affects the readings, other than that other factors can also affect the readings including the position of the hose and the placement of sensors on each channel. From the manufacture of this tool, it is expected that users can be more efficient in using a 2-channel Infusion Device analyzer which can be run at the same time.

The Performance Analysis of the Infrared Photodiode Sensor to Infusion Set on Infusion Device Analyzer Machine

Temperature calibration is an activity that formed the relationship between values indicated by a measuring instrument or measuring system with the temperature scale value measured under certain conditions. A clinical thermometer is a device that doctors use to measure the temperature of a patient's body. In the field of calibration, the suitability of medical device readings with the allowed standard values (tolerance values) is very necessary so that the readings are kept accurate. The purpose of this research is to make ATmega32p-based digital wet media thermometer calibrator to monitor the performance of the thermometer with a temperature setting of 35°C – 40°C in order to maintain its accuracy. The design of this calibrator consists of a wet heater circuit using a DS18B20 temperature sensor processed by ATmega328p minimum system, which then displayed on a 2x16 LCD. This device was designed to make it easier to calibrate the thermometer so that its accuracy is maintained. Thermometer calibration was done by comparing the module with a standard calibrated measuring device. The measurement of the instrument against the setting temperature has the smallest error of 0% and the largest value of 0.25%. Meanwhile, the measurement of the instrument against a standard thermometer (which has been calibrated) has the smallest error value of 0% and the largest value of -1.17%.

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Papers

The Measuring of Vital Signs Using Internet Of Things Technology (Heart Rate And Respiration)

Obstacles and Areas Detection Based on Pulse Width Modulation Method for Electric Wheelchair Safety Using Ultrasound Sensors

Design of Hand Exoskeleton for Paralysis with Voice Pattern Recognition Control

The Performance Analysis of the Infrared Photodiode Sensor to Infusion Set on Infusion Device Analyzer Machine

Design of ATMEGA328p-Based Digital Wet Media Thermometer Calibrator