Heartbeat Pulse Research Articles

Design and Production of Heartbeat Counter for Medical Application

Heartbeat pulse measurement is an important medical practice used to determine the health status of an individual. This research was concerned on the design and production of heartbeat pulse counter for medical applications with specific features. The device senses and monitors the heartbeat rate whenever a fingertip is placed on the pulse sensor, the pulse sensor is a plug and play device which is detachable from the Arduino Uno board, the Arduino Uno receives and calculates this data and the result displayed on the 16X2 LCD display based on the written codes. It has the features for initial display, showing system on and when a finger is placed, it readily displays heartbeat rate detected and presents the pulse rate of the individual. Its design and production was achieved and used to test the heartbeat of many persons. Further work is required to incorporate GSM module with the capability of sending text message and cloud hosting of the medical data of an individual.

A study on requirement optimization of intelligent health care information system for rural elderly based on improved FKM-TOPSIS method

Against the backdrop of China’s young and middle-aged population moving to the cities to work, older adults in rural areas of China are facing even more severe old-age and healthcare dilemmas. Under such circumstances, how to improve the functions of age-appropriate smart wearable products to meet the needs of older adults in rural areas for old age and health care has become a focal issue in Chinese society. In this paper, we change the traditional Fuzzy Kano Model (FKM) research method, quantify the research results, and use the superiority and inferiority coefficient formula to scientifically classify user needs after conducting research on different functions of smart wearable devices and obtaining different user needs: M denotes must-be demand; O denotes performance demand; A denotes attractive demand; and I denote indifferent demand. A theoretical multidimensional analysis of the generated data was performed to determine the requirements priority of the rural elderly. Then, using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach, compute the relative closeness of user satisfaction, i.e., the priority of functional needs. Finally, testing the product prototypes of “PC-based and mobile-based” information systems of smart wearable devices and identifying the most critical demand concentrated on emergency assistance for rural older adults. Such as 120 Call, Call Emergency Contacts, Positioning System, Blood Pressure Testing, Heartbeat Pulse Monitoring, etc. These functions can effectively reduce the health risks of older adults in critical emergencies, confirm the scientific character of user demand prioritization, establish the viability of the study, and provide new ideas for intelligent product-related research to continue.

Modeling peripheral arterial and venous pressure signals with integral pulse frequency modulation

This paper proposes a novel mathematical modeling framework for peripheral arterial and peripheral venous blood pressure signals from porcine experiments. Peripheral blood pressure signals can be acquired using regular catheters during standard patient treatment. The minimally invasive nature and ubiquitous availability of catheters render it an ideal candidate for various applications. However, there is no analytical model for peripheral blood pressure signals in the literature. We address this issue by proposing a model for these signals under the integral pulse frequency modulation (IPFM) framework. The model incorporates the impacts of physiological phenomena, such as the heartbeat pulse shape variation, heart rate variability, respiratory rate, etc. The model parameters are obtained by applying the IPFM model to experimental data collected from four pigs under different anesthetic dosages. The proposed model can fit the experimental data with Pearson correlation coefficients greater than 0.99 and 0.90 for arterial and venous blood pressure signals, respectively. The performance of model-synthesized data on the classification of two different anesthesia is comparable with experimental data. Parameters like pulse shape and duration can also work as distinguishable features under different anesthesia. We also proposed a way to distinguish respiratory-induced heart rate variability from other causes. Increasing doses of vasodilating anesthesia is similar to going from dehydration to hydration. Thus the results obtained in this study can be extended in distinguishing hydrated and dehydrated subjects. This model can be extended to similar biomedical signals like photoplethysmography, cerebral blood flow velocity, and Doppler waveforms.

Calculation of Heartbeat Rate and SpO2 Parameters Using a Smartphone Camera: Analysis and Testing.

Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky-Golay filter. Two approaches-gradient and local maximum methods-were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal's frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.

PEO/cellulose composite paper based triboelectric nanogenerator and its application in human-health detection

Recently, cellulose paper based triboelectric nanogenerators (CPTENGs) has gained widely attention due to the development of wearable, green and miniaturized electronic products. Modification of cellulose fibers or paper is a feasible method to improve the output performance of CPTENGs, however, the simple and effective routes to improve the triboelectric property of cellulose paper still remain a challenge. Herein, we report a simple method to prepare PEO/cellulose composite paper (PEO/CCP) via mixing polyethylene oxide (PEO) with cationic cellulose fibers. Benefiting from amino groups and PEO, the composite paper exhibits higher triboelectric positive property and triboelectric charge density, thereby endowing PEO/CCP based TENG with outstanding output performance. The voltage, current and power density peak values of PEO/CCP based TENG exhibited linear relationship with amino groups content; in this instance, the performance of the TENGs can be readily adjusted by the amino groups. The voltage, current and power density of PEO/CCP based TENG can be up to 222.1 V, 4.3 μA, and 217.3 mW•m−2, respectively. Moreover, a human-health detection device based on this TENG can monitor the physiological signals such as eye muscles, respiration, heart beat and wrist pulse, promising potentials for applications in human health-care.

Three-dimensional AgNps@Mxene@PEDOT:PSS composite hybrid foam as a piezoresistive pressure sensor with ultra-broad working range.

The online version contains supplementary material available at 10.1007/s10853-022-08012-y.

Smart Infant Cradle in Clinical Perspective using H-IoT with Android Application

In scheduled time monitoring the infants in a hospital or at home where the monitoring with human resources, especially nurses is exceptionally difficult. So, these infants can be monitored 24*7 using a device. Infant health monitoring became a fast-growing technology in the medical field. Hence, we are implementing an H-IoT where health monitoring sensors are used. A smart cradle, put into practice which utilizes the basic data from the infant-like heartbeat pulses, temperature, bed wetness, cry pattern, and movement. These parameters-related sensors are associated with the Arduino UNO to keep track of these, the various parameters are displayed in the dashboard. If the system notifies any unforeseen changes in the infant, then it will spontaneously caution the infant status in call notification using the Global System of Mobile Communication module. In this manner, IoT health monitoring is fetched for the monitoring of infants in a real-time environment.

Smart Helmet GPS-Based for Heartbeat Drowsiness Detection and Location Tracking

In Indonesia, motorcycle traffic accidents have increased rapidly. Traffic accidents result in high mortality. One of the causes is influenced by human psychological factors or human error. However, to improve the behavior of the riders and due reducing traffic accidents, the purpose of this research is developed a Smart Helmet that can detect drowsiness by measuring the heartbeats psychological riders. Besides that, this system equipped with an SOS button. Its function is to detect and help the riders if there were any emergency incidents on the roads. This proposed system designed using a heartbeat pulse sensor, GPS module, GSM module, Arduino Nano, push-button, and buzzer. Smart Helmet examined in several scenarios to test the performance of the drowsiness and the SOS button. The resulting test on 10 respondents defined that the drowsiness can be detected and give a buzzer alert when the heartbeat is below 60 bpm. The information can be seen without delay. The incident location can be tracked down by utilizing the google maps application. The shift position as the error distance of the GPS incident location only happens in the range of 21.96-42.63 meters. The conclusion is the helmet can detect drowsiness based on heartrate and give an alarm. The SOS button is functionally properly as long as the helmet is used in the outdoor area.

Normalization of photoplethysmography using deep neural networks for individual and group comparison

Photoplethysmography (PPG) is easy to measure and provides important parameters related to heart rate and arrhythmia. However, automated PPG methods have not been developed because of their susceptibility to motion artifacts and differences in waveform characteristics among individuals. With increasing use of telemedicine, there is growing interest in application of deep neural network (DNN) technology for efficient analysis of vast amounts of PPG data. This study is about an algorithm for measuring a patient's PPG and comparing it with their own data stored previously and with the average data of several groups. Six deep neural networks were used to normalize the PPG waveform according to the heart rate by removing uninformative regions from the PPG, distinguishing between heartbeat and reflection pulses, dividing the heartbeat waveform into 10 segments and averaging the values according to each segments. PPG data were measured using telemedicine in both groups. Group 1 consisted of healthy people aged 25 to 35 years, and Group 2 consisted of patients between 60 and 75 years of age taking antihypertensive medications. The proposed algorithm could accurately determine which group the subject belonged with the newly measured PPG data (AUC = 0.998). On the other hand, errors were frequently observed in identification of individuals (AUC = 0.819).

FRAMEWORK FOR TOUCHLESS PATIENT MONITORING SYSTEM INTEGRATING ULTRA-WIDEBAND RADAR AND INTERNET OF THINGS FOR COVID-19 PATIENTS

Effective Health monitoring and patient monitoring system is an inevitable need for the present world with the rapid increase of affected people due to various chronic diseases. Monitoring various health parameters like body temperature, blood pressure, heart beat, oxygen level and sugar level is very important to be proactive against chronic and pandemic diseases. Internet of Things (IoT) has gained a great interest in the health care sector through different wearable sensor devices. Monitoring the patients is a great challenge during pandemic diseases since the doctors and other medical practitioners were exposed to risk. This paper presents a novel touchless health and patient monitoring system (THPMS) embedding ultra-wide band (UWB) and IoT technologies for effective monitoring of COVID-19 patients. The IoT module is specifically designed for health monitoring whereas as UWB radar is designated for effective touchless patient monitoring. The IoT module comprises of temperature sensor, heart beat sensor and pulse oximeter which senses and stores the information in internet cloud which can be accessed through mobile and web applications. The UWB radar is integrated with the system for touchless patient monitoring. The proposed system also embedded with heath assisting module where it allows the person to interact with the doctors after receiving their health parameters through the mobile bot application termed as IOT FITBOT. The proposed system offers health monitoring through IoT module, patient monitoring through UWB radar and health assisting through IOT FITBOT application. Mobile application and WEB based graphical user interface (GUI) is developed to receive the

Fetal Heart Rate Extraction using NLMS Algorithm

This project develops a fetal heart rate (FHR) extraction application to analyze the fetus activity in the mother uterus. Several methods are available that can be used to detect FHR such as using the fetal electrocardiogram (FECG) that generated by fetus’ heart. Extracting FECG signals is considered a major challenge while the fetus is inside the mother uterus. Normalized Least Mean Square (NLMS) algorithm is one of adaptive filters that is chosen as adaptive filter to get FECG. Pan Tompkins algorithm is used for tracking R-peaks of heartbeat pulses of FECG signal. After detecting the RR interval a formula is used to calculate the bpm (heartbeat per minute) of FECG. Abdominal and direct FECG (ADFECG) database will be used to evaluate the implemented techniques as it has reference signal. At the end of research, calculated FHR is varied from 125.4 bpm to 130.3 bpm. When comparison is done between abdominal ECG (AECG) and direct FECG (DFECG), the error of FHR is 0.1%. The accuracy of R-peaks extraction is 100% where all R-peaks are detected by implemented techniques. MATLAB is used for signal simulations. This system will have ability to interpret the non-invasive FECG (NIFECG) database and compute its FHR.

Soldiers health monitoring and tracking system using IOT and UAV

-For the past five years in Indian history there are totally 106 percent soldiers has lost their life during the terrorist attack so the checking of the soldiers health in a war and the observing during the terrorist attack are been so significant know a days. In the field of sensors the innovation has accumulated the tremendous development and numerous wearable body sensors (WBS) have been invented. In this paper we portray the system of WBS which will be mounted the soldiers jacket and the data from the WBS are been transmitted to the unmanned aerial vehicle (UAV) and for the explanation of strong communication the data are likewise been transmitted to the military truck. Furthermore, the data transmitted will be seen through the secured website in the receiver side so the communication between the transceiver will be the stronger one. The jacket consist of a body temperature & humidity sensor, heart beat pulse sensor, gas sensor, global positioning system (GPS) module and the medic space the outcome which appear in the web page where the precision are practically equivalent to the commercial device.

Exploratory Data Analysis Based on Remote Health Care Monitoring System by Using IoT

The system proposed in this article is to make Exploratory Data Analysis (EDA) of people’s health condition based on remote health care monitoring system in their different activities. Because of several critical physiological parameters of human body, multi body sensor health monitoring approach is vital. A health care monitoring system is displayed for detecting human physiological information. The system implementation consists of heart beat sensor, temperature sensor and pulse oxygen saturation level (SPO2) sensor. In this framework, gathered health information can be observed by utilizing both PC and cell phones. The real time result of the temperature sensor, heart beat sensor, SPO2 sensor to show on the ThingSpeak is tested. EDA analysis is done on measured data statistic to know the human activity for effective health care monitoring using IoT. The analyses are based on both theoretical approaches and experimental approaches according to the confirmation of recent research outcomes and real data from the hospital. The experimental studies have been carried out based on the idea of medical doctors in the hospital. The technology also confirms that the current research trends would be suitable for real world applications. The confirmation of medical doctors and suggestions on this research works were completed according to the standard of commercial products in the market.

A Generic and Robust System for Automated Detection of Different Classes of Arrhythmia

Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis electrocardiography (ECG) plays an important role. The Advancement of Medical Instrumentation (AAMI) suggests, all the heartbeat of MIT-BIH dataset into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed. Then, features computed from ECG signals are RR (separation between two successive heartbeat pulses), local binary pattern(LBP), morphological changes, wavelet, high order statistics, and several amplitude values; Pre-RR, Post-RR, Local RR and Global RR. The main reason is to computing many morphological features to comparison analysis with the model when trained and tested on single features vs combining all features together. Model gives highest performance which is 90.8% accuracy using LBP as single feature compared with other features. Combined all important features together and optimal features (total 104) are given as an input to Single SVM classifier as well as ensembles of SVM which maps the component feature vectors to the particular class name. Principal Component Analysis (PCA) technique is also applied to the model which reduces the total number of features from 104 to 30 features. The Single SVM system can accurately classify four beat types and has an overall classification rate of 94.05% without PCA and using PCA 92.84% while the Ensemble SVM classifies and with an accuracy of 92.96%

Non-Invasive, Continuous, Pulse Pressure Monitoring Method.

Many individuals suffer from ailments such hypertension that require frequent health monitoring. Unfortunately, current technology does not possess the ability for unobtrusive, continuous monitoring. This paper proposes estimation of pulse pressure based on pulse transient time determined from one non-contact, and one contact sensor: Doppler radar for non-contact detection of heart beat, and piezoelectric finger pulse sensor. The time delay between heart beat and finger pulse was determined using MATLAB software, and pulse wave velocity (PWV) was calculated. Finally, subjects' pulse pressure estimated using PWV was found to be in good agreement with pulse pressure measured using an off the shelf sphygmomanometer by reading and taking difference of systolic and diastolic blood pressure.

Measurement of shear wave propagation speed by estimation of two-dimensional wavenumbers using phase of particle velocity

Measurement of shear wave propagation speed in biological tissue is useful for evaluation of tissue stiffness. At present, there is a method using a shear wave generated by an ultrasonic push pulse, whose intensity is relatively higher than that used for B-mode imaging. However, this method poses a risk of tissue injury caused by cavitation. Therefore, a safer assessment method for shear wave propagation speed is required. In this paper, we propose a new method using shear waves generated by heartbeat pulses. Shear wave propagation speed is obtained by estimating wavenumbers in two dimensions using the phase of particle velocity. In basic experiments, different shear wave propagation speeds were obtained from two agar phantoms, whose stiffnesses were altered by changing the concentration of agar. In an in vivo experiment, the estimated shear wave propagation speed in a healthy liver was similar to those reported in the literature. These results show that the proposed method would be useful for safer estimation of shear wave propagation speed.

A flexible transparent one-structure tribo-piezo-pyroelectric hybrid energy generator based on bio-inspired silver nanowires network for biomechanical energy harvesting and physiological monitoring

The rapid development of wearable electronics like e-skins calls for self-powered sensing to harvest diverse bio-energies from human body as well as detect human physiological signals. Here, we report a novel, flexible and biocompatible triboelectric-piezoelectric-pyroelectric hybrid nanogenerator based on a significantly simplified structure with good transparency which mainly due to high-performance transparent electrodes (TEs). Inspired by gradually optimized leaf venation (LV), silver nanowires were arranged into a LV-like network through a green and cost-effective approach. The unique structure led to an ultra-high transmission up to 99% (at 68.2 Ω sq−1 sheet resistance) and sheet resistances as low as 1.4 Ω sq−1 (with 82% transmission). With 3 effects coupled, the generator can scavenge a maximum open-circuit output voltage of 55 V and 86 V to produce mechanical energy and thermal energy, respectively. In addition, the transparent hybrid nanogenerator can be conformally attached on different parts of body for real-time monitoring of various human vital signs including breath, heartbeat pulse and swallowing. With high transparency, the hybrid nanogenerator can be integrated with a thin liquid crystal film possessing thermochromic properties, potentially as a visualized thermometer for medical diagnostics. The research is a substantial advancement toward the realization of cost-effective self-powered sensing like pressure and temperature and its potential applications in device dimension miniaturization, healthcare monitoring, energy conversation, and wearable electronics.

High‐Performance Piezoelectric Nanogenerators with Imprinted P(VDF‐TrFE)/BaTiO3 Nanocomposite Micropillars for Self‐Powered Flexible Sensors

Piezoelectric nanogenerators with large output, high sensitivity, and good flexibility have attracted extensive interest in wearable electronics and personal healthcare. In this paper, the authors propose a high-performance flexible piezoelectric nanogenerator based on piezoelectrically enhanced nanocomposite micropillar array of polyvinylidene fluoride-trifluoroethylene (P(VDF-TrFE))/barium titanate (BaTiO3 ) for energy harvesting and highly sensitive self-powered sensing. By a reliable and scalable nanoimprinting process, the piezoelectrically enhanced vertically aligned P(VDF-TrFE)/BaTiO3 nanocomposite micropillar arrays are fabricated. The piezoelectric device exhibits enhanced voltage of 13.2 V and a current density of 0.33 µA cm-2 , which an enhancement by a factor of 7.3 relatives to the pristine P(VDF-TrFE) bulk film. The mechanisms of high performance are mainly attributed to the enhanced piezoelectricity of the P(VDF-TrFE)/BaTiO3 nanocomposite materials and the improved mechanical flexibility of the micropillar array. Under mechanical impact, stable electricity is stably generated from the nanogenerator and used to drive various electronic devices to work continuously, implying its significance in the field of consumer electronic devices. Furthermore, it can be applied as self-powered flexible sensor work in a noncontact mode for detecting air pressure and wearable sensors for detecting some human vital signs including different modes of breath and heartbeat pulse, which shows its potential applications in flexible electronics and medical sciences.

Flexible Non-Constrained RF Wrist Pulse Detection Sensor Based on Array Resonators

This paper presents the development of a non-contact, nonintrusive wrist pulse sensor based on the near-field variation of an array resonator. A compact resonator and its array were designed and fabricated on flexible substrate. The reflection coefficient of the resonator can vary as a function of the distance between the resonator and the walls of the major arteries, and the corresponding variation is utilized to obtain heart rate information at the wrist. To detect very weak pulse signals from the main arteries, a sensitivity enhancement technique was devised using a radio frequency (RF) array resonator. The sensor system was implemented with an RF switch to combine or select appropriate signals from the resonator element and was tested using the 2.4 GHz ISM band. The results demonstrated the sensor system's excellent performance in both sequential and simultaneous detection schemes. The measurement results showed that a heartbeat pulse can be detected from both radial and ulnar arteries via the array resonators. Considering the high sensitivity and characteristics, the proposed detection system can be utilized as a wearable, long-term health monitoring device.

Method and System for Human Action Detections with Acceleration Sensors for the Proposed Rescue System for Disabled and Elderly Persons Who Need a Help in Evacuation from Disaster Area

Method and system for human action detections with acceleration sensors for the proposed rescue system for disabled and elderly persons who need a help in evacuation from disaster areas is proposed. Not only vital signs, blood pressure, heart beat pulse rate, body temperature, bless and consciousness, but also, the location and attitude of the persons have to be monitored for the proposed rescue system. The attitude can be measured with acceleration sensors. In particular, it is better to discriminate the attitudes, sitting, standing up, and lying down. Also, action speed has to be detected. Experimental results show that these attitude monitoring can be done with acceleration sensors.