Real-time Heart Rate Measurement Research Articles

Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.

Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening.

Revolutionizing Health Management: Developing a Wearable Device for Real-time Heart Rate Measurement and Prediction of Hypertension Risks

This research paper introduces SyncFit, a wearable gadget coupled with a web platform for instant heart rate monitoring and hypertension risk forecasting. The gadget employs a Python-based random forest algorithm to scrutinize heart rate data and anticipate abnormal rates signaling hypertension risk. The web platform, constructed with Streamlit and Firebase, grants users access to their heart rate analysis and hypertension risk evaluation. The research showcases SyncFit's capability to transform health management by enabling individuals to proactively monitor their health and predict risks, thereby facilitating optimization of their wellness journey. SyncFit enhances user experience with a range of features beyond its core functions, ensuring effortless integration into daily routines. Its sleek, ergonomic design prioritizes comfort, enabling seamless wear throughout the day. Additionally, its intuitive interface and user-friendly controls facilitate easy navigation and customization, catering to diverse user preferences. SyncFit's robust construction and advanced tech establish a new benchmark for wearable health monitors, blending style and functionality to empower users in their wellness journey. SyncFit commits to constant improvement and innovation, with ongoing R&D aimed at expanding capabilities and addressing emerging health challenges. Future versions will integrate more sensors and advanced analytics, offering a deeper understanding of health. Integration with AI and cloud tech will revolutionize personalized health management, making preventive care proactive, empowering individuals to optimize well-being. Key Words: wearable device, heart rate, hypertension risk, web application, machine learning, artificial intelligence, streamlit.

SMART System in the Assessment of Exercise Tolerance in Adults.

Health-oriented physical activity should meet two key criteria: safety and an optimal level of exercise. The system of monitoring and rationalization of training (SMART) was designed to meet them. SMART integrates a custom-configured inertial measurement unit (IMU) and a sensor with real-time heart rate measurement (HR) using a proprietary computer application. SMART was used to evaluate the safety and exercise load with 115 study participants: 51 women (44.35%) and 64 men (55.65%) aged 19 to 65 years. The exercise test was the 6MWT test. In 35% of the participants, the mean HR exceeded the recognized safe limit of HR 75% max. Ongoing monitoring of HR allows for optimal exercise and its safety. Step count data were collected from the SMART system. The average step length was calculated by dividing the distance by the number of steps. The aim of the present study was to assess the risk of excessive cardiovascular stress during the 6MWT test using the SMART system.

RESEARCH ON WIDE SPECTRUM FULL-COLOR NIGHT VISION ELDERLY FACIAL VIDEO HEART RATE AUTOMATIC MEASUREMENT AND WARNING SYSTEM

In recent years, the prevalence of cardiovascular diseases has been growing geometrically, especially among the elderly. Real-time heart rate monitoring becomes an effective measure for preventing cardiovascular diseases. Traditional contact-based real-time heart rate measurement methods require long-term wearing of contact electrodes, which can cause inconvenience to the elderly population. In this study, a wide spectrum full color night vision elderly face video heart rate automatic measurement and early warning system was designed and developed by combining imaging photoplethysmography (IPPG), video acquisition equipment based on starlight wide spectrum camera and high-speed face recognition signal processing algorithm based on deep learning. By constructing a software and hardware platform, a comprehensive evaluation of the system’s application in elderly facial video heart rate measurement experiments was conducted.

Development of algorithm for work intensity evaluation using excess overwork index of construction workers with real-time heart rate measurement device.

The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

A Human Expert Based Approach to Heartrate Monitoring System with Automatic Message Sending Via Smartphones

The objective of the paper was to propose a human – expert based approach on a heartbeat calculating and message sending system. Heart related disease is rising among today's population. The need for an accurate yet affordable heart rate is essential to ensure one's health quality. However, in today's market, most heart monitors are expensive and are not user friendly. Our goal is to calculate the heart beat using smart phones that is user friendly and less expensive. In Today's situation 50% percent of people using Smart phones, so in order to make our goal possible we are using the smart phones to calculate the humans heart beat rate accurate. In this project, a real-time heart rate measurement technique called Photo-Plethysmography (PPG) is implemented using simple infrared transmitter and receiver circuit. Our main aim to decrease the death rate of humans due to heart related diseases, so we can assign the smart phones to sending an emergency alert message to the particular persons relatives and to an ambulance or to the nearby doctor resources along with the location of the user when the Blood pressure is increasing/decreasing then normal rate. By sending an alert message the lives of an human will be recovered from critical situation.

Non-contact and real-time measurement of heart rate and heart rate variability using microwave reflectometry.

In this paper, we present noncontact and noninvasive vital signal detection using a microwave reflectometer. Elimination of noise components due to random movement of human subjects has been the biggest issue for microwave measurement. Appropriate filtering, amplitude control of the reflectometer signal, and cross correlation among multiple reflectometers together with new algorithms have enabled motion artifact elimination, signal peak detection, and data processing for various parameters related to heart rate (HR) and heart rate variability (HRV). We focus here on the real time measurements of instantaneous HR and HRV for practical use. The evaluation by microwave reflectometry is completely noninvasive and feasible even through clothing, which is extremely effective for health maintenance in daily life as well as for preventing sudden death related to, for example, coronary heart disease and ventricular arrhythmia.

Non-contact Real-time Heart Rate Measurement Algorithm Based on PPG-standard Deviation

Heart rate is an important physiological parameter for clinical diagnosis, it can infer the health of the human body. Thus, efficient and accurate heart rate measurement is important for disease diagnosis and health monitoring. There are two ways to measure heart rate. One is contact type and the other is non-contact. Contact measurement methods include pulse cutting, electrocardiogram, etc. Because of the inconvenience of this method, a non-contact heart rate method has been proposed. Traditional non-contact measurement method based on image is collecting RGB three-channel signals in continuous video and selecting the average value of the green channel pixels as the heart rate signal for processing. However, this method is not accurate when the pixel values are changing greatly. To overcome this problem, non-contact real-time heart rate measurement method is proposed in this paper based on pixel standard deviation. Because of the changes in skin color caused by heart rate, the standard deviation signal of the green channel pixels in the region of interest (ROI) is filtered and extracted by the forward and inverse Fourier transform respectively, measuring the heart rate. The experimental results show that the improved algorithm can measure heart rate with faster speed and higher accuracy comparing to traditional methods. And we hope that our algorithm can apply in intelligent elder caring.

Bounded Kalman filter method for motion-robust, non-contact heart rate estimation.

The authors of this work present a real-time measurement of heart rate across different lighting conditions and motion categories. This is an advancement over existing remote Photo Plethysmography (rPPG) methods that require a static, controlled environment for heart rate detection, making them impractical for real-world scenarios wherein a patient may be in motion, or remotely connected to a healthcare provider through telehealth technologies. The algorithm aims to minimize motion artifacts such as blurring and noise due to head movements (uniform, random) by employing i) a blur identification and denoising algorithm for each frame and ii) a bounded Kalman filter technique for motion estimation and feature tracking. A case study is presented that demonstrates the feasibility of the algorithm in non-contact estimation of the pulse rate of subjects performing everyday head and body movements. The method in this paper outperforms state of the art rPPG methods in heart rate detection, as revealed by the benchmarked results.

Contactless and Hassle Free Real Time Heart Rate Measurement with Facial Video

AbstractIn the era of automation, an automatic and noninvasive tool is required for day-to-day health monitoring. Cardiac pulse being one of the vital physiological parameters has drawn attention of researchers for its measurement. In this article, the authors present an approach for automatic cardiac pulse measurement, using a typical webcam. In the presented approach, face region is detected and RGB traces are retrieved for the face region. FFT is applied on the extracted traces to compute power spectrum of the respective traces. The peaks of the power spectrum limited in the band region (0.75–4 Hz) are investigated to provide cardiac pulse measurement. The presented approach is hassle free in nature, and hence, can be adopted even in home environment. Experimental results represent the efficacy of the proposed approach.

Real-time Heart Rate Measurement based on Photoplethysmography using Android Smartphone Camera

With the development of smartphone technologies enable photoplethysmogram (PPG) acquisition by camera and heart rate (HR) measurement. This papers presents improved algorithm to extract HR from PPG signal recorded by smartphone camera and to develop real-time PPG signal processing Android application. 400 video samples recorded by Samsung smartphone camera are imported as input data for further processing and evaluating algorithm on MATLAB. An optimized algorithm is developed and tested on Android platform with different kind of Samsung smartphones. To assess algorithm's performance, medical device Beurer BC08 is used as reference. According to related works, accuracy parameters includes 90% number of samples that has relative errors less than 5%, Person correlation (r) more than 0.9, and standard estimated error (SEE) less than 5 beats-per-minutes (bpm).

Use of Biotelemetry to Define Physiology-Based Deterioration Thresholds in a Murine Cecal Ligation and Puncture Model of Sepsis.

Murine models of critical illness are commonly used to test new therapeutic interventions. However, these interventions are often administered at fixed time intervals after the insult, perhaps ignoring the inherent variability in magnitude and temporality of the host response. We propose to use wireless biotelemetry monitoring to define and validate criteria for acute deterioration and generate a physiology-based murine cecal ligation and puncture model that is more similar to the conduct of human trials of sepsis. Laboratory and animal research. University basic science laboratory. Male C57BL/6 mice. Mice underwent cecal ligation and puncture, and an HD-X11 wireless telemetry monitor (Data Sciences International) was implanted that enabled continuous, real-time measurement of heart rate, core temperature, and mobility. We performed a population-based analysis to determine threshold criteria that met face validity for acute physiologic deterioration. We assessed construct validity by temporally matching mice that met these acute physiologic deterioration thresholds with mice that had not yet met deterioration threshold. We analyzed matched blood samples for blood gas, inflammatory cytokine concentration, cystatin C, and alanine aminotransferase. We observed that a 10% reduction in both heart rate and temperature sustained for greater than or equal to 10 minutes defined acute physiologic deterioration. There was significant variability in the time to reach acute deterioration threshold across mice, ranging from 339 to 529 minutes after cecal ligation and puncture. We found adequate construct validity, as mice that met criteria for acute deterioration had significantly worse shock, systemic inflammation (elevated tumor necrosis factor-α, p = 0.003; interleukin-6, p = 0.01; interleukin-10, p = 0.005), and acute kidney injury when compared with mice that had not yet met acute deterioration criteria. We defined a murine threshold for acute physiologic deterioration after cecal ligation and puncture that has adequate face and construct validity. This model may enable a more physiology-based model for evaluation of novel therapeutics in critical illness.

무선 압력센서를 이용한 실시간 맥박수 측정기 개발

Department of Electronics and Control Engineering, Hanbat National University, Daejeon 34158, Korea(Received March 14, 2016; Revised April 19, 2016; Accepted April 22, 2016)Abstract: Among the various physiological information that could be obtained from human body, heartbeat rate is a commonly used vital sign in the clinical milieu. Photoplethysography (PPG) sensor is incorporated into many wearable healthcare devices because of its advantages such as simplicity of hardware structure and low-cost. However, healthcare device employing PPG sensor has been issued in susceptibility of light and motion artifact. In this paper, to develop the real-time heart rate measurement device that is less sensitive to the external noises, we have fabricated an ultra-small wireless LC resonant pressure sensor by MEMS process. After performance evaluation in linearity and repeatability of the MEMS pressure sensor, heartbeat waveform and rate on radial artery were obtained by using resonant frequency-pressure conversion method. The measured data using the proposed heartbeat rate measurement system was validated by comparing it with the data of an commercialized heart rate measurement device. Result of the proposed device was agreed well to that of the commercialized device. The obtained real time heartbeat wave and rate were displayed on personal mobile system by bluetooth communication.Keywords: Heartbeat, Wearable, Wireless power transmission, Pressure sensor, LC resonance !

Emotions in dogs being petted by a familiar or unfamiliar person: Validating behavioural indicators of emotional states using heart rate variability

Although physical human–dog interactions have an important influence on the human–dog relationship, few studies have proposed the key factors of physical human–dog contact affecting the well-being of dogs. Therefore, our aim was to investigate the effect of physical human–dog contact on the behaviour and cardiac activity of dogs. Thereby, we focused on the human–dog familiarity and the body area petted. The participating nine male and 15 female dogs were either familiar (familiar group) or unfamiliar (unfamiliar group) with the testperson. The testing procedure consisted of nine physical human–dog interactions applied in a random order, e.g. petting and holding the lying dog on the ground, holding a forepaw of the dog, petting the dog on its shoulder, and covering the dog's muzzle with one hand. The dogs were petted in a prescribed manner. A test sequence lasted for 30s and the inter-test sequence interval was set at 60s. The whole testing procedure was videotaped and the frequency and duration of the dogs’ behavioural responses were analysed. For the non-invasive real-time measurement of heart rate (HR) and heart rate variability (HRV) the RS 810 Polar-Systems® was applied on the dog's chest.The mean HR (F1,197=15.32, P<0.001) and root mean square of successive heartbeat interval differences (RMSSD) response values (F1,204=12.11, P=0.001) differed significantly between the dog groups. The HR values of the familiar group were significantly higher compared to the baseline values (F1,23=13.42, P=0.001). The RMSSD values of the unfamiliar group decreased significantly compared to the baseline values in all human–dog interactions (F8,45=4.36, P=0.001). Furthermore, differences of RMSSD values were analysed between the familiar and unfamiliar group, especially if the dogs were being petted on the chest, head or shoulder, under the neck, being held on the collar or ground, or when the muzzle was covered with a hand. The dogs showed more appeasement gestures if the physiological parameters revealed an increased HR and decreased RMSSD value compared to the baseline values. Likewise, decreased RMSSD values were associated with an enhanced frequency of redirected behaviours.Our results point out that the assessment of dogs’ behavioural and physiological responses during physical human–dog contact provide adequate information, whether the level of the dogs’ emotional conflict might jeopardize the benefits associated with human–dog interactions.