Real-time Heart Rate Monitoring Research Articles

Excellent Mechanical Performance of Cellulose Composite Papers for Flexible Self-Powered Photodetectors Using the ZnO/PbS Heterojunction.

Cellulose is attracting considerable attention in the field of flexible electronics due to its unique properties and environmental sustainability, particularly as a substrate for flexible devices. Flexible photodetectors are an integral part of cellulose-based devices and have become essential in optical communication, heart rate monitoring, and imaging systems. The performance and adaptability of these photodetectors depend significantly on the quality of the flexible substrates. However, poor bonding with conductive materials results in poor conductivity stability, and limited thermal stability makes cellulose unsuitable for many preparation processes. This study designed polyvinylpyrrolidone (PVP)-coated silver nanowires (AgNWs) as conductive materials, integrating them with cellulose nanofibers (CNFs). The resulting AgNW/CNF composite paper demonstrated high tensile strength (183.9 MPa), low sheet resistance (2.38 Ohm sq-1), and excellent bending durability. Spin-coated PbS colloidal quantum dots (CQDs) films and low-temperature magnetron-sputtered ZnO films were used as functional layers to create a flexible self-powered photodetector. This device features low dark current density (2.35 × 10-9 mA cm-2 @0 V), fast photoresponse (40/24 ms), and stability under bending (up to 180°, 1 mm radius). It shows potential for applications in infrared optical communication and real-time heart rate monitoring, demonstrating the promise of cellulose substrate photodetectors for flexible electronics.

Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Integrating Artificial Intelligence (AI) into Unmanned Aerial Vehicle (UAV) operations has advanced efficiency, safety, and decision-making. This study addresses critical gaps in UAV methods, including insufficient integration of human factors, operator variability, and the lack of systematic error analysis. To overcome these challenges, a novel approach combines the Analytic Hierarchy Process (AHP) with three core human factors models: the Observe-Orient-Decide-Act (OODA) loop, the Human Factors Analysis and Classification System (HFACS), and the SHELL model. An online survey was conducted across diverse UAV operator groups to prioritize critical factors within each model. Additionally, real-time monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) was conducted during UAV operations at various automation levels with different experience levels. Visualization through boxplots and percentage change matrices provided insights into operator stress and workload across automation levels. Integrating AHP findings and physiological data revealed significant differences in operator prioritization, highlighting the need for tailored AI-UAV strategies. This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.

The Wearable Band with Electromagnetic Band Gap Antenna for Heart Rate Detection System

Wearable antennas are antennas that can be applied to the human body and are made of flexible materials, making them ideal for healthcare technology. The quality of the signal received by the antenna directly affects the accuracy of heart rate detection If the antenna measurements indicate strong, clear signal reception, the heart rate monitor can accurately detect and interpret heartbeats. In this study, a planar monopole antenna was designed and developed using a circular patch with Ultra Wide Band (UWB) characteristics. The FR-4 and copper were utilized for the substrate and the ground plane and patch, respectively. Simulations and measurements were conducted at 2.4 GHz and 5 GHz. The antenna with the added EBG structure showed improved performance compared to the conventional antenna, exhibiting better S11 and VSWR values. Additionally, all radiation patterns were unidirectional. Applying this antenna to transmit heart rate measurements results in an accuracy of 94.34% compared to conventional onsite heart rate measurement. This study demonstrates that the wearable band provides real-time heart rate monitoring, while the EBG antenna enhances sensitivity and accuracy in detecting heart rate. This research can be enhanced by optimizing the EBG design and conducting additional trials to ensure the device performs well for a variety of users.

Advanced Necklace for Real-Time PPG Monitoring in Drivers.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Adrenaline intravenous therapy persistence grade I severe allergic reaction: A case report and literature review.

At present, there is still insufficient understanding of the progression from persistent allergic reactions to severe reactions. Adrenaline remains the preferred medication for severe allergic reactions, and intramuscular injection of adrenaline can also be considered for patients with grade I reactions that are difficult to alleviate gastrointestinal symptoms. It is worth further discussing whether it is possible to break the conventional intramuscular injection recommended by the guidelines when the effect of intramuscular injection is not ideal for persistent grade I severe allergic reactions. A young male, 20 years of age, was admitted to emergency department because of repeated rash for 3 days and abdominal pain for 6 hours after taking traditional Chinese medicine. After hormone therapy, the rash continued to recur and secondary gastrointestinal symptoms occurred on the 3th day. Adrenaline intramuscular injection was given to temporarily relieve the rash and abdominal pain, but symptoms still persisted. The patient was diagnosed with persistent severe allergic reaction (grade I). Continuous intravenous infusion of low-dose adrenaline under electrocardiographic monitoring, real-time monitoring of heart rate and blood pressure, and routine treatment with methylprednisolone, diphenhydramine, calcium gluconate, and cetirizine. During this period, adrenaline intramuscular injection is temporarily added when abdominal pain symptoms are obvious. The entire treatment process used a total of 6.8 mg of adrenaline. During the entire period of adrenaline intervention, the patient did not experience any new discomfort, and there were no abnormal fluctuations in heart rate, rhythm, or blood pressure. The symptoms of rash and abdominal pain gradually improved. For patients with persistent grade I severe allergic reactions, intravenous administration of low-dose adrenaline under close vital sign monitoring is safe, feasible, and highly effective in preventing biphasic, persistent, or worsening allergic reactions.

IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy.

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

High-accuracy heart rate detection using multispectral IPPG technology combined with a deep learning algorithm.

Image Photoplethysmography (IPPG) technology is a noncontact physiological parameter detection technology, which has been widely used in heart rate (HR) detection. However, traditional imaging devices still have issues such as narrower receiving spectral range and inferior motion detection performance. In this paper, we propose a HR detection method based on multi-spectral video. Our method combining multispectral imaging with IPPG technology provides more accurate physiological information. To realize real-time evaluation of HR directly from facial multispectral videos, we propose a new end-to-end neural network, namely IPPGResNet18. The IPPGResNet18 model was trained on the multispectral video dataset from which better results were achieved: MAE = 2.793, RMSE = 3.695, SD = 3.707, p = 0.304. The experimental results demonstrate a high accuracy of HR detection under motion state using this detection method. In respect of real-time monitoring of HR during movement, our method is obviously superior to the conventional technical solutions.

CADCare: Smart System for CHD Identification & Sensor Alerts

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.

Lightweight and interpretable convolutional neural network for real-time heart rate monitoring using low-cost video camera under realistic conditions

Recent research has shown that a person's heart rate (HR) can be estimated using video data through remote photoplethysmography (rPPG). However, this approach is faced with various challenges, including the inability to prepare training data that encompasses all realistic conditions, the impact of complex inference models on reasoning speed, and the lack of interpretability that hinders medical and healthcare applications. To tackle these issues, a lightweight and interpretable convolutional neural network is proposed for real-time HR monitoring using a low-cost video camera under realistic conditions. The Mediapipe framework is leveraged to construct a facial detection and tracking pipeline that is robust to head movements and illumination changes. Empirical mode decomposition (EMD) is then combined with a channel-wise attention-based convolutional neural network (CNN) for HR inference. Additionally, a temporal long-term peak merge method is proposed as a post-processing step to further enhance the accuracy of the neural network inference. The results of linear regression and Bland-Altman analysis demonstrate consistency between the estimated HR values and the ground truth. Moreover, experimental outcomes show no significant difference in the inference times of the proposed method running with or without a GPU, with a reasoning speed on mobile CPU remaining within 100 ms, ensuring real-time HR monitoring. Furthermore, this study provides pioneering empirical evidence to open the black box of neural networks in HR monitoring using rPPG signals.

Abnormal heart rate detection through real-time heart monitoring application

Health monitoring that requires doctors and patients at the healthcare center may not be practical during the coronavirus disease 2019 (COVID-19) pandemic. Alternatively, mobile health (mHealth) should be embraced to minimize contact between patients and healthcare personnel. This research aims to enhance the detection of abnormal heart rate (HR) detection by developing a real-time heart rate monitoring (RTHM) application. Sixteen healthy adults participated in a physical real-time HR monitoring testbed. Participants HR was measured for three minutes resting and three minutes performing moderate-intensity physical activity. The results were compared with the polar beat app. Additionally, the energy consumption, the time taken to receive an alarm message, and an acceptance test were analyzed. The app is acceptably accurate, the mean absolute percentage error less than 2%. The response time to receive the alarm message is 30 seconds on average, which is under an acceptable range of medical standards. Moreover, the app is power efficient, 477 mW on average. Participants show a positive attitude towards using RTHM. RTHM is expected to provide a more plausible tool for monitoring the heart towards enhancing abnormal HR detection by promoting patient-oriented healthcare and minimizing sudden deaths due to heart failure.

Abnormal heart rate detection through real-time heart monitoring application

Health monitoring that requires doctors and patients at the healthcare center may not be practical during the coronavirus disease 2019 (COVID-19) pandemic. Alternatively, mobile health (mHealth) should be embraced to minimize contact between patients and healthcare personnel. This research aims to enhance the detection of abnormal heart rate (HR) detection by developing a real-time heart rate monitoring (RTHM) application. Sixteen healthy adults participated in a physical real-time HR monitoring testbed. Participants HR was measured for three minutes resting and three minutes performing moderate-intensity physical activity. The results were compared with the polar beat app. Additionally, the energy consumption, the time taken to receive an alarm message, and an acceptance test were analyzed. The app is acceptably accurate, the mean absolute percentage error less than 2%. The response time to receive the alarm message is 30 seconds on average, which is under an acceptable range of medical standards. Moreover, the app is power efficient, 477 mW on average. Participants show a positive attitude towards using RTHM. RTHM is expected to provide a more plausible tool for monitoring the heart towards enhancing abnormal HR detection by promoting patient-oriented healthcare and minimizing sudden deaths due to heart failure.

An improved real-time detection algorithm based on frequency interpolation

Real-time monitoring of heart rate (HR), i.e., extraction of heart rate variability (HRV), plays an important role in diagnosis and prevention of cardiovascular diseases. Compared with traditional contact monitoring devices, the use of continuous wave (CW) Doppler radar to monitor HRV does not require contact and is not sensitive to light and temperature, which makes it more and more popular. To monitor the HRV based on CW Doppler radar, the time window must be shortened to less than 5 s, which will lead to the spectrum leakage and degrade the measurement accuracy of HRV. To solve this problem, a custom CW Doppler radar has been developed in an integrated fashion on a single PCB, whose transmitting frequency and power of the radar are 24 GHz and 3 dBm, respectively. Furthermore, four frequency interpolation algorithms are introduced to compare their extraction accuracy. Experiments are performed on three subjects, and results show that the Quinn algorithm can obtain best HRV extraction results compared with other algorithms. Specially, the average HRV extraction error is 3.61% using the Quinn algorithm.

Standardized motion detection and real time heart rate monitoring of aerobics training based on convolution neural network

In order to make the teaching and training of aerobics more standardized, it is necessary to use scientific means to detect and monitor the movement standardization in teaching and training and the change of human heart rate in the training process, but at present, there are some difficulties in both detection and monitoring, Therefore, this paper proposes to use the advantages of convolutional neural network to solve the current aerobics teaching problems of motion detection and heart rate monitoring. In the process of operation, the complete aerobics video needs to be divided into several different images, the standardized action image background needs to be eliminated, and then the visual error caused by the difficult action image needs to be corrected. On the premise of image processing, the convolutional neural network is used to pre train the image, and the skeleton map of the human body is constructed in the computer. In the process of practical operation, the use of convolutional neural network for heart rate monitoring has many advantages. It can not only save the time of contact with the human body, but also integrate various information of the time dimension, reducing a lot of computing steps, saving a lot of computing resources for practical work, and promoting the improvement of system output signal quality to a certain extent. The result of the experiment also proves that the convolutional neural network can improve the accuracy of students' movement detection and heart rate change monitoring in aerobics teaching and training.

Self-Filtered NIR Photodetectors Based on Silicon Micro-Holes Array for Stable Heart Rate Monitoring

Here, we presented a novel near-infrared (NIR) photodetectors based on Si micro-holes array/graphene (SiMHs/Gr) Schottky barrier diode (SBD) for monitoring heart rate. The SBD exhibited a unique self-filtered NIR response in a range from 900 nm to 1160 nm with a responsivity of 122 mA/W at zero bias voltage. Moreover, our device possessed an ultrahigh signal to noise ratio (SNR) of more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{{5}}$ </tex-math></inline-formula> between visible and NIR light. The presented self-filtered NIR photodetector was used as a non-contact transmission-type photoplethysmography sensor for real-time heart rate monitoring. Due to the suppression of disturbances caused by ambient light, our device performed more accurate heart rate monitoring than those extracted from commercial broadband photodetectors.

Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.

Effect Of Flexi-bar Exercise Intervention On Cognitive Function Of Middle-aged And Elderly Women

BACKGROUND:Aerobic training (AT) can improve the ability of working memory and inhibitory control of healthy elderly. Flexi-Bar vibration exercise, as a special form of exercise has been confirmed to have a positive effect on the cognition. This study aimed to evaluate whether Flexi-Bar exercise can be used as an effective intervention to delay the decline of cognitive function caused by aging and its potential mechanism. METHODS:A total of 30 middle-aged and elderly sedentary women (aged 55.47 ± 5.45 years) were randomly divided into the intervention group and the control group. Mini-mental State Examination (MMSE) and international physical activity questionnaire (iPAQ) were administered at baseline before randomization. The ability of inhibitory control was assessed via the Stroop task before and after the whole intervention. Functional near-infrared spectroscopy (fNIRS) was used to measure the oxygenated hemoglobin (O2Hb) concentrations in the prefrontal area. The intervention group was given the Flexi-Bar exercise intervention 3 days/week for 8 weeks with an intensity of 50 ~ 65% HRmax for 1 hour/day, and the real-time heart rate monitoring was carried out during the intervention. The control group only maintained normal daily life. RESULT:(1) Compared with the control group, O2Hb in the left ventral dorsolateral prefrontal lobe in the exercise group significantly increased (p < 0.05, t = 2.46). (2) Compared with the pre-test of EX group, the brain-derived nerve growth factor (BDNF) in the intervention group significantly increased after the intervention (p < 0.05, t = 2.304), but there was no significant difference in the control group (p > 0.05). (3) In terms of epigenetic miRNA, a total of 165 differentially expressed miRNAs were screened between the intervention group and the control group, of which 60 miRNAs were related to the cognitive function. In the intervention group, 4 miRNAs positively correlated with cognitive decline were down-regulated (all p < 0.01). The down-regulation of miR-126-5p and miR-21-5p was negatively correlated with the expression of BDNF. CONCLUSION:These findings supported our hypothesis by demonstration of a beneficial effect of Flexi-Bar exercise that can improve the cognitive function of middle-aged and elderly people.

Research on noncontact heart rate detection based on face tracking and BVP signal extraction

The noncontact heart rate detection technology is very suitable for daily health monitoring due to its advantages of no contact with the human body, simple operation, and low cost. However, this technique is susceptible to ambient light variation interference and motion artifacts. In addition, most of the existing HR (real-time heart rate monitoring) detection studies based on remote photoplethysmography only focus on illumination variation interference or motion artifacts, and few studies consider both illumination variation interference and motion artifacts. First, a nonparametric skin color segmentation model and support vector machine (SVM) are used in the Cb and Cr components of the YCbCr color space to construct a joint skin color segmentation model specific to the current video sequence, to remove the skin-like background in the video frame in a coarse-to-fine manner. Subsequently, the CamShift algorithm color histogram is constructed within the Cr component and face tracking is performed. It should be noted that the use of SVM to build a skin color segmentation model is based on the following three considerations: first, the skin color segmentation algorithm based on SVM has a relatively fast running speed. Second, considering skin color and background sample information at the same time will help to improve the accuracy of skin color segmentation. Third, SVM can enhance the generalization ability of the learning machine according to the principle of structural risk minimization, which makes the skin color segmentation model based only on video standard frames can also play a better role in the segmentation of subsequent video frames. The experimental results show that the proposed algorithm has higher accuracy for noncontact heart rate detection than the original algorithm; compared with the tracking algorithm in recent years, it has good tracking accuracy and obvious speed advantage.

Continuous time series analysis on the effects of induced running fatigue on leg symmetry using kinematics and kinetic variables: Implications for knee joint injury during a countermovement jump.

This study investigates the symmetry change in joint angle and joint moment of knee joints following a Running-Induced Fatigue counter movement Jump. Twelve amateur runners volunteered to participate in the study. A prolonged running protocol was used to induce fatigue. Joint angle and moment were recorded during the push and flexion phase of the CMJ before and immediately after fatigue. Borg scale (RPE>17) and real-time heart rate monitoring (HR>90%HRmax) were used to confirm running fatigue. Symmetry function (SF) was used to assess the symmetry of the knee Angle and moment variation parameters over the entire push-off and landing phases based on time series analysis. Paired sample t-test was used to examine changes in SF before and after acute fatigue. The Angle and moment of the knee are asymmetrical in all planes (SF > 0.05), with SF ranging from 5 to 130% in angle and 5–110% in moment. There was a significant increase in knee joint angle asymmetry in the horizontal plane during the push-off and landing stage following the prolonged - Running Protocol implementation. These increases in asymmetry are mainly caused by excessive external rotation of the dominant knee joint. These findings indicate that fatigue-induced changes during CMJ may progress knee movement pattern asymmetry in the horizontal plane.

WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT

Abstract The rapid development and growth in the Internet of Things (IoT) area offer great potential in the healthcare sector. Currently, wireless technology, fitness trackers, and body sensors in the sports field have a major impact on living efficiency and health system reliability. Wearable devices have become increasingly interested in measuring physiological factors, promoting health, and improving adherence to the practice in different populations from elite athletes to patients. In this paper, fog assisted Computational efficient Wearable sensor networks (FCE-WSN) has been proposed in health monitoring systems for sports athletic using IoT. The wearable device for the continuous real-time monitoring of heart rate, respiratory frequency, and movement cadence during physical activity has been analyzed. Besides, the sensor collected data is uploaded to the IoT-connection system Ethernet module, and the Authorized Individual accesses the data via the internet to track the athletics health. Moreover, the wearable device's analytical model and its use illustrate how computing resources' costs can be minimized while preserving health requests for access to medical information stored in a fog and cloud distribution setting. Our assessment system depends on a queue and can estimate the minimum computing resources needed to achieve the Service Level Agreement (SLA) (both fog and cloud nodes). The experimental results show that the proposed method is user-friendly, reliable, and economical to use regularly.