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Optical fiber sensors have been validated for physiological measurement as a potential device for smart textiles and wearable sensors. Also, developing sensors with Polymeric Optical Fiber (POF) shows promising applications in dynamic and real scenarios, as low-cost alternatives. Furthermore, the functioning of POF sensors can be used in no-skin contact applications, such as in highly skin-sensitive children with Autism Spectrum Disorder (ASD) during therapies. On the other hand, Dog-Assisted Therapy (DAT) is a stress-reducing method and has been studied to identify its influence on improving therapies and stress-related events. This work presents a case study in three weekly DAT sessions focused on speech therapy in a subject with ASD. A validated low-cost POF sensor was used in this first dynamic physiological measurement scenario. The analysis of five Heart Rate Variability (HRV) variables was made to compare the participant's physiological status before and during the DAT. The obtained pulse signal showed the dynamic movements affecting the signal but being able to obtain the pulse peaks. The physiological comparison showed a difference in the mean peakto-peak time interval when the child was interacting with the dog than previous to the interaction in Session 1 and Session 2 (p-value<0.05). Also, an improvement in phonological consciousness was obtained, increasing by eight points after the three weeks of therapy. This case study showed the potential use of POF sensors in dynamic environments, and for wearable or smart textiles for children with ASD. [ABSTRACT FROM AUTHOR]

The physiological response of motorcyclists significantly related to driving stress can be used as an indicator of traffic safety measurement. The driver's stress can be measured using heart and respiration rates. In this study, the experiment was conducted along the busiest urban arterial road in the capital city of Aceh province. This research aims to provide empirical results expected to determine the safety threshold for motorcyclists' stress on arterial roads in Banda Aceh City. The Polar Vantage V2 Smartwatch Multisport is used to measure the heart and respiration rate of volunteer motorcycle users. A total of 25 volunteer motorcycle users contributed to this study, with a total of collected data 750. The Heart Rate Variability (HRV) method and driving stress were discovered to be essential factors in measuring traffic accidents. The variables observed were the Heart Rate (HR) and Respiration Rate (RR) using Polar Vantage V2. Furthermore, the GoPro camera uses to record the geometric conditions of the targeted road segment. The findings from our experiment reveal that the average HR and RR for the volunteer are 96 bpm, and 24 bpm, respectively. These HR and RR values show that motorcyclists in Banda Aceh are considered to have abnormal HR and RR. Consequently, they considered unsafe driving stress. [ABSTRACT FROM AUTHOR]

Asthmatics often endure severe recurrent attacks, which repeatedly interfere with daily life. To meet this challenge, the Asthma Attack Prediction System (AAPS) was developed with a unique U-shaped design. The system comes with an attack prediction (AP) algorithm, using a combination of biosensors to predict attacks by monitoring cough sounds, throat movements, and heart rate – an alternative way to detect asthma severity an integrated smartphone is a remarkable feat. In emergencies, AAPS can mechanically alert hospitals through SMS, ensuring well-timed intervention. Calibration and validation against business gadgets revealed superb accuracy, with coronary heart charge measurements hitting 99.03% accuracy and cough sounds with throat movement sensors recording 93.75% and 95.83% accuracy, respectively. This gave an impressive 96.2% overall detection precisely. These consequences pass beyond previous studies and highlight advances in assault prediction, portability-making plans, and implementation. [ABSTRACT FROM AUTHOR]

This research presents an accurate remote monitoring prototype for the elderly biomedical signals. A MAX30102 sensor was interfaced with the Arduino Nano 33 BLE Sense microcontroller (nRF52840). The measurement accuracy was validated relative to the Benchmark (Pulse Oximeter) depending on the mean absolute error (MAE). Experimental results show that the proposed current system obtained an MAE of 3.904 and 1.156 for heart rate and SpO2, respectively, suggesting a close agreement between the Benchmark and the proposed system. The proposed prototype has an accuracy of about 99 and 98.3 for heart rate and SpO2, respectively. The suggested system is monitoring in real-time, accurate, applicable, low-cost, and effective resources for transmitting data. Moreover, the proposed system can be used in many applications and for parameters other than heart rate and SpO2 with simple modification (e.g., gait rehabilitation, heartbeat rate, and electrocardiography for observing the health care system, respiratory rate, muscle action, and elderly fall detection). [ABSTRACT FROM AUTHOR]

Blind or visually impaired people face several challenges in safely navigating their environments due to their lack of inclusive real-time obstacle detection and surrounding information aid. In this paper, a smart stick for visually impaired people was designed and practically implemented. The smart stick system contains heart rate, ultrasound, and moisture sensors. The system's core utilizes an Arduino Nano microcontroller programmed in C++ and GPS, GSM/GPRS, a vibration motor, a buzzer, and a battery. Ultrasound and soil moisture sensors are used to detect the obstacles and the nature of the floor faced by the visually impaired person during navigation in various environments. The heart rate was employed to monitor the health condition of the visually impaired. Moreover, the position of the visually impaired individual was tracked in real-time based on GPS, and the geolocations were transmitted to the family members through the GSM/GPRS network to facilitate the monitoring of the user. The vibration motor and buzzer alert the user to unwanted environments, like obstacles, wet surfaces, or elevated heart rates. The performance of the alert decision was enhanced by utilizing an Artificial Neural Network (ANN) implemented in Matlab software. The analysis results revealed exceptionally high correlation coefficients (R²) across all datasets: 0.997 for training, 0.988 for testing, 0.982 for validation, and 0.994 for all combined datasets, as determined by the applied ANN. Furthermore, the alert decision error was enhanced to 4.096×10-5. This error revealed that applying ANN resulted in a remarkable reduction in errors relative to those obtained from statistical analysis of differences among the smart stick system and benchmark systems. [ABSTRACT FROM AUTHOR]

This project mainly aims at developing a cardio arrest monitoring device. The device is designed to evaluate and display 3 parameters namely heart rate, body temperature and pulse rate in real time. The main advantage of the hardware is it is cost effective and specifically small size so that it work on daily basis. Individuals with history of attack will have a continual change in heart rate and body temperature. The fluctuation varies throughout time periods of one or even two weeks. The data obtained from the individuals will be displayed on the device and if the values displayed in the device are abnormal then the individuals are warned. Thus the chances of cardiac arrest can be predicted. [ABSTRACT FROM AUTHOR]

This system utilizes GPS and GSM technology to track the health and position of soldiers in the field. The system includes wearable devices that soldiers wear on their bodies, which monitor vital signs such as heart rate, oxygen levels, and body temperature. These devices also have GPS capabilities, allowing for real-time tracking of the soldier's location. In case of an emergency, the soldier can press a button on the device to send an alert to their superiors, who will then be able to locate them using the GPS data. The system also allows for communication between soldiers and their commanders through the GSM network, providing an efficient way to keep track of the soldiers' movements and overall well-being. The system can also be integrated with other technologies, such as drones, for monitoring and surveillance purposes. The data collected from the wearable devices can also be analyzed to identify patterns and trends in the soldiers' health,which can be used to improve overall troop health and readiness. Overall, this system provides a valuable tool for military operations and can help ensure the safety and well-being of soldiers in the field. [ABSTRACT FROM AUTHOR]

Based on heart rate signals collected during fight tasks, the present work analyzes unmanned air vehicle pilots' workload qualified with A1/A3 Open category. Preliminary results are based on a sample of physiological data from five pilots. The physiological indexes for the workload assessment are computed by the Heart Rate Variability (HRV) based on the electrocardiogram signal acquired with a specific app developed by the authors. The tasks performed and the difficulty of the associated maneuver are also described. Results are given in terms of HRV indexes to characterize the mission difficulty based on objective measurements of mental workload. Results obtained can be assumed as a source to create a database for developing a workload assessment model of the human being interacting with an unmanned system combining HRV indexes in a single index. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a novel method for controlling the heart rates of people with atrial fibrillation who are being treated with pacemaker devices. To achieve the system's goals, the P and PID controller methods are used. In all, there are forty personnel spread over two sets of controllers: proportional (P) and proportional (PID) controllers. The current parameters for the sample size are as follows: 80% significance, 80% Gpower, 80% confidence, 95% confidence, and a 1% enrollment ratio. Overshoot, rise/fall/settling times, and peak/settling times are some of the metrics used to assess the performance of P and PID controllers. Rise Time, Peak Time, Settling Time, and Overshoot averaged 0.0633, 0.0633, 6.1700, and 48.2290 for the proportional controller (P). Rise Time (0.0586), Peak Time (0.0586), Settling Time (7.1667), and Overshoot (51.0441) were often seen in PID controllers. Independent sample T-test results suggested that the Proportional Integral Derivative (PID) Controller was slightly more effective than the Proportional (P) Controller, but after examining all of the relevant factors, we were unable to identify a statistically significant difference between the two groups. [ABSTRACT FROM AUTHOR]

Aim: To investigate the sodium atom movie potential to divert impacts in atrial center Electrocardiogram (ECG) age for the norm and remarkable condition utilizing Rungekutta system. Materials and Methods: In this research human atrial cells are based on Seemann et al. method can be used to describe the action potential model of sinoatrial nodes. Sample size can be calculated as 70 and the G power is 80%. Results: The conventional film capacity of sodium is 81.2 mV, but for the different abnormal values of 100%, half and 25%, then the membrane potentials are 162.4 mV, 121.8 mV and 101.5 mV and thereafter the reducing of 100% sodium levels doesn't outline single ECG waveform, decreasing of half and 25%, the membrane potentials are 40 mV and 37.7 mV. By taking a gander at these characteristics there is a differentiation in pacemaking development and causes capricious heart beat and prolongation occurs. Conclusion: In common place Action Potential, the withdrawal will happen reliably. In strange AP, the conduction of electrical excitation will change and make erratic heart work cause arrhythmia. A predominant perception of the cell model can be used to perceive the prescription that helps with making a regular heart beat. [ABSTRACT FROM AUTHOR]

This study intends to create a novel heart rate regulation technique for pacemaker-using atrial fibrillation patients. This study will focus on people in this situation. System uses Proportional controller (P) and Proportional Integral Derivative (PID) controllers. The PID controller controls the control group, whereas the P controller manages research. The following categories and subcategories describe sample size features: The enrollment ratio and cutoff must be one and 0.05. Gpower is required for 80% of the population. Rise Time, Peak Time, Settling Time, and Overshoot assess proportional (P) and PID control efficacy. Overshoot is the sixth time to evaluate proportional (P) and PID controllers. The sixth choice is overshoot. The proportional controller's Rise and Peak averaged 0.0633, Settling 6.1700, and Overshoot 48.2290. (P). In a proportional integral derivative (PID) controller, rising, peak, settling, and overshoot times were 0.0586, 0.0586, 7.1667, and 51.0441 milliseconds, respectively. An independent sample T-test suggested that the Proportional Integral Derivative (PID) Controller was somewhat better than the Proportional (P) Controller, although other parameters were similar between the two groups (p>0.05). This showed that the PID Controller is not much superior than the P Controller. [ABSTRACT FROM AUTHOR]

The present paper is devoted to experimentally registered recordings of ECG signal and heart rate, using a textile-embedded online monitoring tool for recording heart activity. The experiment was conducted by surveying squash athletes during their training period. [ABSTRACT FROM AUTHOR]

The delay in the doctor's arrival to the patient and the difficulty of diagnosis the patient's condition consistently is one of the health issues that this field suffers from it. Internet of things (IoT) based on health monitoring systems are very valuable for patients where the doctor can know the patient's condition from anywhere in the world. This research presents a real-time Smart Healthcare Monitoring System Based on an IoT Environment. This system will be able to measure values of body temperature, heart pulse rate, and oxygen saturation in patients using a small device that contains a set of sensors such as a body temperature sensor, heart rate sensor, and oxygen sensor, as well as a processor that collects data and transmits it via internet, so a specialist can view the results any time and from any location by utilizing a mobile application. this paper aims to present an architecture design in order to develop of health monitoring system and to describe the main required components proficient for producing efficient and accurate health monitoring. In this work, the high price and size of the device were taken into account, and the problem was addressed by reducing the size of the components and connecting them with a small "Arduino mini", which in turn is low in cost. This system is efficient because of its low power consumption, ease of setup, great performance, and quick response time. [ABSTRACT FROM AUTHOR]

Using synchrosqueezed wavelet transform of patterns of electroencephalograms and electrocardiograms, the features of the frequency relationships between the brain and heart rhythms during hyperventilation load in patients with vascular pathology were revealed. The data obtained can be useful in determining the neurophysiological correlates of the severity of cerebral vascular pathology. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a commonly observed cardiac arrhythmia in clinical contexts, accounting for roughly one-third of hospitalizations for cardiac rhythm disorders. The present epidemiological evidence has substantiated that Atrial Fibrillation (AF) is a prevalent worldwide epidemic that has deleterious effects on both enduring morbidity and mortality. The manual interpretation of an electrocardiogram (ECG) for diagnostic purposes is a demanding and time-consuming process that requires a significant level of expertise. Furthermore, the issue is characterized by inconsistencies among various observers and even within a single observer. The identification of Atrial Fibrillation (AF) presents difficulty owing to its frequent asymptomatic nature and the necessity of conducting multiple diagnostic examinations to verify its existence. The lack of adequate documentation or identification of atrial fibrillation (AF) and its associated comorbidities is a cause for concern. Atrial fibrillation (AF) has the capacity to give rise to significant medical complications, such as stroke and heart failure, among others. The proposed study aims to create a machine learning algorithm that has low computational requirements and can accurately identify Atrial fibrillation in its early stages using short single lead electrocardiogram signals in near real-time. The framework under consideration has been primarily developed with a focus on the Heart Rate Variability (HRV) characteristics of the human cardiac cycle. The framework underwent training and testing on the MIT-BIH Atrial Fibrillation and CinC 2017 Database and demonstrated a sensitivity of 95.30% and specificity of 94.71% through the implementation of a ten-fold cross-validation approach. The framework was subjected to experimentation using medical samples obtained from a local laboratory, and the results indicate an accuracy rate of approximately 91.5%. The proposed method could help doctors find atrial fibrillation early on during routine ECG screenings. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) is the name given to using mobile technology, including wearables and health tracking applications, to monitor and share health information. The use of mobile devices and wireless technologies to monitor symptoms and provide care helps clinicians to make diagnoses more quickly and with fewer mistakes. The ease of mHealth is a significant benefit for patients and clinicians. In this study, a mobile health (mHealth) application for cancer survivor monitoring and screening is developed within the project PERSIST. To that aim, objective and subjective data, including heart rate, blood pressure and questionnaires, are continuously collected from wearable devices. The application enables data flow between users and doctors. Health parameters and personal inputs are analyzed regularly so that users can benefit from better quality care and improved health and well-being. [ABSTRACT FROM AUTHOR]

To forge effective collaborations with humans, robots require the capacity to understand and predict the behaviors of their human counterparts. There is a growing body of computational research on human modeling for human-robot interaction (HRI). However, a key bottleneck in conducting this research is the relative lack of data of cognitive states -- like intent, workload, and trust -- which undeniably affect human behavior. Despite their significance, these states are elusive to measure, making the assembly of datasets a challenge and hindering the progression of human modeling techniques. To help address this, we first introduce Rescue World for Teams (RW4T): a configurable testbed to simulate disaster response scenarios requiring human-robot collaboration. Next, using RW4T, we curate a multimodal dataset of human-robot behavior and cognitive states in dyadic human-robot collaboration. This RW4T dataset includes state, action and reward sequences, and all the necessary data to replay a visual task execution. It further contains psychophysiological metrics like heart rate and pupillometry, complemented by self-reported cognitive state measures. With data from 20 participants, each undertaking five human-robot collaborative tasks, this dataset (comprising of 100 unique trajectories) accompanied with the simulator can serve as a valuable benchmark for human behavior modeling. [ABSTRACT FROM AUTHOR]

In 2013 we published an experimental study (Versteegh and Van der Heijden, doi.org/10.1007/s10162-013-0393-0) that introduced a traveling-wave perspective of cochlear nonlinearity, the so-called spatial buildup of compression and suppression. We recorded basilar membrane (BM) motion in the base of the gerbil cochlea, and obtained experimental evidence for the spatial buildup by independently varying the frequencies of the suppressor and the probe. The study concluded that the nonlinear response at any point in the cochlea can only be properly understood in terms of a spatial buildup of compression. Traveling waves start out as linear responses to the stapes drive and gradually accumulate compressive nonlinearity after entering their peak region, ∼½ octave short of their best place. Likewise, the nonlinear effects of intensity on vibration phase can only be understood in terms of local changes in propagation velocity of the wave. Once viewed through this lens, a large body of published data on the effects of compression and suppression, often featuring complex patterns and seemingly contradicting trends, was shown to fit in a unifying framework. Despite the emphasis on spatial buildup, the data underling the 2013 study were obtained from single BM locations, due to the need for reflective microbeads with laser Doppler vibrometry. The spatial aspect was only explored by proxy, i.e., by varying the stimulus frequency and exploiting the tonotopy of the cochlear response ("scaling"). Optical coherence tomography (OCT) vibrometry has liberated us from this limitation, and in the current work we actually traced the spatial buildup of compression. In the BM and OHC region of the 13-24-kHz region of the gerbil cochlea, we recorded responses to equal-amplitude tone pairs centered at 15 kHz, spaced by 20 Hz. The resulting beating pattern affords a dynamic way of obtaining I/O curves. Spectral analysis of the responses yielded an accurate quantitative description of the compressive growth. We observed that the amount of compression (the difference in gain between the peak and dip episodes of the beat) increased gradually and systematically along the course of the slowly propagating wave, which confirms the spatial buildup claimed in our 2013 study. With increasing instantaneous intensity, the spatial rate of compression increased systematically over the nonlinear region. Overall, the conclusions of the 2013 study were confirmed, emphasizing the tight relation between cochlear compression and the wave character of cochlear vibrations. [ABSTRACT FROM AUTHOR]

This project presents the use of IoTs in the healthcare systems. This project presents wireless heart disease detection and monitoring heart rate. This program contains a specific component. Monitoring the heart rate is a significant factor in maintaining heart health. People from different age groups have different levels of maximium and lower heart rates. We all know that heart disease could kill one life in 3 attempts. Now the days are especially dangerous in the early stages. Taking control of our body means we can protect our health from all other diseases. Our lives are very important and very precious. Nowadays many people lose their precious lives due to a sudden heart attack. A heart attack is caused by diet, age, lack of exercise etc. The heart is not easily accessible. Winning and assisting our community with a heart attack to develop such a system that can help reduce mortality in the early stages. In this case we use or improve the heart rate monitoring and cardiovascular monitoring system using IoT (Internet of Things). To achieve the best possible monitoring, connected nerves can be of a type of cord that you know or are attached to where we live. This program monitors patients pulsate, vital saliva saliva. if there is a high parameter that goes beyond the remote value this sensible system notifies doctor or caregivers and triggers correct measures to aid many patients' lives. People face rejection problems and die from various illnesses that are due to a lack of timely medical treatment for patients. The primary goal of developing a systematic patient monitor systems is using IoTs. Patient pulse rate or data is observed and monitored and sent to mobile doctors containing applications. The IoT-based patient monitoring system has successfully implemented patient life and condition and saved lives on time. [ABSTRACT FROM AUTHOR]

Health monitoring is a serious concern nowadays as there is a need for regular monitoring of some parameters in our body like temperature, heart rate, oxygen levels etc. In the present scenario, utmost care has to taken towards individual's health. As the world is now facing the issue of Covid 19, there is a shoot-up in the demand for temperature and heart sensors which can detect the temperature and heart rate of an individual. These values upon being measured can be stored in a cloud platform for further analysis. A doctor can remotely monitor a patient looking at the values stored in the database which avoids physical contact with the person. This application is a part of IoT system where there is a flow of data from bottom to top i.e. from device layer to cloud layer. This objective of this paper is providing a system which can monitor the patient's health and provide a basic treatment necessary in case of any medical supervision. [ABSTRACT FROM AUTHOR]

Emotion is a multifaceted phenomenon that plays a critical role in enhancing one's quality of life by influencing motivation, perception, cognition, creativity, empathy, education, and decision-making. Emotion is also closely related to stress regarding previous reports. Most reported studies use a long-duration of data recording and employ complex features to classify certain emotions. However, those systems are challenging to be implemented in a real-time environment. We proposed a low-cost real-time detection system that can recognize two basic emotions. Fifteen male university students participated in this study. To elicit various emotions, we provided pictures as the visual stimuli for six seconds during the experiment from the International Affective Picture System (IAPS) database that represented five fundamental emotions (happiness, sadness, fear, surprise, and anger). We proposed a single photoplethysmograph parameter, namely Heart Rate Changes (HRC), before and after viewing a picture's event. As a single parameter, HRC can be obtained in six seconds and utilized as an input of a classifier model. We employed logistic regression as a binary classifier to differentiate positive and negative emotions. We tested with 30%, 40%, and 50% test sizes data and obtained the best performance when recognizing positive (happiness) and negative (sadness) with 47%, 55%, and 57% regarding the AUC metric. We concluded that the HRC is sensitive to sadness as negative emotion during this study rather than the others (fear and anger). Our proposed system has the possibility of real-time emotion recognition with further specific improvements for future studies. [ABSTRACT FROM AUTHOR]

The components of a low-cost, open-source mechanical ventilation system device were created using a 3D printer. Due to a global shortage of ventilators for COVID-19 patients, this kind of ventilator is required. In certain regions, particularly in impoverished communities, the COVID-19 epidemic has been quite tough. Structure that seeks to mitigate the impacts of scarcity in these regions. Only commercial spare components are used here, according to the documentation. This study also discusses a computerized approach for monitoring patients' lung health. This technique additionally considers air pressure, oxygen saturation (Spo2), and heart rate (Bpm) to inform clinicians if a patient is healthy or sick in real time. It depicts laboratory trials that have led to the potential benefits of a derivative ventilator for both healthy and sick patients. [ABSTRACT FROM AUTHOR]

Anxiety disorder has been known as one of mental disorder characterized by significant and uncontrollable feelings of anxiety and fear. Anxiety may cause physical and cognitive symptoms such as restlessness, irritability, easy fatigability, difficulty concentrating, increased heart rate, chest pain, abdominal pain, and many others. Motivated by current research that accompanies anxiety and stress with physical reactions such as increased heart rate, blood flow, dilation of the pupil and skin conductance, this work builds on the premise that real-time measurement of such reactions could indirectly recognize older adult anxiety while interacting with the system. For this research, an in-house computer mouse with embedded sensors circuit was constructed and simulated via Proteus environment to test the heart rate and skin conductance of the users. In this project a rules-based algorithm for distinguishing the anxiety disorder events have been developed. The detection is being processed based on the measured physiological data that being quantified via the embedded sensors in the mouse circuits. The result shows that the developed system able to detect mental health state of the user at accuracy of 0.87, which defines a good performance of the proposed system. [ABSTRACT FROM AUTHOR]

Now a day, nobody in this world is worried about what is happening around them. Many people lose their precious life mainly because of the non-availability of timely care and treatment. If it takes 10 months of care for a person to come to this earth, it takes hardly 10 minutes after the critical accident or a severe heart attack. According to NDTV survey, 50% of death occurs only due to information delay to hospital in accidents or while taking to hospital in both heart attack and accidents. With a view to reduce this type death, a smart helmet is designed and developed. This project implies the design of smart helmet. It senses the blood clot in the brain using the heart beat sensor, body temperature sensor, pulse oximetry sensor, optical sensor and LDR type sensor embedded in the helmet. If blood clot occurs in the brain due to accident, the amount of radiation from LEDs becomes high. This indication is sensed and intimation is sent to the registered mobile number. The information obtained can be used to provide necessary treatment required for the victim. [ABSTRACT FROM AUTHOR]

The aim of the study was to have a convenient and reliable heart rate monitor which could work efficiently for different cardio exercises. The designed cuffless device was compared with commercial smartwatch for 2 cardio regimes (skipping and walking). The subjects under study were divided firstly in 2 main, group 1 for BPM data from smart watch and group 2 from cuff-less device. Each group had 20 normal individuals under study. The G power was kept to be 80% with 0.05 alpha. Based on the cardio regimes the 20 individuals in each group were further divided as BPM reading for skipping and walking respectively. Hence a total of 4 groups were under study. The ANOVA results between the BPM recorded using smartwatch and cuffless device was statistically insignificant. The one way ANOVA, P value was 0.933 (P>0.05 one way ANOVA). Henceforth we would like to conclude that the designed cuff less device using BME280 is an equally efficient heart rate monitor as compared to any commercial smartwatch available in the market (one way ANOVA (P>0.05), insignificant). [ABSTRACT FROM AUTHOR]

A pulse oximeter is an electronic instrument which is used to measures the blood oxygen level and pulse rate level of human body. These pulse oximeters use red light, infrared light and photodiode to measure the blood oxygen level and heartbeat level. This pulse oximeter system enables instant recognition of heart disease and corona. There are two types of pulse monitoring systems are available. They are Transmittance type and Reflective type. In reflective type red and infrared LED's are used to measure the blood flow and blood oxygen level. Red light is used to penetrate the tissue and find the blood vessels and infrared light wavelength is used to find the blood flow and blood oxygen level. Based on the infrared light emission photodiode absorb the reflected signal. Based on the photodiode signal Arduino UNO calculate the blood oxygen level and heart beat level. The blood oxygen level and heartbeat level is calculated by using specific blood saturation and heart beat equation. The MAX 30102 is small integrated sensor. Which measure the blood saturation level and heart beat level. It operates at 400 Hz. This sensor detect the heartbeat level and blood oxygen level by using Infrared light, Red light and photodiode. This system had provision for low range alarm settings when SpO2 below 95% and pulse rate below 70 BPM on that time this system gives alarm. When the Spo2 level is below 95% and pulse rate below 60 BPM on the time that system create a vibration and alert the user. [ABSTRACT FROM AUTHOR]

To investigate the calcium particle membrane potential to divert impacts in atrial node Electrocardiogram (ECG) age for the ordinary and unusual condition utilizing Rungekutta technique. In this research human atrial cells are based on [1] method can be used to describe the action potential model of sinoatrial nodes. Sample size can be calculated as 70 and the G power is 80%. The normal membrane potential of calcium is -30 mV, but for the different abnormal values that is increasing of 100%, 50% and 25% of the membrane potentials are -52mV, -26mV and -42mV and then the decreasing of 100% and 50% calcium levels does not form single ECG waveform, decreasing of 25%, the membrane potential is - 45mV which having the mean value range of 7mV to 17mV. By comparing these values there is a difference in peacemaking activity and causes irregular heart beat and prolongation occurs. In normal Action potential, the contraction will occur regularly. In abnormal AP, the conduction of electrical excitation will differ and make irregular heart function cause arrhythmia. In the future, a better understanding of the cellular model can be used to detect the drug that helps to make a normal heart beat. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to compare novel mobile applications for monitoring glucose levels to traditional monitoring methods. Methods and Materials: The readings of the various heart rates and glucose levels are taken and the sample size is 40. SPSS software was used for identification of significance between the two groups. Parameters were maintained as a threshold by 0.05, enrollment ratio as 1, 95% confidence interval power at 80%. Results: The results can be viewed on a mobile screen after the successful login the user can see the readings of heart rate and glucose levels. The independent test values for heart rate is 53bpm and blood glucose is 128 mg/dl. Between the two groups, there is a statistically negligible difference (mobile app: p=0.986, conventional monitoring: p=0.895, p>0.05). Conclusion: The glucose level monitored using mobile applications (React Native) showed more accurate and faster results and can be used in real-time applications. [ABSTRACT FROM AUTHOR]

The main objective of this work is to create an innovative heart rate control technique for pacemaker devices used by patients with Bradycardia. Materials and Methods: This method employs the proportional control (P) and integral derivative (ID) controller methods, with the Proportional Controller (P) acting as the control group and the Integral Derivative(ID) Controller serving as the study group, each with a sample size of 40 calculated by keeping G power 80% and threshold as 0.05. Results: The efficiency of both Proportional (P) and Integral Derivative (ID) controllers was measured using Rise Time, Peak Time, Settling Time, and Overshoot. In Proportional Controller (P), the mean Rise Time is 0.0633, the mean Peak Time is 0.0633, the mean Settling Time was 6.1700, and the mean Overshoot was 48.2288. In an Integral Derivative (ID) Controller, the mean Rise Time was 0.0530, the mean Peak Time was 0.0530, the mean Settling Time was 6.4123, and the mean Overshoot was 52.9560. Conclusion: A statistical analysis result confirms that Integral Derivative (ID) Controller was significantly better than Proportional (P) Controller but there was no statistical significance for all the parameters when compared between groups. [ABSTRACT FROM AUTHOR]

The fitness tracker can measure oxygen saturation, heart rate, blood pressure, and temperature. With the help of the Internet of Things, a new era for medicine has begun. A health tracker based on IoT is used in this proposal to measure factors. In addition, it is quite useful for displaying and saving data. Through the Internet of Things, all resources can be easily connected and quality of life can be enhanced. Whenever an irregularity occurs, users will receive email or SMS notifications from this IoT-based healthcare tracking system. Using the Internet of Things, doctors can also access and save data to read on their mobile devices. Using the Internet of Things, doctors can obtain information that is useful to them and allow them to make quick decisions. [ABSTRACT FROM AUTHOR]

The majority of heart illnesses can be diagnosed by analysing the ECG signal for arrhythmias. The P-QRS-T waves in an ECG signal represent one cardiac cycle. Due to varied aberrant rhythms and noise distribution, extracting powerful features from raw ECG data for fine-grained illnesses classification remains a difficult task today [1]. Previous research has primarily relied on heartbeat or single scale signal segments for ECG interpretation, ignoring the underlying complementing information of several scales. This suggested study addresses several methods and transformations that have been previously described in the literature for analysing an ECG signal and extracting features from an arrhythmia analysis. [ABSTRACT FROM AUTHOR]

Most elderly people are frequently affected by disease; generally, the elderly begin to experience a decline in physical condition, resulting in various physical and mental changes, and they are easily affected by illness, so they must take medications on a regular basis and according to the proper schedule. However, there are still elderly people who take medication irregularly because their memory is starting to weaken. Early signs experienced by the elderly who suffer from memory skills are forgetting about information that has just been obtained or learned and often having difficulty completing daily work routines, so it is necessary to develop a research on products that help the elderly in carrying out regular drug-taking activities without need help from people to remind them. In this research, the design thinking method was used, namely identifying the wants and needs of 20 elderly people while taking medication and identifying them according to the first stage of design thinking empathy, through interviews and direct observation at the Guido Valadares Hospital in Timor-Leste. After the first stage, the author enters the second stage, which is define to describe the user's view which will be the basis of the product to be made. With the first and second stages already, the author will enter the third stage, namely ideate to describe the required solution. This study aims to describe each stage of design thinking in the design of reminder aids for the elderly in an effort to create appropriate and effective solutions to the problems faced by patients at Guido Valadares Hospital Timor-Leste. This study uses a qualitative approach with descriptive analysis. The result of this study is that with a good design thinking process, this research has resulted in a solution for patients in the hospital by designing an image of a proposed reminder tool that helps patients take medication regularly. The implementation of the design thinking stage that was carried out was not optimal due to material constraints so that it did not reach the prototype and test stages for users, and the author only used three stages of design thinking (empathy, define, and ideate), without reaching the prototype and test stages does not affect the results of the identification using the design thinking method. In designing a reminder tool, it is hoped that it can help patients at the hospital to take medication on time and can check heart rate and oxygen levels in the body when needed, the product is expected to be easy to use so that users can use it easily. Reminder tools can assist users with audible alarm sounds for reminders. The reminder aid product also has a large screen so that users can see the time easily and can see the results of checking heart rate and oxygen levels and the results of subsequent alarms. The reminder aid has a button that can set the next alarm so that it helps the patient easily. Reminder aid products also have additional partitions to divide medication in the morning, noon, or evening. [ABSTRACT FROM AUTHOR]

This research aims to create a prototype of a smart home system to assist an elder in a home care scenario. Smart home system users can control devices such as lights using voice commands, keep track of the user's heart rate, monitor house situation, and store user's events using a schedule reminder. The smart home system will use Raspberry Pi 3 as the main microcontroller, Wemos D1 as a microcontroller on the smart home model, Broadlink RM Mini 3 as the medium to control an air conditioner and television, and Xiaomi Mi Band 2 as the wearable device. The program will use the Node-RED, NORA, and Firebase platforms which are very helpful in the development of the Internet of Things. System testing is done by testing the features of smart home systems, wearable devices, schedule reminders, as well as surveying the level of user satisfaction. The test results show that the smart home system can function properly. The microphone can detect voice commands up to a two-meter distance. Broadlink is able to control devices up to a three-meter distance. Wearable devices have succeeded in measuring the user's heart rate up to a distance of 9 meters, whereas the schedule reminder manages to store user events. Additionally, survey results indicate that respondents are satisfied with our system features. [ABSTRACT FROM AUTHOR]

Humans need communication for survival in this community group is the fact that humans are social creatures. By communicating with each other, people indirectly exchange information, but any information provided is not completely correct based on actual events or facts. By saying that it is not appropriate, the information provided to the recipient becomes a problem so that the emergence of new information that is wrong or often referred to as lying. To reduce the occurrence of errors in analyzing the information received, a tool is needed to detect information conveyed from someone's words based on the level of someone's lies. This research was conducted using the Arduino Mega tool that applies the Tsukamoto fuzzy logic method that works with two GSR and Heartrate sensors as input to determine the level of someone's lies. From the results of this study by testing 30 times, it was found that the accuracy of Tsukamoto's fuzzy logic in determining the level of lies was 73.33%. [ABSTRACT FROM AUTHOR]

Heart disease is a very dangerous disease, and threatens the life of the sufferer, heart disease can also cause other diseases such as hypoxemia which is a condition that occurs because the oxygen content in the blood is not enough so that it can cause damage to important organs in the body such as the brain and kidneys, a person's risk of heart disease can be predicted in several ways, by calculating the heart rate per minute or using classification methods. The method used in this research is the Naive Bayes classification method, with the Naive Bayes method the probability of each attribute is calculated and classified into the specified class. The system developed by the author is a web-based system to predict a person's risk of heart disease, by classifying trigger factors such as diabetes, blood pressure cholesterol, and in addition to heart rate and blood oxygen content (Spo2). Input used in the system made by the author in the form of heart rate per minute, blood oxygen content (Spo2), cholesterol, diabetes, and blood pressure. The system is made using an IoT-based prototype tool designed by the author to take the user's heart rate and blood oxygen content (Spo2) input values. The conclusion obtained in this study is that by using the Naive Bayes method, the prediction of a person's risk of heart disease can be predicted and has an accuracy of 96%, with 100 test data. [ABSTRACT FROM AUTHOR]

Any type of stress will be harmful to physical and mental health if left untreated. It is of great significance to detect stress in its early stages before it causes any harm. This paper proposed the implementation of the Fuzzy Logic technique to detect stress according to two physiological inputs; heart rate and skin conductance. The implemented Fuzzy Logic uses Mamdani Fuzzy Inference System (FIS) provided by MATLAB R2020b and is designed with nine rules to obtain the state of stress; "stress" or "not stress". The simulation results show the designed FIS is able to detect stress with 87.5% accuracy which considered a reliable technique for stress detection. [ABSTRACT FROM AUTHOR]

Procedure for examining vital signs and awareness must be carried out in places determined by law enforcement. Often they do not admit that they are drunk, then refuse the examination. The aim of this research is to make a tool that is able to identify and determine the parameters of consumption of alcohol levels. Where the parameters used are through the detection of alcohol levels and heart rhythm, this can be done by the authorities. The process of checking whether a person is in the influence of alcoholic beverages by measuring gas levels in the breath and heart rate rhythm. Where to measure alcohol levels using the MQ-3 sensor, while for heart rate variability using a photoplethysmography sensor. Then the gas concentration data (1BAC: 2100ppm) and pulse rate data are obtained, which is then processed and then the awareness level of the suspect. By designing using regression (α = 11.14185295, b = -26.3749) the MQ-3 sensor has an error of 0 %. While the Heart Rate variability design is error 2.52%. Based on the test results, this tool can detect whether a person is under the influence of alcoholic drinks or not where measurements are made using two parameters, namely gas levels in the breath and heart rate rhythm. [ABSTRACT FROM AUTHOR]

One of the most significant issues facing hospital authorities today is implementing a real-time monitoring system to track patients' physical status. The Health Monitoring System (HMS) is a sophisticated modern technology that offers an alternative to traditional health management. This article presents the development of a mobile application that uses biosensors and Bluetooth technology to measure and show human biological data. Heart rate, body temperature, ambient temperature, and humidity are all measured by biosensors. The doctor will be able to check the patient's health at regular intervals by scanning the barcode of the built mobile application. The proposed system will improve the system's efficiency while also lowering costs and reducing errors. This system's output displays a heart rate of 70-75 beats per minute, a body temperature of 35-40 degrees Celsius, and a humidity of 70-75 grams per cubic meter. [ABSTRACT FROM AUTHOR]

This paper is focused on the implementation of an athletic performance system for the measurement of the vital signs like Heart rate, temperature, and kinetic parameters like the number of revolutions of a fixed bike wheel for football players. The proposed system uses a Raspberry Pi3 as a base station, in addition to the ESP-32S microcontroller for data collection tools from sensors. Wi-Fi technology is utilized as a communication tool between wireless sensors and the base station. The received data are stored in the base station database, which in turn is analyzed using the fuzzy system. The fuzzy system was applied on three-player status; the rest state, during exercise and after the effort, to evaluate the final player assessment. [ABSTRACT FROM AUTHOR]

Learning educational boards are one of the most significant devices in the education of students in universities. This technique, considered for the particular creation of the experimental setting, enables students to learn basic skills quickly and attain successful careers. This paper designs and tests an educational training board to educate students how to set-up the equipment in medical systems. The educational training board consists of many sensors and actuators like electrocardiogram (ECG), heart rate, motion processing unit (MPU), servomotor, DC motor, relay, and ultrasonic sensor (UUS). In addition, to record data and appearance information techniques, there is a mini oscilloscope and LCD. The experimental results were successfully used by 87 students to motivate learners and apply a variety of medical sensor applications. The difference ratio of the educational boards' depending on the opinions of the students is about 8.3%. The educational training board has been aimed at actual experiments. This work has generally involved the progress of an experimental system for several courses to address inadequacies in the teaching of medical sensors. Therefore, this board is an opportunity to teach students to monitor and record signals and learn to avoid mistakes during work and operation. [ABSTRACT FROM AUTHOR]

An electrocardiogram (ECG), it is used to screen for numerous cardiac issues. De-noising ECG data is a crucial pre-processing technique that minimises noise while increasing the normal swells in ECG signals. Experimenters have devised a number of methods for describing morphological abnormalities throughout time. This research looks at the process and design principles used by various styles, as well as the classification of state-of-the-art styles into separate orders for collective comparison and the development of ultramodern styles to denoise ECG. Different approaches' performance is compared using standard measures such as root mean square error, chance root mean square difference, and signal-to-noise rate improvement. ECG is important in diagnosing the majority of heart problems. The P-QRS-T swells make up one cardiac cycle in an ECG signal. For posterior analysis, this point birth technique determines the boundaries and intervals in the ECG signal. The borders and intervals values of the P-QRS-T component govern the cardiac function of every individual. Fuzzy Sense Styles, Artificial Neural Networks (ANN), SR-Scat Net Algorithm, Support Vector Machines (SVM), and other Signal Analysis methods were heavily used in the presented schemes. All of these methods and algorithms have benefits and drawbacks. This suggested study addresses the various methods and metamorphoses for determining the rooting point from an ECG signal that have been proposed previously in the literature. [ABSTRACT FROM AUTHOR]

With various advances in innovation, different wearable sensors have been produced for observing human physiological boundaries. A Body Sensor Network comprising of such physiological and biomedical sensors near orwithin a human body can be utilized for observing them constantly. This paper reports an tracking and monitoring health parameters of the soldiers in war field. The suggested technology may be worn by soldiers to keep track of theirhealth which includes their Temperature level, Blood oxygen level, Humidity level, Blood pressure rate, Heartbeat rate, Pulse rate using sensors such as Temperature sensor, Blood oxygen sensor, Humidity sensor, Blood Pressure sensor, Heart beat sensor, Pulse rate sensor and their current location using GPS. The data coming from sensors and GPS receiver will be transmitted wirelessly to Blynk app using IOT. This Assists the Soldiers Control Unit in reducingsearch rescue and time attempts. [ABSTRACT FROM AUTHOR]

We send the Anytime Medical Counter in every one of the country regions where individuals can't get great/best specialist on target. We introduce Heart Beat, Temperature sensor; Ultrasonic sensor, load cell, Camera and Head telephone arise likewise associated with the Medical dispatcher. Clinical contradictory client together is screen withthe aim of the distant region. Implementation exists as introduced one an the other finishes to pronounce correspondence and talking in the company of specialist. Specialist especial the sufferer recommends drugs and the Medicine Dispatcher will dispatchen the drugs from the Medical Dispatcher as far as client. Prescriptions ensue refreshed on sensible cloud server. Client catapulates solicitation in the vicinity waiter togged oneself on the drugs admission potencies. [ABSTRACT FROM AUTHOR]

The heart rate is one of the most important factors while dealing with the health of humans. Recently it has been shown that, you can use normal camera to detect the radiance changes in the face skin which is caused by human cardiac pulse from which we can calculate the heart rate of humans. Contact heart rate measurements like ECG (Electrocardiography) requires patients to wear external devices like chest straps which are attached by sticky gel that may causes discomfort and sometimes it may hurt the patients. So, it is difficult to use these types' of methods for calculating heart rate. This paper gives a solution to work with real time video streams for measuring heart rate. Face detection issued in this paper which issued produce asset of face rectangles, it is of sample of RGB (Red, green, blue) and its (ROI) chosen on the face that express a signal that corresponds to the heart rate. This paper analyses an optimal framework that is able to works independently from multiple local regions. A set of face rectangles is generated using face detection and object tracking, sampled of RGB (Red, Green, Blue) and its (ROI) on the face, which expresses a signal that corresponding to the heart rate. This paper analyses an optimal framework that able to works independently from multiple local regions. In this paper we present an optimal framework that comes from multiple local regions yet works independently. [ABSTRACT FROM AUTHOR]

Health workers as the front liners in facing covid-19 pandemic are the most vulnerable group to infection. Covid-19 virus infection has a long-term impact on health including autonomic dysfunction. The objective of this study became to determine how the Covid-19 status affects the Resting Heart Rate (RHR) which is a reflection of the Cardiac Autonomic Function. This research is an observational analytic study with a cross-sectional design and the sampling method was total sampling and obtained 149 residents who served at the Zainoel Abidin Hospital from March-September 2021. Data were obtained from filling out questionnaires by participants and measuring RHR. We used the Independent T to assess the difference in the average RHR according to the historical status of Covid-19. There was a significant difference of RHR for resident who had been infected with Covid-19 [60 residents (40.3%)] and those who were not [89 residents (59.7%)] with p=0.002, the average RHR for those who had been infected was higher than those who had never been infected. It can be concluded that the RHR in resident after Covid-19 is higher than those who have never experienced Covid-19. [ABSTRACT FROM AUTHOR]

The Faculty of Medicine at Syiah Kuala University (USK) employees play a significant role in the continuity of the faculty's work. Even though working from home has been applied during the pandemic, in person meetings with health protocols are sometimes needed. Many employees have been confirmed positive for coronavirus infection. There are many reports of further effects on the cardiovascular organs of patients. The role of serum 25-hydroxyvitamin D as an immunomodulator has been proven, and the absence of comorbid factors such as hypertension, diabetes, obesity, and smoking may protect against reinfection. This study aimed to evaluate the physical health parameters, hs-Troponin I, 25-Hydroxyvitamin D serum, and comorbid factors of Faculty of Medicine USK employees with confirmed COVID-19. A cross-sectional study was conducted at two sites, the Faculty of Medicine USK and the dr. Zainoel Abidin Hospital Banda Aceh, to assess serum 25-hydroxyvitamin D levels, hs-Troponin I levels, and heart muscle fitness of positive participants. COVID-19 respondents with confirmatory PCR swab > 1 month, aged 20–45 years, mild symptoms, and a history of coronary heart disease or lower limb disability were not included in this study. A total of 30 USK Medical Faculty employees were included in this study. The research was carried out with permission from the Faculty of Medicine USK Health Research Ethics Committee. Blood samples for serum 25-hydroxyvitamin D and hs-troponin I were tested five days before the cardiac fitness test. Cardiac fitness checks carried out at the Cardiac Rehabilitation Unit of RSUDZA hospital were: 6-minute walk test (6MWT) and Heart Rate Variability. The mean value of 25-Hydroxyvitamin D serum was 18.4 ng/ml (insufficiency), and only two employees (0.06%) had 25-Hydroxyvitamin D serum levels within normal limits. There were 28 (93%) employees with hs-Troponin I values of 3.2 pg/ml, and two with abnormal values of 13.7 pg/ml and 30.6 pg/ml, respectively. There were four employees (13.3%) who had comorbid diabetes, hypertension, and obesity. Based on Heart Rate Variability results, 25 employees (83.3%) with heart signal problems require further monitoring. The results also revealed that 11 (36.7%) employees had limited 6-minute walk test (6MWT) ability. This study has not obtained satisfying results because the cardiovascular fitness variable still needs further analysis. Employees confirmed positive for COVID-19 had low 25-Hydroxyvitamin D serum levels and hs-Troponin I was within the normal range. Less than half of the respondents had limited 6-minute walk test (6MWT) ability, and the cardiac fitness scores still needed further analysis. [ABSTRACT FROM AUTHOR]

The concept of the Internet of things encompasses the usage of electronic devices that observe that the data and correlate the data to a cloud (private or public), forward them automatically to the cloud. The IoT also helps the patient care at home. In addition, the internet of things (IoT) has been applied to various available medical resources and provide the smart healthcare to the patients those who are unable to go to the hospital and meet to the doctor. By collecting heart rate, blood pressure, and temperature data through sensors, processing data with a microcontroller, and thereafter storing the data in the cloud, the proposed study demonstrates IoT-based health monitoring. After that the doctor can access the heart rate of the patient and temperature values through the webpage and after the message is sent to the patient or the caretaker. Arduino, UNO, Raspberry Pi3 and think speak cloud is used to implement this system. [ABSTRACT FROM AUTHOR]

In modern times, enemy threat plays a vital role in the security policies of any nation. In this expectation, the military played a vital and important role. There are several considerations regarding the safety of those soldiers in the Line of Control (LOC). In our paper a health monitoring system based on IoT technology and military tracking is being developed to monitor the health status of soldiers and warn them in case they cross the border unintentionally. The proposed system contains a military module that can be inserted into a soldier's body to track his or her current state of health and location using GPS and HUB module. The Military Module contains small wearable sensors such as heart rate and temperature sensor and transmission modules. The Communication Protocol used is LoRa WAN, which can be used for an active high-speed transmission range. The harp can be established every 15 miles through the Control line. This hub contains LoRa WAN and Raspberry pi that act as a gateway. The boundaries of health and military status are determined and transferred to the harp. This information is then transmitted to the base station via IoT. Therefore, with the use of the proposed equipment, it is possible to use an accurate system to monitor the health conditions of soldiers from remote areas. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent monitoring system was proposed to follow vital parameters such as the electrocardiogram (ECG), oxygen saturation (SPO2), the temperature of the patient, and also heart rate. The system is built around a Raspberry 3B+ and an Arduino Uno. The prototype is equipped with an intelligent system that can currently detect sleep apnea from ECG signals. These parameters are detected by the following sensors: AD8232, and MAX 30102. We have implemented and compared three algorithms: Perceproron multi-layer, Support Vector Machine, and a Random Forest Classifier. [ABSTRACT FROM AUTHOR]

The study efforts were made to find out the impact of physical activities and yogic practices on resting heart rate(RHR) among women diabetes patients. To achieve this purpose, the subjects (n=30) were chosen at random with thirty Type 2 women diabetic patients. The subjects' age were ranged from 40 to 50 years. The subjects allotted into 3 even groups with ten subjects each (n=10). Group I underwent physical activities (PG), group II underwent yogic exercises (YG) during a period of twelve weeks, five days every week and group III acted as control (CG) they did not exposed any special training programme. The independent variable was resting heart rate, which was measured during the initial and final training sessions. Analysis of Covariance (ANCOVA) was used to statistically analyze the acquired data and Scheffe's test was applied as post hoc test to determine the paired mean difference. According to the findings of the study, there was a considerable decrease (p≤0.05) in resting heart rate of both physical activities group and yogic exercise group while related to control group. [ABSTRACT FROM AUTHOR]