Heart Rate Sensor Research Articles

From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability.

More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep ("orthosomnia"). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., "light sleep"). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, κ = 0.79), as well as the H10 (84.4%, κ = 0.76), and VS (84.2%, κ = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.

Dementia prediction using novel IOTM (Internet of Things in Medical) architecture framework

In the last decades the health care developments highly rise the level of ages of world population. This improvement was accompanied by increasing the diseases related with elder like Dementia, which Alzheimer’s disease represents the most common form. The present studies aim to design and implementation a medical system for improving the life of Alzheimer’s disease persons and ease the burden of their caregivers. AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patient’s future health, and recommend better treatments. AI goes beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. Diagnosis is about identifying disease. By building an algorithm we can diagnosis chest X-ray and determine whether it contains disease, another algorithm that will look at brain MRIs and identify the location of tumours in those brain MRIs health of the patients lab values and their demographics and use those to predict the risk of an event. A Smart IOT Interactive Assistance is a technological device that continuously monitors the stability of an Alzheimer’s patient, indicates their position on a map, automatically reminds them to take their prescriptions, and has a call button for any emergencies they could experience during the day. The system has two components, one of which the patient wears and the other of which is an IoT platform application utilized by the caregiver. The motion processing unit sensor, GPS, heart rate sensor with microcontrollers, and LCD display were used to construct the wearable device. An Internet of Things (IoT) platform supports this device, allowing the caregiver to communicate with the patient from any location.

LoRa and IoT-based Heart Rate and Temperature Monitoring Tool Using MQTT Protocol

Heart rate is one of the most important organs in the human body as it plays a role in the circulatory system. Body temperature is the result of the body's metabolic processes and is influenced by external and internal factors. In this research, we design a monitoring tool that uses LoRa and IoT technology to monitor heart rate and human body temperature. This tool aims to provide real-time health monitoring and can communicate remotely. MQTT allows efficient data transmission between connected devices in the network. Pulse heart rate sensor and temperature sensor MLX90614 The data obtained from these two sensors is sent through the LoRa network to the LoRa gateway. The LoRa gateway functions as a link between the monitoring device and the MQTT server. The gateway receives data from the monitoring device via the LoRa network and forwards it to the MQTT server via an internet connection. The MQTT server sends the data to an application that can be accessed via a smartphone. The test results show that the RSSI value of -72.5 dBm is obtained when the minimum distance is 5 metres. The lowest value for RSSI, which is -93.5 dBm, is obtained at the maximum testing distance of about 55 metres. For the results of the packet loss value is 0% occurs at a closer distance, but at a distance of 50m (5%) and at a distance of 55m (20%) indicates that the signal begins to weaken or experience interference at a greater distance

Heat illness detection with heart rate variability analysis and anomaly detection algorithm

Objective:Incidence of heat illness has been increasing dramatically due to the progression of global warming. Preventing severe heat illness, called heatstroke, is crucial because it can lead to long-term multiple organ damage, including the brain, and results in more than 600 deaths per year in the United States. It has been reported that heat stress affects heart rate variability (HRV), which is the fluctuations of the R-R interval (RRI) on an electrocardiogram (ECG). We propose a method for detecting symptoms of heat illness based on HRV analysis in order to prevent exacerbation of heat illness. Methods:In the proposed method, monitoring abnormal changes in HRV caused by heat stress is monitored. Multivariate statistical process control (MSPC), a commonly used anomaly detection method in machine learning, is adopted for training the heat illness detection method. To validate the proposed method, we recruited 103 healthy volunteers with risks of heat illness development: employees working in hot environments, athletes, and amateur marathon runners. Data collection was performed using our wearable heart rate sensor and smartphone app. Results:The result of applying the proposed method showed that a sensitivity of 75% (21 out of 28 cases) and a false-positive rate of 1.02 times per hour were achieved. Conclusion:The proposed heat illness detection method will be used in daily life because RRI data can be easily measured by a wearable sensor. Significance:The proposed method will contribute to receiving appropriate treatment for heat illness before exacerbation, which contributes to protecting people’s health.

Human Activity Recognition with Smartwatch Data by using Mahalanobis Distance-Based Outlier Detection and Ensemble Learning Methods

Recognition of human activities is part of smart healthcare applications. In this context, the detection of human actions with high accuracy has been a field that has been working for many years. With the increase in the usage of smart devices, smartphones and smartwatches have become the constant equipment of these studies thanks to their internal sensors. Sometimes abnormal data are included in data sets due to the way the data were collected and for reasons arising from the sensors. For this reason, it becomes important to detect outlier data. In this study, step counter and heart rate sensors were used in addition to an accelerometer and gyroscope in order to detect human activities. Afterward, the outliers were detected and cleared with a Mahalanobis distance-based approach. With the aim of achieving a better classification performance, machine learning methods were used by strengthening them with ensemble learning methods. The obtained results showed that step counter, heart rate sensors, and ensemble learning methods positively affect the success of the classification. In addition, it was found that the Mahalanobis distance-based outlier detection method increased the classification accuracy significantly.

Modular Open-Core System for Collection and Near Real-Time Processing of High-Resolution Data from Wearable Sensors

Wearable devices, such as smartwatches integrating heart rate and activity sensors, have the potential to transform health monitoring by enabling continuous, near real-time data collection and analytics. In this paper, we present a novel modular architecture for collecting and end-to-end processing of high-resolution signals from wearable sensors. The system obtains minimally processed data directly from the smartwatch and further processes and analyzes the data stream without transmitting it to the device vendor cloud. The standalone operation is made possible by a software stack that provides data cleaning, extraction of physiological metrics, and standardization of the metrics to enable person-to-person and rest-to-activity comparisons. To illustrate the operation of the system, we present examples of datasets from volunteers wearing Garmin Fenix smartwatches for several weeks in free-living conditions. As collected, the datasets contain time series of each interbeat interval and the respiration rate, blood oxygen saturation, and step count every 1 min. From the high-resolution datasets, we extract heart rate variability metrics, which are a source of information about the heart’s response to external stressors. These biomarkers can be used for the early detection of a range of diseases and the assessment of physical and mental performance of the individual. The data collection and analytics system has the potential to broaden the use of smartwatches in continuous near to real-time monitoring of health and well-being.

Traveler Empowerment through IoT-Based Wearable Health Monitoring Gloves for Accident Prevention

MAAK is a sort of protective glove worn by the rider that increases the safety of motorcycling. The protection of the rider is the main purpose of this glove. This may be done by utilizing cutting-edge characteristics including heart rate, vehicle-to vehicle distance, and vehicle speed. This make it not only smart glove but also a feature of a smart bike. TFT display show the output of the rider`s Heartrate and speed of the vehicle continuously. The output of the rider's heart rate and the vehicle's speed are continually shown on a TFT display . The vehicle's speed is determined by an IR (infrared) sensor, the distance between the cars is determined by an ultrasonic sensor, and the rider's heart rate is continually monitored by a heart rate sensor. Keywords: Heart rate monitoring, Infrared sensor, Ultrasonic sensor, Wearable devices.

Effectiveness of an Occupational Well-being Intervention Among Nurse Educators: A Quasi-Experimental Study.

Occupational well-being supports the retention of the nurse educator workforce and their ability to manage workload. There is a research gap regarding interventions promoting occupational well-being. To evaluate the effectiveness of an 8-workweek digital occupational well-being intervention using self-conducted exercises among nurse educators in secondary vocational nursing schools in Finland. A quasi-experimental study design was applied using an intervention group (n = 37) and a control group (n = 40). Data were collected at baseline, post, and 1-month follow-up using a questionnaire and a heart rate sensor to assess the resource-workload-balance and its associating and promoting factors. This study found no statistical effects on the resource-workload-balance. Positive effects were found on associating factors (general well-being) and promoting factors (recovery experiences and self-regulation). Self-Help INtervention for Educators (SHINE) has the potential to promote recovery experiences during working hours; however, the intervention needs more investigation.

High-Performance Flexible Graphene Oxide-Based Moisture-Enabled Nanogenerator via Multilayer Heterojunction Engineering and Power Management System.

Recently, there has been a surge of interest in nanogenerators within the scientific community because their immense potential for extracting energy from the surrounding environment. A promising approach involves utilizing ambient moisture as an energy source for portable devices. In this study, moisture-enabled nanogenerators (MENGs) are devised by integrating heterojunctions of graphene oxide (GO) and reduced graphene oxide (rGO). Benefiting from the unique structure, a larger ion concentration gradient is achieved as well as a lower resistance, which leads to enhanced electricity generation. The resulting MENG generates a desirable open-circuit voltage of 0.76V and a short-circuit current density of 73 µAcm-2 with a maximum power density of 15.8 µWcm-2. Notably, the designed device exhibits a high voltage retention of more than 90% after 3000 bending cycles, suggesting a high potential for flexible applications. Moreover, a large-scale integrated MENG array is developed by incorporating flexible printed circuit technology and connecting it to a power management system. This integrated system can provide ample energy to operate an electronic ink display and drive a heart rate sensor for health monitoring. The outcomes of this research present a novel framework for advancing next-generation self-powered flexible devices, thereby demonstrating significant promise for future wearable electronics.

Feasibility of biomarkers to measure stress, burnout and fatigue in emergency nurses: a cross-sectional study

IntroductionRetaining nurses in the workforce is an urgent concern in healthcare. Emergency nurses report high levels of stress and burnout, however, there is no gold standard of how to measure...

Pelatihan pembuatan sensor medis berbasi IoT sebagai pengenalan smart medical devices

Cardiovascular disease (CVD) is a leading cause of death globally, resulting in approximately 17.9 million deaths each year (WHO, 2017), with estimates projecting a rise to 23.3 million deaths by 2030 (Pusdatin Kemenkes RI, 2014). Early detection of heart disease plays a crucial role in CVD prevention, with heart rate (bpm) being a key indicator to assess heart function, ranging from 60 to 100 beats per minute. To address the need for early detection, a practical heart rate monitoring device utilizing the Internet of Things (IoT) and Smart Medical Devices (SMDs) was developed. This research aimed to provide training on IoT-based heart rate detection to high school students in Trenggalek. The training encompassed lectures and hands-on practice, successfully enhancing participants' knowledge of IoT, as demonstrated by improved test scores. Moreover, the training resulted in a prototype of an IoT-based heart rate monitoring system that utilizes Arduino and a heart rate sensor. Post-training evaluations showed the majority of participants were satisfied with the quality of materials and organization, indicating the positive impact of this engagement on the partners. The results support the potential of this IoT training to equip high school students with essential skills, fostering self-reliance in medical device production and reducing dependence on imports in the face of ASEAN Economic Community challenges. Ultimately, this initiative contributes to building a competent healthcare workforce in Indonesia

Need of Li-Fi (light fidelity) technology for the world to track COVID-19 patients

In this modern world, a single day without light or the internet is unimaginable. Nowadays, wireless fidelity, often known as Wi-Fi, is the most well-known and commonly utilized conventional wireless technology. Wi-Fi employs radio waves or electromagnetic waves to carry data across networks. Imagine if a basic LED light in and around the hospital could link us to high-speed wireless internet with just a simple flickering of light at a very high speed where eyes cannot detect it. This technology is known as Li-Fi, or light fidelity, and it is 10,000 times faster than Wi-Fi. Hospitals are among the locations where Wi-Fi is absolutely forbidden. As doctors are the frontline soldiers against COVID-19, the objective of this project is to develop smart healthcare systems that use green communications to monitor COVID-19 patients using temperature, pressure, and heart rate sensors from Li-Fi transmitter to Li-Fi receiver by using simple LED light as a medium to transmit the data or information of COVID-19 to the cloud by using Li-Fi Dongle.

IoT-Based Instrumented Bicycle Handlebar for Children with Autism Spectrum Disorder

Common symptoms detected by children with autism spectrum disorder (ASD) include having problems with their fine and gross motor skills. One of the parameters usually associated with the development of ASD children's motor skill performance is Handgrip Strength (HGS). In healthcare, data gathering for the purpose of rehabilitation and therapy are usually conducted manually by the trainer or therapist for multiple candidates in a continuous intervention plan over days, weeks, or even months. This traditional and restrictive approach might not result in prompt, effective, and accessible health services or care that is centred on the needs of the patient. This creates a gap in the adoption of digital transformation-based solutions, particularly in giving the benefit of having a therapist, trainer, or guardian who can track the progress of treatment interventions online via a web application. Hence, this study aims to develop a bicycle handlebar with monitoring systems technology such as an IoT module, hand grip sensor, and heart rate to be used as an intervention tool for ASD children during the physical therapy session. The bicycle handlebar is customized and designed in SolidWorks software. It is 3D Printed to integrate the usage of sensors such as load cells and heart rate sensors to track the motor skill condition of ASD children. The handlebar is designed at a diameter of approximately 30 mm to enable an optimum reading of children's handgrip strength. The data logging system uses the TTGO T-Display with ESP32 microcontroller to allow a wireless and seamless data reading and transfer. Data from the sensors are sent via Wi-Fi to a custom website, Smart Fi-cycle IoT Website, allowing users to access through devices such as mobile phones and PC/Laptop. This system will enable patients and interventionists to track their motor skill performance through a systematic monitoring system.

On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild.

Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs. The framework offered here is illustrated using several brands of commercially available heart rate sensors. Measurement reliability varied across sensors and, more importantly, across the situations tested, and was highest during sleep. Our hope is that by systematically quantifying measurement reliability, researchers will be able to make informed choices about specific wearable devices and measurement procedures that meet their research goals.

System for Detecting Learner Stuck in Programming Learning

Getting stuck is an inevitable part of learning programming. Long-term stuck decreases the learner’s motivation and learning efficiency. The current approach to supporting learning in lectures involves teachers finding students who are getting stuck, reviewing their source code, and solving the problems. However, it is difficult for teachers to grasp every learner’s stuck situation and to distinguish stuck or deep thinking only by their source code. Teachers should advise learners only when there is no progress and they are psychologically stuck. This paper proposes a method for detecting when learners get stuck during programming by using multi-modal data, considering both their source code and psychological state measured by a heart rate sensor. The evaluation results of the proposed method show that it can detect more stuck situations than the method that uses only a single indicator. Furthermore, we implemented a system that aggregates the stuck situation detected by the proposed method and presents them to a teacher. In evaluations during the actual programming lecture, participants rated the notification timing of application as suitable and commented that the application was useful. The questionnaire survey showed that the application can detect situations where learners cannot find solutions to exercise problems or express them in programming.

Smart Scarf: An IOT-based Solution for Emotion Recognition

This paper presents the design rationale of a color-changing scarf depending on the emotional state it detects. The goal of the design is to associate people to show up their emotional state especially those who have difficulties in expressing their feelings, such as elderly people, children, people with special needs, and people who have to wear a face mask, which covers their facial expression, as it is common during the COVID-19 pandemic period. Wearing a smart emotional scarf provides a context of the individual's emotional state that might help surrounding people in dealing with them. Our design uses a heart rate sensor and a skin sensor to detect and recognize emotional information. The scarf will change its color based on the emotional state it detects (neutral, angry, happy, and sad). An interface allows the provision of emotional data and displays emotional statistics and daily history. We went through a user-centered design process adopting wearable technology guidelines. A proposed prototype of the smart scarf is presented called Scarfy. A user evaluation is conducted showing that wearing the scarf with changing colors based on emotional status is a good solution to express feelings with comfort and wearing it might enhance social engagement.

Energy management for wearable medical devices based on gaining–sharing knowledge algorithm

Wearable devices are a growing field of research that can have a wide range of applications. The energy harvester is the most common source of power for wearable devices as well as in wireless sensor networks that require a battery-free operation. However, their power is restricted; consequently, power saving is crucial for wearable devices. Finding the best schedule for the various tasks that run on the wearable device can help to reduce power consumption. This paper presents a task scheduler for wearable medical devices based on Gaining–Sharing Knowledge (GSK) algorithm. The purpose of this task scheduler is to handle the tasks of a heart rate sensor and a temperature sensor to optimize the energy consumption throughout wearable medical devices. The proposed GSK-based scheduling algorithm is assessed against the state-of-the-art technique. The data used in our experiments are collected from an in-lab prototype.

Heart Attack Detection and Heart Rate Monitoring System Using IOT

The system proposed in this project uses an IoT-based ESP32 Node MCU and a heart rate sensor to detect heart attacks and monitor heart rate. The device has the ability to measure the user's heart rate in real-time and spot irregularities that might be signs of a possible heart attack. The proposed system also has the ability to upload the collected data to the cloud for additional processing and archiving. A Max 10300 heart rate sensor, an ESP32 Node MCU, and a microcontroller to process and analyse the gathered data make up the system. Wi-Fi connectivity is used by the system to send data to the cloud, which can both the patients' own and the healthcare professionals' access. The suggested system has a number of benefits, including continuous monitoring, real-time data processing, and automatic heart attack detection, all of which have the potential to save lives. A noteworthy development in the healthcare industry is the heart attack diagnosis and heart rate monitoring system employing the Internet of Things-based ESP32 Node MCU and Max 10300 heart rate sensor. For people who want to track their heart health in real-time and look for any potential irregularities, it provides a practical and affordable alternative. Healthcare professionals might also utilize the technology to remotely check the heart health of their patients and take emergency action

IOT Platform for Vital Signs Detection Using Node Microcontroller

It is possibly possible to stop the spread of the coronavirus by utilizing IoT technology to monitor the vital signs of patients. As a consequence, a proposal has been developed for an efficient and affordable remote monitoring system that measures vital signs. This system makes use of a temperature sensor, a dual heart rate and oxygen level sensor, a temperature sensor, two microcontrollers, a UNO as a power supply, and a Node as the main controller. The vital signs are monitored in a non-invasive manner using photoplethysmography technique, and the information is transmitted wirelessly to the individual involved via the Blynk platform, making use of a Wi-Fi device. It also demonstrates how the system can connect to the internet anywhere in the world, which enables its utilization in a variety of clinical trials.

Customized evaluation of special endurance and functionality for optimal health and performance in judo athletes

Background and Study Aim. Achieving optimal health and performance in judo athletes requires a customized approach to training and evaluation. This study aimed to assess the level of special endurance and functionality of experienced judo athletes for optimal health and performance. Material and Methods. Thirteen experienced judo athletes were tested using a dummy throw with a deflection for 4 minutes, with the number of throws and heart rate dynamics assessed during and after the exercise. Heart rate indicators were recorded using a heart rate sensor and a specialized computer program. Results. The athletes completed the maximum number of throws in the first minute, followed by stabilization. Heart rate gradually increased during the test, reaching a maximum of 184 beats per minute at the end of the test. These findings suggest that the dummy throw test can be used to assess the level of special endurance in judo athletes, while heart rate indicators can be used to characterize their current functional state. Conclusions. A customized evaluation of special endurance and functionality provides valuable insights for optimizing health and performance in judo athletes. By tailoring training programs to individual needs, coaches and trainers can help judo athletes achieve their full potential while minimizing the risk of injury and burnout. Overall, this study highlights the importance of a personalized approach to training and evaluation for maintaining optimal health and performance in judo athletes.