Driver-centered pervasive application for heart rate measurement

People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology

Awareness and Readiness of Malaysian University Students for Emotion Recognition System

Emotion Recognition System (ERS) identifies human emotion like happiness, sadness, anger, disgust and fear. These emotions can be detected via various modalities such as facial expression analysis, voice intonation, and physiological signals like the brain’s electroencephalogram (EEG) and heart’s electrocardiogram (ECG).  The emotion recognition system allows machines to recognized human emotions and reacts to it. It offers broad areas of application, from smart home automation to entertainment recommendation system to driving assistance and to automated security system. It is a promising and interesting field to be explored especially as we are moving towards industrial revolution 5.0. Therefore, a survey was conducted on the awareness and readiness of the usage of emotion recognition system among Malaysian youths, specifically among university students. The findings are presented here. Overall, positive orientation towards the technology is observed among the participants and they are ready for its adoptio

ECG datasets of young healthy adults

<p>This dataset is acquired using ECG AD8232 sensor.</p&gt

A comparison of emotion recognition system using electrocardiogram (ECG) and photoplethysmogram (PPG)

Electrocardiogram (ECG) and Photoplethysmogram (PPG) are derived from electrical signals of the heart activities and frequently used to diagnose and monitor cardiovascular disease. In the field of affective computing, these two signals can be used to recognize human emotions, this is supported by the wide availability of wearable devices that able to collect ECG or PPG in the market. ECG is frequently used as a unimodal signal for ERS, but the usage of PPG signals as unimodal ERS is still limited. There is no consensus about whether ECG is more suitable than PPG in ERS or vice versa. Only a few research have compared ECG and PPG. Therefore, this work intends to close this gap by developing an ERS employing ECG and PPG and evaluating the efficacy of both signals in ERS. This is done through data collected from 47 participants and two public datasets. The result from the data collected indicates that ECG is superior at recognizing arousal emotion with accuracy up to 68.75%, whereas PPG superior at recognizing valence up to 64.94% and dimension classes with 37.01% accuracy. The findings suggest that despite the current trend where researchers favour ECG over PPG, the PPG signals can be used as the only modality in developing ERS with results comparable to those obtained using ECG signals

Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models

Predicting the onset of cardiovascular disease (CVD) has been a hot topic for researchers for years, and recently, the concept of transfer learning has been gaining traction in this field.Transfer learning (TL) is a process that involves transferring information gained from one task or domain to another related task or domain.This paper comprehensively reviews recent advancements in pre-trained TL models for CVD, focusing on electrocardiogram (ECG) signals.Forty-three articles were chosen from Scopus andGoogle Scholar sources and reviewed, focusing on the type of CVD detected, the database used, the ECG input format, and the pre-training model used for transfer learning.The results show that more than 80% of the studies utilize 2-dimensional (2D) ECG input from the two most utilized available ECG datasets: MIT-BIH arrhythmia (ARR) and MIT-BIH normal sinus rhythm.alexnet, visual geometry group (VGG), and residual network (ResNet) are among the pre-trained TL models with the highest number used among reviewed articles.Additionally, the development of pre-trained TL models over time has made it possible to detect CVD with ECG signals.It can also address limited data problems, promote the development of more dependable and resilient detection systems, and aid medical professionals in diagnosing CVD and other diseases

A systematic review of emotion recognition using cardio-based signals

There is a growing demand for emotion recognition systems (ERS) to be adopted in everyday life from various fields, particularly automotive, education, and social security. Recently, the use of cardio-based physiological signals, electrocardiogram (ECG), and photoplethysmogram (PPG) in ERS has yielded promising results. Furthermore, the development of wearable devices equipped with cardio-based physiological sensors has significantly aided towards the adoption of ERS in daily life. This paper systematically reviews emotion recognition using cardio-based physiological signals, encompassing emotion models, emotion elicitation methods, and ERS development methods, emphasizing feature extraction, feature selection methods, feature dimension reduction methods, and classifiers. A summary and comparison of recent studies are presented to highlight existing studies’ gaps and suggest future research for better ERS especially using cardio-based signals

Real-time monitoring tool for heart rate and oxygen saturation in young adults

Health monitoring is crucial to maintain optimal well-being, especially for young adults. Wearable sensors have become popular for collecting healthcare data, but there are concerns regarding their reliability and safety, particularly with wireless sensorsthat use radio-frequency (RF) based devices. Researchers have proposed real-time monitoring systems for measuring heart rate beats per minute (BPM) and blood oxygen saturation (SpO2) saturation levels, but more studies are needed to determine the accuracyand user acceptance of these tools among young adults. To address these concerns, this study proposes a real-time monitoring tool that incorporates MAX 30100 sensors to collect heart rate BPM and SpO2 data. The collected data is then connected to a visualization platform, i.e.,InfluxDB and Grafana, to provide valuable insights of the body’s physiological state. By testing the feasibility and usability of the tool, we found motivating differences in resting heart rates and changes in heart rate after activity between male and female participants. By developing this real-time monitoring tool and investigating gender-specific differences in heart rate and activity-induced changes, our study contributes to the advancement of health monitoring technologies for young adults, ultimately promoting personalized healthcare and well-bein

Selecting Video Stimuli for Emotion Elicitation via Online Survey

Video stimulus is commonly used to induce different emotional states. Numerous sets of stimulus materials were produced in recent years; however, sets that include Asian clips are still inadequate. This study identified and validated 24 videos expected to elicit specific emotional reactions in a two-dimensional model of valence and arousal. The videos consist of excerpts from movies, TV shows, and advertisements from various regions, including Asia. The study was conducted during the COVID-19 pandemic; therefore, instead of the traditional approach of physical sessions in the laboratory, online surveys were conducted to collect responses from 42 participants. The findings show that 79% of the videos successfully evoked the targeted emotions. The participants’ demographic factors, such as age, gender, race, nationality, and place of residence, were taken into account to explore and understand the different perspectives among the participants towards the videos. The outcomes disclosed that all selected videos are gender-neutral. The emotions elicited by several videos revealed significant differences among people of different races and nationalities. This finding indicates that the background and culture affected one’s perspective and, subsequently, the emotion

Machine Learning Insights into Basketball Championship Predictions: An Analytical Comparison

Leveraging machine learning techniques to forecast the eventual championship-winning teams through the consideration of diverse sport-related variables has garnered substantial attention in contemporary sports research. In this study, the focus resides in employing machine learning models to forecast National Basketball Association (NBA) champions. The techniques were applied to a selected NBA dataset originated from data.world. Central to this endeavor is the role of feature selection which proves pivotal not only in discerning NBA championships but also in gauging predictive capacities. Five machine learning models encompassing Decision Trees, Random Forests, Logistic Regression, Support Vector Machines (SVMs), and Autoencoders were employed to offer multifaceted insights into the projection of victorious teams and their performance trajectories. Each model exhibited varying degrees of predictive accuracy. The autoencoders model emerged as the pinnacle performer by attaining the highest accuracy levels of 0.888 and 0.810. The findings provided exciting insights into determining the NBA championship and demonstrating the roles of machine learning in predicting championships. This study demonstrates the potential application of machine learning across a wide range of sports arenas. It aspires to foster advancements in predictive analytics thereby enhancing the precision of such forecasts and their broad applicability

Monitoring Physiological State of Drivers Using In-Vehicle Sensing of Non-Invasive Signal

Eighty percent of traffic accidents are caused by human error, called hypo vigilance, stemming from drowsiness, stress, or distraction while driving. This poses a significant threat to road safety. An electrocardiogram (ECG) is often used to monitor drivers' health. Thus, enhancing vehicles with Internet of Things (IoT) sensors and local analytical databases becomes crucial for real-time detection and transmission of relevant health data to avoid things that compromise road traffic safety. This study introduces a cost-effective in-vehicle ECG sensing prototype using an AD8232 sensor integrated with an Arduino Uno and an AD8232 Wi-Fi module placed on the steering wheel to monitor the driver's heart signal while driving. Short-term heart rate variability (HRV)features were computed through Python from the acquired ECG data, and supervised machine learning techniques such as AdaBoost, Random Forest, Naïve Bayes, and Support Vector Machine (SVM) classified the features into normal and abnormal classes. Naive Bayes exhibited the highest accuracy (90.91%) and F1 score (85.71%), surpassing Random Forest's lower accuracy (63.64%) and F1 score (50.00%). These findings indicate the prototype's potential as a valuable tool for ensuring safe and efficient driving, proposing integration into standard vehicle safety systems for enhanced road traffic safety