Real-time Cognitive Load Measurement Using Wearable Photoplethysmogram Sensor

Working memory tasks, such as n-back and arithmetic tasks, are frequently used in studying mental workload. The present study investigated and compared the sensitivity of several physiological measures at three levels of difficulty of n-back and arithmetic tasks. The results showed significant differences in fixation duration and pupil diameter among three task difficulty levels for both n-back and arithmetic tasks. Pupil diameters increase with increasing mental workload, whereas fixation duration decreases. Blink duration and heart rate (HR) were significantly increased as task difficulty increased in the n-back task, while root mean square of successive differences (RMSSD) and standard deviation of R-R intervals (SDNN) were significantly decreased in the arithmetic task. On the other hand, blink rate and Galvanic Skin Response (GSR) were not sensitive enough to assess the differences in task difficulty for both tasks. All significant physiological measures yielded significant differences between low and high task difficulty except for SDNN. Practitioner summary: This study aimed to assess the sensitivity levels of several physiological measures of mental workload in n-back and arithmetic tasks. It showed that pupil diameter was the most sensitive in both tasks. This study also found that most physiological indices are sensitive to an extreme change in task difficulty levels.

At present, the living habits of college students are relatively poor, and the amount of exercise is reduced, leading to their physical fitness getting worse and worse. Therefore, people began to study the physical health monitoring of college students. Machine learning and high‐performance computing in medical applications provide technical support for intelligent medical technology. With the rapid development of computer network, human beings have entered the information and digital era, and sports health big data has become more and more popular. The combination of sports health big data and student physical health monitoring technology, in a sense, can realize automatic data processing through intelligent medical and sports health database and other information technologies, thus promoting the popularization of health monitoring technology. However, the current health monitoring equipment has many problems, such as complex collection, low accuracy and limited processing of health data. To solve this problem, this paper developed an Application (APP) based on sports health big data technology that can monitor multiple vital signs such as human heart rate and body temperature, and analyze the physical health of college students, so that college students can easily understand their health status in daily life, so as to promote the healthy development of students' physique and encourage them to actively participate in physical exercise. The experiment proved that, in the heart rate monitoring, when the speed is 6 km/h, the error rate of the college students' physical health monitoring APP designed in this paper is 8.15%. The average accuracy rate of student steps monitoring is 97.12%. This showed the accuracy and availability of the APP's monitoring function for human vital signs. It has certain application value and significance to help students improve their physical quality.

Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.

In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

Assessing mental workload with wearable devices – Reliability and applicability of heart rate and motion measurements

Wearable health monitoring devices are becoming increasingly ubiquitous in clinical settings and even in monitoring daily activities. This recent spurt in wearable devices has been made possible through the development of low power electronics, small footprint components and efficient data transmission methods. The next big step in making monitoring devices more 'wearable' is the elimination of batteries. Without the need to replace and recharge batteries, monitoring can be uninterrupted and the monitoring device itself can be seamlessly integrated into garments. However, to achieve this goal, merely reducing sensor power consumption is not enough. There is a need for unconventional methods of health monitoring. par In this work, a novel passive Radio Frequency Identification (RFID) based method for transmitting health parameters wirelessly and without batteries is described. The dissertation proposes an innovative method of transmitting health parameter data by simply turning RFID tags on and off. Technology for RFID based continuous monitoring that include a wireless power harvester and low-power circuits for amplification and health parameter detection are developed in this research. The dissertation includes practical applications of the technology that are demonstrated using heart rate and uterine contraction monitoring as examples. Empirical tests for characterizing the heart rate monitoring system are also conducted. The heart rate monitoring technology is validated with human testing which showed a correlation of over 99% between actual and detected heart rate data.

Teknolojinin her alanda gösterdiği gelişim, spor ekipmanları konusunda da önemli mal ve hizmet çeşitliliğini doğurmaktadır. Bunlardan biri olan nabız monitörleri aktif spor yapan bireylerin egzersiz kalitesini etkilemektedir. Bu ise, söz konusu mal ve hizmetlere dair geliştirilen davranışsal niyetlere etki etmektedir. Nabız monitörleri spor yapan bireylerin antrenmanın yeri, antrenman süresi, antrenman mesafesi, kalori tüketimi gibi birçok aktivite göstergesini yakalamasına imkân veren cihazlardır. Birleştirilmiş teknoloji kabul ve kullanım teorisi 2 üzerinden yapılan bu araştırma ile nabız monitörlerinin spora dair katkıları ve kullanıcıların davranışsal niyetleri incelenmiştir. Tarama araştırmalarının veri toplama metodu olan anket aracılığıyla yürütülen çalışma sonucunda katılımcıların cinsiyet, yaş, eğitim durumu ve spor alışkanlıklarına göre oluşturulan değişkenlerin davranışsal beklenti temelindeki niyetleri araştırılmıştır. Bu niyetler performans beklentisi, çaba beklentisi, sosyal etki, kolaylaştırıcı koşullar, ücret değeri ve hedonik motivasyon olarak belirlenmiştir. Araştırmanın sonucunda kullanıcı deneyimine işaret eden kolaylaştırıcı koşulların önemli bir değişken olduğu bulgulanırken hedonik motivasyon ve ücret değerinin de davranışsal beklenti niyetinin önemli yordayıcıları olduğu gözlemlenmiştir.

The association between physiological and eye-tracking metrics and cognitive load in drivers: A meta-analysis

Monitoring of Physical and Mental Health in Secondary Care Services Following Initiation of Antipsychotic Medications.

Heart rate monitoring has become a pivotal tool in both medical and fitness domains, providing invaluable data to monitor cardiovascular health, fitness levels, and overall well-being. The heart rate, which is the number of heartbeats per minute, can offer insight into an individual’s physical state, stress levels, and potential health risks. Traditionally, heart rate measurement was restricted to clinical settings using equipment like electrocardiograms (ECGs). However, with the evolution of technology, heart rate monitors have transitioned into portable and non-invasive devices, enabling continuous and real-time monitoring outside of medical environments. Modern heart rate monitors use a variety of technologies, including optical sensors, electrocardiography (ECG), and impedance cardiography, to measure the electrical signals or blood flow associated with heartbeats. Optical sensors, often found in wearable devices like smart watches or fitness trackers, utilize light-based technology to detect changes in blood volume and derive heart rate information. In contrast, ECG-based monitors directly measure the electrical impulses that stimulate the heart muscles, providing highly accurate data that is often used for clinical diagnostics. The integration of heart rate monitors with mobile applications and cloud-based platforms has further enhanced their utility, offering users not only real-time data but also long-term trends, analytics, and recommendations for health optimization. Athletes and fitness enthusiasts use heart rate monitors to track workout intensity, recovery, and overall cardiovascular performance. In healthcare, these devices have become integral tools for managing chronic conditions such as hypertension, arrhythmia, and heart failure. Remote monitoring technologies, enabled by heart rate sensors, allow healthcare professionals to track patients' conditions from afar, reducing the need for frequent hospital visits and improving patient outcomes. In addition to physical health monitoring, heart rate monitors are also being explored for mental health applications. Since heart rate can fluctuate with stress, anxiety, and emotional responses, these devices have potential for use in psychological assessments and interventions. Furthermore, heart rate variability (HRV), a measure of the variation in time between heartbeats, has been linked to autonomic nervous system function and can provide insights into an individual’s resilience to stress and their overall recovery state.

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

To explore nurses' views of their role in the screening and monitoring of the physical care needs of people with serious mental illness in a mental health service provider. There is increasing awareness through research that people with serious mental illness disproportionately experience and die early from physical health conditions. Mental health nurses are best placed as front-line workers to offer screening, monitoring and interventions; however, their views on physical care interventions are not studied often. Qualitative exploratory study. The study was carried out in a mental health inpatient centre in England. Volunteer sampling was adopted for the study with a total target sample of (n=20) nurses from three inpatient wards. Semistructured interviews were conducted with (n=10) registered mental health nurses who had consented to take part in the study. Inductive data analysis and theme development were guided by a thematic analytic framework. Participants shared a clear commitment regarding their role regarding physical health screening and monitoring in mental health settings. Four themes emerged as follows: features of current practice and physical health monitoring; perceived barriers to physical health monitoring; education and training needs; and strategies to improve physical health monitoring. Nurses were unequivocal in their resolve to ensure good standard physical health monitoring and screening interventions in practice. However, identified obstacles have to be addressed to ensure that physical health screening and monitoring is integrated adequately in everyday clinical activities. Achieving this would require improvements in nurses' training, and an integrated multiservice and team-working approach. Attending to the physical health needs of people with serious mental illness has been associated with multiple improvements in both mental and physical health; nurses have a vital role to play in identifying and addressing causes of poor physical health to improve physical health outcomes in people with serious mental illness.

Over the past decades, wearable and implantable devices have demonstrated great potential for a wide range of personalized health monitoring and therapeutic applications. This special issue primarily focuses on functional and electronic materials, sensors technologies and capabilities, and the associated energy solutions for wearable and implantable devices toward healthcare applications. We have collected 17 reviews, four research articles, and one perspective, all of which are within the scope of this area and cover the topics in breadth and depth.

It is important to evaluate mental work load on drivers will be useful for managing operator’s work load or for preventing from overloaded tasks. However, there does not exist any quantitative method for evaluating mental work load in real time. This motivates the study of proposing a new method to evaluate the influence of mental work load caused by information processing demand. Our method focuses on involuntary eye movement of human, which is vestibulo-ocular reflex (VOR). The eye movement occurs reflexively for gaze stabilization while paying attention to the target. We have investigated the influence of mental work load on VOR using a new model-based method. The first step of the method is to identify the eye movement model for a particular subject from measured data without any secondary task. This model represents the subject’s dynamics of VOR. After that the eye movement is measured when the subject get distracted by paying attention to secondary tasks, and it is compared with the identified model output. This method makes it possible to assay the influence of mental work load on VOR. This study has investigated the influence of mental work load on human eye movement by giving n-back tasks as the secondary task. By varying the amount of information processing demand of n-back tasks, we compare the variations of the dynamics of VOR from the identified model which represents human VOR dynamics in ideal situation. Consequently, we give a proof of quantitatively evaluating mental work load using our proposed model-based method.

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.