A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare

Understanding Robot Gesture Perception in Children with Autism Spectrum Disorder during Human-Robot Interaction

Social robots are increasingly being used in therapeutic contexts, especially as a complement in the therapy of children with Autism Spectrum Disorder (ASD). Because of this, the aim of this study is to understand how children with ASD perceive and interpret the gestures made by the robot Pepper versus human instructor, which can also be influenced by verbal communication. This study analyzes the impact of both conditions (verbal and nonverbal communication) and types of gestures (conversational and emotional) on gesture recognition through the study of the accuracy rate and examines the physiological responses of children with the Empatica E4 device. The results reveal that verbal communication is more accessible to children with ASD and neurotypicals (NT), with emotional gestures being more interpretable than conversational gestures. The Pepper robot was found to generate lower responses of emotional arousal compared to the human instructor in both ASD and neurotypical children. This study highlights the potential of robots like Pepper to support the communication skills of children with ASD, especially in structured and predictable nonverbal gestures. However, the findings also point to challenges, such as the need for more reliable robotic communication methods, and highlight the importance of changing interventions tailored to individual needs.

Wearable Physiological Signals under Acute Stress and Exercise Conditions

In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research.

Understanding cardiac events in breast cancer (UCARE): pilot cardio-oncology assessment and surveillance pathway for breast cancer patients

PURPOSE

In Ireland, over 3000 patients are diagnosed with breast cancer annually, and 1 in 9 Irish women will be diagnosed with breast cancer in their lifetime. There is evidence that female breast cancer survivors are more likely to die of cardiovascular disease than their age-matched counterparts. Specific services for cancer patients suffering from cancer therapy related cardiovascular toxicity have led to a higher incidence of safe anti-cancer treatment completion. Such services are not widely available in our jurisdiction, and the purpose of this trial is to remedy this situation.

METHODS

This protocol describes a prospective, single arm, pilot feasibility study implementing a dedicated Cardio-Oncology assessment and surveillance pathway for patients receiving multimodal breast cancer treatment. It incorporates novel biomarker and radiomic surveillance and monitoring approaches for cancer-therapy related cardiac dysfunction into routine care for breast cancer patients undergoing adjuvant systemic chemotherapy.

RESULTS

Declaration of results will via peer reviewed academic journals, and communicated directly to key knowledge users both nationally and internationally. This engagement will be critical to enable to healthcare services and policy sector make informed decisions or valuable changes to clinical practice, expenditure and/or systems development to support specialized Cardio-Oncology clinical pathways. All data is to be made available upon request.

CONCLUSION

Dedicated cardio-oncology services have been recommended in recent literature to improve patient outcomes. Our protocol describes a feasibility study into the provision of such services for breast cancer.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Physiologic Fidelity as a Domain in Assessing Mixed Reality Trauma Simulation

INTRODUCTION

Mixed reality has been used in trauma and emergency medicine simulation for more than a decade. As mixed reality potential in trauma simulation continues to expand, so too does the need to validate it as a surrogate for real-life emergency scenarios. Validation of these simulations can occur by measuring fidelity, or the degree to which a computing system can reproduce real-world experiences. After performing a literature review, we determined that most fidelity assessments of trauma and emergency simulations focus on how the user subjectively experiences the simulation. Although subjective user assessment is an important component of determining fidelity, we pose an introductory three-part framework that may assess mixed reality trauma simulation more adequately.

MATERIALS AND METHODS

A literature review was conducted using Google Scholar, PubMed, and the Uniformed Services University PowerER search database. Relevant articles were assessed to identify how studies measured fidelity in trauma simulation. We then designed the three-part framework to aid researchers in assessing the fidelity of mixed reality trauma simulations.

RESULTS

The domains we determined to best assess mixed reality emergency simulation are as follows:1. Continue assessing fidelity via subjective user assessments. This allows the researcher to know how real the simulation looked and felt to the user based on their individual report.2. Determine whether the trauma simulation changes the medical decision-making capacity of the user. If the user's decision-making capacity changes with a stress-inducing trauma simulation versus a non-stress-inducing simulation, then the stress-inducing trauma environment would be approaching greater fidelity.3. Study the domain of our newly proposed concept: physiologic fidelity. We define physiologic fidelity as the degree to which the simulation elicits a measurable, autonomic response independent of observed emotion or perceived affect. Recreating objective autonomic arousal may be the best way to ensure a trauma simulation reaches fidelity.

CONCLUSION

We propose a methodology to assess mixed reality trauma simulation fidelity. Once fidelity is more fully known to the researcher and the simulation user, adjustments can be made to approach reality more closely. Improved simulators may enrich the preparedness of both junior and senior learners for real-life emergencies. We believe assessing the three domains using the Wide Area Virtual Experience at the Val G. Hemming simulation center in Bethesda, MD, will validate mixed reality-trauma simulators as invaluable surrogates for real-life emergency scenarios and ultimately contribute to improved clinical outcomes for clinicians and their patients.

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.

Stress monitoring using wearable sensors: IoT techniques in medical field

The concept "Internet of Things" (IoT), which facilitates communication between linked devices, is relatively new. It refers to the next generation of the Internet. IoT supports healthcare and is essential to numerous applications for tracking medical services. By examining the pattern of observed parameters, the type of the disease can be anticipated. For people with a range of diseases, health professionals and technicians have developed an excellent system that employs commonly utilized techniques like wearable technology, wireless channels, and other remote equipment to give low-cost healthcare monitoring. Whether put in living areas or worn on the body, network-related sensors gather detailed data to evaluate the patient's physical and mental health. The main objective of this study is to examine the current e-health monitoring system using integrated systems. Automatically providing patients with a prescription based on their status is the main goal of the e-health monitoring system. The doctor can keep an eye on the patient's health without having to communicate with them. The purpose of the study is to examine how IoT technologies are applied in the medical industry and how they help to raise the bar of healthcare delivered by healthcare institutions. The study will also include the uses of IoT in the medical area, the degree to which it is used to enhance conventional practices in various health fields, and the degree to which IoT may raise the standard of healthcare services. The main contributions in this paper are as follows: (1) importing signals from wearable devices, extracting signals from non-signals, performing peak enhancement; (2) processing and analyzing the incoming signals; (3) proposing a new stress monitoring algorithm (SMA) using wearable sensors; (4) comparing between various ML algorithms; (5) the proposed stress monitoring algorithm (SMA) is composed of four main phases: (a) data acquisition phase, (b) data and signal processing phase, (c) prediction phase, and (d) model performance evaluation phase; and (6) grid search is used to find the optimal values for hyperparameters of SVM (C and gamma). From the findings, it is shown that random forest is best suited for this classification, with decision tree and XGBoost following closely behind.

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review

INTRODUCTION

Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress.

OBJECTIVE

The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face.

METHODS

This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2].

RESULTS

A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability.

CONCLUSION

Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.

The Role of Major Biomarkers of Stress in Atrial Fibrillation: A Literature Review

Numerous studies have reported that physical or emotional stress can provoke atrial fibrillation (AF) or vice versa, which suggests a potential link between exposure to external stressors and AF. This review article sought to describe in detail the relationship between major stress biomarkers and the pathogenesis of AF and presents up-to-date knowledge on the role of physiological and psychological stress in AF patients. For this purpose, this review article contends that plasma cortisol is linked to a greater risk of AF. A previous study has investigated the association between increased copeptin levels and paroxysmal AF (PAF) in rheumatic mitral stenosis and reported that copeptin concentration was not independently associated with AF duration. Reduced levels of chromogranin were measured in patients with AF. Furthermore, the dynamic activity of antioxidant enzymes, including catalase as well as superoxide dismutase, was examined in PAF patients during a period of <48 h. Malondialdehyde activity, serum high-sensitivity C-reactive protein, and high mobility group box 1 protein concentrations were significantly greater in patients with persistent AF or PAF compared to controls. Pooled data from 13 studies confirmed a significant reduction in the risk of AF related to the administration of vasopressin. Other studies have revealed the mechanism of action of heat shock proteins (HSPs) in preventing AF and also discussed the therapeutic potential of HSP-inducing compounds in clinical AF. More research is required to detect other biomarkers of stress, which have not been reported in the pathogenesis of AF. Further studies are required to identify their mechanism of action and drugs to manage these biomarkers of stress in AF patients, which might help to reduce the prevalence of AF globally.

An Ex Vivo Study of Wireless Linkage Distance between Implantable LC Resonance Sensor and External Readout Coil

The wireless monitoring of key physiological parameters such as heart rate, respiratory rate, temperature, and pressure can aid in preventive healthcare, early diagnosis, and patient-tailored treatment. In wireless implantable sensors, the distance between the sensor and the reader device is prone to be influenced by the operating frequency, as well as by the medium between the sensor and the reader. This manuscript presents an ex vivo investigation of the wireless linkage between an implantable sensor and an external reader for medical applications. The sensor was designed and fabricated using a cost-effective and accessible fabrication process. The sensor is composed of a circular planar inductor (L) and a circular planar capacitor (C) to form an inductor-capacitor (LC) resonance tank circuit. The reader system comprises a readout coil and data acquisition instrumentation. To investigate the effect of biological medium on wireless linkage, the readout distance between the sensor and the readout coil was examined independently for porcine and ovine tissues. In the bench model, to mimic the bio-environment for the investigation, skin, muscle, and fat tissues were used. The relative magnitude of the reflection coefficient (S11) at the readout coil was used as a metric to benchmark wireless linkage. A readable linkage signal was observed on the readout coil when the sensor was held up to 2.5 cm under layers of skin, muscle, and fat tissue. To increase the remote readout distance of the LC sensor, the effect of the repeater coil was also investigated. The experimental results showed that the magnitude of the reflection coefficient signal was increased 3-3.5 times in the presence of the repeater coil, thereby increasing the signal-to-noise ratio of the detected signal. Therefore, the repeater coil between the sensor and the readout coil allows a larger sensing range for a variety of applications in implanted or sealed fields.

Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection

Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress.