Evaluation of chronic stress indicators in geriatric and oncologic caregivers: a cross-sectional study.

Caregiving induces chronic stress with physical and psychological impact on informal caregivers health. Therefore, subjective and objective indicators are needed for the early diagnosis of pathologic stress to prevent the risk of developing stress-related diseases in caregivers. Our aim was to assess the self-perceived stress, that is, how and how much the stressor affects the individual, through endocrine, metabolic, and immunologic biomarkers levels in geriatric and oncologic informal caregivers. Informal caregivers and non-caregivers were invited to participate in a cross-sectional study at the Clinic Hospital of Barcelona. Demographic and lifestyle characteristics, self-perceived stress (Perceived Stress Scale, State-Trait Anxiety Inventory and Stress Visual Analogue Scale), and biomarkers (copeptin, glucose, glycated hemoglobin, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), cholesterol, triglycerides, α-amylase, cortisol, tumor necrosis factor (TNF-α), and Interleukins (IL-6 and IL-10)) were evaluated. Descriptive and non-parametric statistical data analysis were performed. Fifty-six subjects (19 non-caregivers, 17 geriatric caregivers, and 20 oncologic caregivers) participated. Median age (IQR) was 57 years (47-66) and 71.46% were women. Self-perceived stress was higher in oncologic caregivers than geriatric caregivers in all psychometric test analyzed (Wilcoxon Rank Sum test, p value < .05). Glucose concentrations and glycated hemoglobin levels differed statistically among groups (Kruskal-Wallis test (K-W tests), p value < .05), even though the median levels were not clinically relevant. Levels of other biomarkers did not differ significantly (K-W tests, p value > .05). These findings suggest that perceived stress is not homogeneous in the caregivers community and thus these two groups could be differentiated. These results provide the baseline information to initiate social actions addressed to each group of caregivers to increase their wellbeing.

Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices.

OBJECTIVE: Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operator's cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. METHODS: Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life. RESULTS: We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for CWM classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life. CONCLUSION: We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge. SIGNIFICANCE: The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones.

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

Psychosomatic response to acute emotional stress in healthy students.

The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress.

ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm.

This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The proposed procedure relies only on the ICG signal compared to most algorithms found in the literature that rely on synchronous electrocardiogram signal (ECG) recordings. ReBeatICG was designed with implementation on an ultra-low-power microcontroller (MCU) in mind. The detection accuracy of the developed algorithm is tested against points manually labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%, 98.6%, 90.3%, and 84.3% for the B, C, X, and O characteristic points, respectively. Furthermore, several hemodynamic parameters were calculated based on annotated characteristic points and compared with values generated from the cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11 ms, 9.72 ms, 8.32 ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude, respectively.

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals.

Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.

MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection.

Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58 h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.

Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions.

High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observable. However, cognitive workload affects several physiological signals that can be measured non-invasively. The main goal of this work is to develop a reliable machine learning algorithm to identify the cognitive workload induced during rescue missions, which is evaluated through drone control simulation experiments. In addition, we aim to minimize the computing resources usage while maximizing the cognitive workload detection accuracy for a reliable real-time operation. We perform an experiment in which 24 subjects played a rescue mission simulator while respiration, electrocardiogram, photoplethysmogram, and skin temperature signals were measured. State-of-the-art feature-based machine learning algorithms are investigated for cognitive workload characterization using learning curves, data augmentation, and cross-validation techniques. The best classification algorithm is selected, optimized, and the most informative features are selected. Finally, the generalization power of the optimized model is evaluated on an unseen test set. We obtain an accuracy level of 86% on the new unseen datasets using the proposed and optimized eXtreme Gradient Boosting (XGB) algorithm. Then, we reduce the complexity of the machine learning model for future implementation on resource-constrained wearable embedded systems, by optimizing the model and selecting the 26 most important features. Overall, a generalizable and low-complexity machine learning model for cognitive workload detection based on physiological signals is presented for the first time in the literature.

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices.

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this paper, we evaluate the possibility of stress and emotion monitoring using off-the-shelf wearable sensors. To this aim, we propose a multi-modal machine-learning technique for acute stress episodes detection, by fusing the information careered in several biosignals and wearable sensors. Furthermore, we investigate the contribution of each wearable sensor in stress detection and demonstrate the possibility of acute stress recognition using wearable devices. In particular, we acquire the physiological signals using the Shimmer3 ECG Unit and the Empatica E4 wristband. Our experimental evaluation shows that it is possible to detect acute stress episodes with an accuracy of 84.13%, for an unseen test set, using multi-modal machinelearning and sensor-fusion techniques.

REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm.

Wearable devices are an unobtrusive, cost-effective means of continuous ambulatory monitoring of chronic cardiovascular diseases. However, on these resource-constrained systems, electrocardiogram (ECG) processing algorithms must consume minimal power and memory, yet robustly provide accurate physiological information. This work presents REWARD, the Relative-Energy-based WeArable R-Peak Detection algorithm, which is a novel ECG R-peak detection mechanism based on a nonlinear filtering method called Relative-Energy (Rel-En). REWARD is designed and optimized for real-time execution on wearable systems. Then, this novel algorithm is compared against three state-of-the-art real-time R-peak detection algorithms in terms of accuracy, memory footprint, and energy consumption. The Physionet QT and NST Databases were employed to evaluate the algorithms' accuracy and robustness to noise, respectively. Then, a 32-bit ARM Cortex-M3-based microcontroller was used to measure the energy usage, computational burden, and memory footprint of the four algorithms. REWARD consumed at least 63% less energy and 32% less RAM than the other algorithms while obtaining comparable accuracy results. Therefore, REWARD would be a suitable choice of R-peak detection mechanism for wearable devices that perform more complex ECG analysis, whose algorithms require additional energy and memory resources.

Measuring acute stress response through physiological signals: towards a quantitative assessment of stress.

Social and medical problems associated with stress are increasing globally and seriously affect mental health and well-being. However, an effective stress-level monitoring method is still not available. This paper presents a quantitative method for monitoring acute stress levels in healthy young people using biomarkers from physiological signals that can be unobtrusively monitored. Two states were induced to 40 volunteers, a basal state generated with a relaxation task and an acute stress state generated by applying a standard stress test that includes five different tasks. Standard psychological questionnaires and biochemical markers were utilized as ground truth of stress levels. A multivariable approach to comprehensively measure the physiological stress response is proposed using stress biomarkers derived from skin temperature, heart rate, and pulse wave signals. Acute physiological stress levels (total-range 0-100 au) were continuously estimated every 1 min showing medians of 29.06 au in the relaxation tasks, while rising from 34.58 to 47.55 au in the stress tasks. Moreover, using the proposed method, five statistically different stress levels induced by the performed tasks were also measured. Results obtained show that, in these experimental conditions, stress can be monitored from unobtrusive biomarkers. Thus, a more general stress monitoring method could be derived based on this approach. Graphical abstract Stress measurements of different healthy young people throughout a Stress Session that includes a pre-relax stage (BLs), memory test (ST and MT), stress anticipation time (SA), video display (VD) and arithmetic task.

Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment.

Respiratory rate and heart rate variability (HRV) are studied as stress markers in a database of young healthy volunteers subjected to acute emotional stress, induced by a modification of the Trier Social Stress Test. First, instantaneous frequency domain HRV parameters are computed using time-frequency analysis in the classical bands. Then, the respiratory rate is estimated and this information is included in HRV analysis in two ways: 1) redefining the high-frequency (HF) band to be centered at respiratory frequency; 2) excluding from the analysis those instants where respiratory frequency falls within the low-frequency (LF) band. Classical frequency domain HRV indices scarcely show statistical differences during stress. However, when including respiratory frequency information in HRV analysis, the normalized LF power as well as the LF/HF ratio significantly increase during stress ( p-value 0.05 according to the Wilcoxon test), revealing higher sympathetic dominance. The LF power increases during stress, only being significantly different in a stress anticipation stage, while the HF power decreases during stress, only being significantly different during the stress task demanding attention. Our results support that joint analysis of respiration and HRV obtains a more reliable characterization of autonomic nervous response to stress. In addition, the respiratory rate is observed to be higher and less stable during stress than during relax ( p-value 0.05 according to the Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 % ).

Aproximación a una escala de referencia de estrés emocional agudo / An approach to an acute emotional stress reference scale

Introducción. El diagnóstico clínico persigue identificar el grado de afectación del estado psicofísico del paciente como orientación hacia la intervención terapéutica. En el estrés, la falta de un instrumento de medición por comparación con una referencia dificulta la valoración cuantitativa del nivel de afectación. Objetivo. Definir y hacer una primera validación de un patrón de referencia para la medida del estrés emocional agudo a partir de marcadores identificados como indicadores del nivel. Sujetos y métodos. En general, las medidas más sólidas y aceptadas de estrés por la comunidad científica son los tests psicométricos y las variables bioquímicas. Cada uno de ellos responde probablemente a procesos distintos y complementarios de la reacción frente a un estímulo estresante. La referencia que se propone es una media ponderada de estos indicadores, asignándoles pesos relativos de acuerdo con un análisis de componentes principales. Resultados. Para una primera aproximación y verificación de coherencia de la referencia propuesta, se ha utilizado un estudio experimental con una muestra de 40 jóvenes sanos sometidos al estímulo estresante psicosocial del Trier Social Stress Test. La escala propuesta diferencia netamente entre los dos estados con distintos niveles de estrés inducido. Conclusiones. Aceptando la subjetividad de la definición, y a falta de una validación posterior con nuevos datos experimentales, el patrón propuesto diferencia entre un estado de relax y uno de estrés emocional generados con un estímulo estresante moderado, como es el Trier Social Stress Test. La escala es robusta, ya que variaciones en la composición porcentual repercuten ligeramente en la puntuación, pero no en la diferenciación válida entre estados (AU)

Introduction. The clinical diagnosis aims to identify the degree of affectation of the psycho-physical state of the patient as a guide to therapeutic intervention. In stress, the lack of a measurement tool based on a reference makes it difficult to quantitatively assess this degree of affectation. Aim. To defi ne and perform a primary assessment of a standard reference in order to measure acute emotional stress from the markers identified as indicators of the degree. Subjects and methods. Psychometric tests and biochemical variables are, in general, the most accepted stress measurements by the scientific community. Each one of them probably responds to different and complementary processes related to the reaction to a stress stimulus. The reference that is proposed is a weighted mean of these indicators by assigning them relative weights in accordance with a principal components analysis. Results. An experimental study was conducted on 40 healthy young people subjected to the psychosocial stress stimulus of the Trier Social Stress Test in order to perform a primary assessment and consistency check of the proposed reference. The proposed scale clearly diff erentiates between the induced relax and stress states. Conclusions. Accepting the subjectivity of the defi nition and the lack of a subsequent validation with new experimental data, the proposed standard diff erentiates between a relax state and an emotional stress state triggered by a moderate stress stimulus, as it is the Trier Social Stress Test. The scale is robust. Although the variations in the percentage composition slightly aff ect the score, but they do not aff ect the valid diff erentiation between states (AU)