Signal processing and machine learning algorithms for stress monitoring using wearable sensor technologies

Iqbal, Talha
In recent years, there has been a notable increase in depression, anxiety, pathological stress, and other stress-related diseases. Stress is a known contributor to several life-threatening medical conditions and triggers acute cardiovascular events, as well as one of the root causes of several social problems. According to the statistics from the World Health Organization, stress is associated with several medical and social problems, and these problems are seriously affecting the health and well-being of not only adults but also children and youngsters. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. Therefore, a vast interest has been developed to investigate the underlying mechanisms of stress and monitor various biophysiological and biochemical responses of the body to stress. The review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies shows that prolonged exposure to stress triggers the adrenocorticotrophic hormonal (ACTH) system and causes the release of cortisol hormones from the adrenal cortex that boosts the alertness of the body. As a result, there is an increase in blood supply to muscles, heart rate, respiratory rate, and cognitive activity, along with several other responses. The variable and contradictory evidence in the literature on the use of either physiological or biochemical stress markers leads to the conclusion that neither of these biomarkers in isolation can provide sufficient means of monitoring stress. Therefore, a combination of physiological and chemical stress biomarkers, with contextual information, can be a more reliable solution for stress monitoring. The current standard for stress evaluation is based on self-reported questionnaires and standardized stress scores. There is no gold standard to independently evaluate stress levels despite the availability of numerous biophysiological stress indicators. Moreover, there is no clear understanding of the relative sensitivity and specificity of these stress-related biophysiological indicators of stress in the literature. An extensive statistical analysis and classification modelling of biophysiological data gathered from healthy individuals, undergoing various induced emotional states was performed to assess the relative sensitivity and specificity of common biophysiological indicators of stress. The key indicators of stress, such as heart rate, respiratory rate, skin conductance, RR interval, heart rate variability in the electrocardiogram, and muscle activation measured by electromyography, are evaluated as gathered from an already existing, publicly available WESAD (Wearable Stress and Affect Detection) dataset. Respiratory rate and heart rate were the two best features for distinguishing between stressed and unstressed states. Both parameters can be estimated using a single photoplethysmography (PPG) sensor. The heart rate is estimated by counting the number of peaks in the PPG signal. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method. Thus, a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring is proposed. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. The results endorse that integration of the proposed algorithm into a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting. Additionally, as the public availability of datasets for the development of stress monitoring devices is limited, a clinical study was performed. The dataset created is an open-access dataset named Stress-Predict dataset. The inclusion of an additional feature, i.e., respiratory rate data along with stress and baseline labels within the dataset, makes the dataset more desirable and unique from all the other publicly available Empatica E4-based datasets. The dataset and outcomes of this study contribute to understanding any accuracy gaps in current stress monitoring and help improve these technologies or develop new technologies for stress monitoring. Most wearable stress monitoring systems are built on a supervised learning classification algorithm trained on simple statistical features. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Thus, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The outcome of the study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification. One of the most challenging tasks in physiological or pathological stress monitoring is the labelling 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. Traditional supervised machine learning classifiers require hand-crafted features and labels while on the contrary, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. 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.
NUI Galway
Publisher DOI
Attribution-NonCommercial-NoDerivs 3.0 Ireland