Graphene Oxide Functionalized Biosensor for Detection of Stress-Related Biomarkers

A graphene oxide (GO)-based cortisol biosensor was developed to accurately detect cortisol concentrations from sweat samples at point-of-care (POC) sites. A reference electrode, counter electrode, and working electrode make up the biosensor, and the working electrode was functionalized using multiple layers consisting of GO and antibodies, including Protein A, IgG, and anti-Cab. Sweat samples contact the anti-Cab antibodies to transport electrons to the electrode, resulting in an electrochemical current response. The sensor was tested at each additional functionalization layer and at cortisol concentrations between 0.1 and 150 ng/mL to determine how the current response differed. A potentiostat galvanostat device was used to measure and quantify the electrochemical response in the GO-based biosensor. In both tests, the electrochemical responses were reduced in magnitude with the addition of antibody layers and with increased cortisol concentrations. The proposed cortisol biosensor has increased accuracy with each additional functionalization layer, and the proposed device has the capability to accurately measure cortisol concentrations for diagnostic purposes.

Anxiety Detection Using Physiological Data and Wearable IoT Devices

Anxiety has been the primary cause of multiple illnesses in society. Gadgets, smartwatches, and wristbands have become an integral part of our daily lives and are widely used. This shows whether wearable sensors and technologies can be used to prevent anxiety and stress. The authors look at recent research on recognizing anxiety in everyday life in this chapter. There are few studies that examine the detection of stress in daily life, as there are few studies that examine a variety of tasks involving the recognition of anxiety in regulated laboratory settings. In this analysis, the authors isolate and examine tasks based on the physiological modality used and their intended areas, such as the workplace, education, automobiles, and the uncontrolled conditions of everyday life. In addition, they explore promising technologies, prevention, and research issues.

Immunosensor for Assessing the Welfare of Trainee Guide Dogs

Cortisol is a well established biomarker hormone that regulates many processes in the body and is widely referred to as the stress hormone. Cortisol can be used as a stress marker to allow for detection of stress levels in dogs during the training process. This test will indicate if they will handle the stress under the training or if they might be more suitable as an assistant or companion dog. An immunosensor for detection of cortisol was developed using electrochemical impedance spectroscopy (EIS). The sensor was characterized using chemical and topographical techniques. The sensor was calibrated and its sensitivity determined using a cortisol concentration range of 0.0005 to 50 μg/mL. The theoretical limit of detection was found to be 3.57 fg/mL. When the immunosensor was tested on canine saliva samples, cortisol was detected and measured within the relevant physiological ranges in dogs.

The Use of Artificial Intelligence in Assessing Affective States in Livestock

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

STRESS PREDICTION USING RANDOM FOREST LINEAR REGRESSION

In today’s world stress has become a more familiar word because of its disastrous impact on the huge number of people worldwide. It is very important to keep stress under control every time, as it is the primitive reason for much major health issues. Some people meditate to g e t r i d o f i t and others choose to use medicines to control their stress levels. Students also found with very much stressed out because of academics, projects, exams, and whatnot. There are many ways through which one can check whether you have stress or not. According to this situation, the medical diagnosis system based on human physiology becomes more requisite as compared to others. Human physiology-based study plays a important character in the detection of mental stress in persons. There have also been eventual researches which are done on the detection of stress based on facial emotions. To find out whether stressed or not we need to see a doctor and get checked, but it seems to be not practical at all times to do so. In fact, in the era of digitalism, where everyone has a smartphone there is a dearth of finding novel ways through which we can make use of technology to detect your stress levels automatically. There are wearable devices that detect stress levels based on your body activity. Many approaches aim for the detection of stress through the use of wearable devices. The approach that we are presenting in this project is predicting stress through medical data of the patients using random forest regression. Additionally, an examination between oneself fabricated convolution neural model and a portion of the pre-trained models has been finished. This is another methodology and we are getting very promising precision by utilizing sufficient research experiments on 2000 irregular trees in the model. The results achieved are the outcomes of effectively anticipated with the accuracy utilizing the model. The outcomes of this research can be useful in directing the future which explor

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.

Real-Time Psychological Stress Detection According to ECG Using Deep Learning

Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications.

The effect of DC magnetic field on signal characteristics of magnetic Barkhausen noise

Magnetic Barkhausen Noise (MBN) detection technology shows great potential in the pipeline detection of stress, microstructure, and fatigue damage. However, in actual application, some pipes such as those near power plant are exposed to external magnetic fields or contain remanence. The presence of these magnetic fields will affect MBN features and then influence the testing results. To investigate the effect of DC magnetic field on MBN signal characteristics, theoretical analysis and experimental verification were carried out in this paper. It is found that the intensity and direction of the DC magnetic field have great effects on MBN signal characteristics, including the changes in the energy of MBN signal, the peak position and the shape of MBN signal profile.