Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features

Work stress impacts people's daily lives. Their well-being can be improved if the stress is monitored and addressed in time. Attaching physiological sensors are used for such stress monitoring and analysis. Such approach is feasible only when the person is physically presented. Due to the transfer of the life from offline to online, caused by the COVID-19 pandemic, remote stress measurement is of high importance. This study investigated the feasibility of estimating participants' stress levels based on remote physiological signal features (rPPG) and behavioral features (facial expression and motion) obtained from facial videos recorded during online video meetings. Remote physiological signal features provided higher accuracy of stress estimation (78.75%) as compared to those based on motion (70.00%) and facial expression (73.75%) features. Moreover, the fusion of behavioral and remote physiological signal features increased the accuracy of stress estimation up to 82.50%. © 2022 Owner/Author.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review.

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person's physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3% and the error of forehead temperature within ±0.5°C. © 2023 Tech Science Press. All rights reserved.

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

Exercise becomes Music: Creating communication through new music experiences using exercise data

This is a new communication proposal using data acquired by Physiological signal measurement. Since the COVID-19 pandemic, opportunities for exercise have been decreasing as people have fewer opportunities to go outside. For this reason, we have created several exercise guidance contents that can be used on a daily basis. The data obtained from the exercise guidance contents can be used in various ways. For example, in a physical education class, the children were motivated to improve their exercise by seeing the actual data of their exercise. There are three exercise guidance contents, each of which has its own feedback loop of acquiring data and utilizing it. "Exercise becomes Music"is an attempt to use them in a more meta way to motivate people to exercise, and to create a larger feedback loop throughout. The data acquired from the exercise guidance content is converted into music that makes the most of the characteristics of each content, and the content is designed to be played by the experiencer while combining the generated music. The surprise of having one's own data converted into music, and the experience of playing with the combination of data, will create feedback for each exercise, and we also hope that sharing the combined music online will create new communication. © 2022 IEEE.

A Novel Methodology for the Synchronous Collection and Multimodal Visualization of Continuous Neurocardiovascular and Neuromuscular Physiological Data in Adults with Long COVID.

Background: Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be linked to neuromuscular function impairment (NMFI). The current clinical assessment for NCVI monitors neurocardiovascular performance upon the application of orthostatic stressors such as an active (i.e., self-induced) stand or a passive (tilt table) standing test. Lower limb muscle contractions may be important in orthostatic recovery via the skeletal muscle pump. In this study, adults with long COVID were assessed with a protocol that, in addition to the standard NCVI tests, incorporated simultaneous lower limb muscle monitoring for NMFI assessment. Methods: To conduct such an investigation, a wide range of continuous non-invasive biomedical sensing technologies were employed, including digital artery photoplethysmography for the extraction of cardiovascular signals, near-infrared spectroscopy for the extraction of regional tissue oxygenation in brain and muscle, and electromyography for assessment of timed muscle contractions in the lower limbs. Results: With the proposed methodology described and exemplified in this paper, we were able to collect relevant physiological data for the assessment of neurocardiovascular and neuromuscular functioning. We were also able to integrate signals from a variety of instruments in a synchronized fashion and visualize the interactions between different physiological signals during the combined NCVI/NMFI assessment. Multiple counts of evidence were collected, which can capture the dynamics between skeletal muscle contractions and neurocardiovascular responses. Conclusions: The proposed methodology can offer an overview of the functioning of the neurocardiovascular and neuromuscular systems in a combined NCVI/NMFI setup and is capable of conducting comparative studies with signals from multiple participants at any given time in the assessment. This could help clinicians and researchers generate and test hypotheses based on the multimodal inspection of raw data in long COVID and other cohorts.

Physio6: A Sensor-Based Monitoring System for 6-Minute Walking Test in the Era of COVID-19

Coronavirus disease of 2019 (COVID-19) is still severe nowadays, and plentiful COVID-19 patients need careful rehabilitation. The 6-minute walking test (6MWT) is a common clinical trial that requires the patient to walk as far as possible in a corridor for 6 minutes, significantly indicating patients' cardiopulmonary disease conditions and rehabilitation. A traditional 6MWT provides the 6-minute walking distance (6MWD) as the primary result for clinical analysis. In this paper, we propose Physio6, a sensor-based monitoring system for 6MWT, which monitors one patient's various physiological signals and indicates her/his condition during the test. The system also provides the functions of early warning based on physiological signal monitoring and automatically or manually recording the adverse events, such as hypoxia or dyspnea. Moreover, Physio6 is able to communicate with the existing systems in hospitals, and to generate a comprehensive report that summarizes the performance of the patient in the current 6MWT and even in the past ones. Our system has been deployed in four hospitals. Compared with the conventional distance-based measurement, our preliminary validation reveals that the extracted physiological parameters are promisingly valuable for clinical decision-making. System quality and device comfort are also confirmed by questionnaires. The potential of leveraging this system to perform the remote 6MWT at home/in communities as a solution of COVID-19 patient rehabilitation monitoring is also discussed. © 2021 IEEE.

LSTM-Based Emotion Detection Using Physiological Signals: IoT Framework for Healthcare and Distance Learning in COVID-19.

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike the coronavirus (Covid-19) outbreak, a remote Internet of Things (IoT) enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework that enables wireless communication of physiological signals to data processing hub where long short-term memory (LSTM)-based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions that enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In the proposed IoT protocols (TS-MAC and R-MAC), ultralow latency of 1 ms is achieved. R-MAC also offers improved reliability in comparison to state of the art. In addition, the proposed deep learning scheme offers high performance ([Formula: see text]-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support, and general wellbeing.