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Submission: On February 23 via api from US — Scanned from DE
Effective URL: https://huntergatherergroup.com/
Submission: On February 23 via api from US — Scanned from DE
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Before and After * Description of the * Yoga and Mindfulness * Machine Learning Classifier * EDA Preprocessing Artifact * How to Relax DESCRIPTION OF THE DATA COLLECTION PROCEDURE IN THIS STUDY, BY USING OUR AUTOMATIC STRESS DETECTION SYSTEM WITH THE USE OF EMPATICA-E4 SMART-BANDS, WE DETECTED STRESS LEVELS AND SUGGESTED APPROPRIATE RELAXATION METHODS (I.E., TRADITIONAL OR MOBILE) WHEN HIGH STRESS LEVELS ARE EXPERIENCED. OUR STRESS DETECTION FRAMEWORK IS UNOBTRUSIVE, COMFORTABLE AND SUITABLE FOR USE IN DAILY LIFE AND OUR RELAXATION METHOD SUGGESTION SYSTEM MAKES ITS DECISIONS BASED ON THE PHYSICAL ACTIVITY-RELATED CONTEXT OF A USER. TO TEST OUR SYSTEM, WE COLLECTED EIGHT DAYS OF DATA FROM 16 INDIVIDUALS PARTICIPATING IN AN EU RESEARCH PROJECT TRAINING EVENT. INDIVIDUALS WERE EXPOSED TO VARIED STRESSFUL AND RELAXATION EVENTS (1) TRAINING AND LECTURES (MILD STRESS), (2) YOGA, MINDFULNESS AND MOBILE MINDFULNESS (PAUSE) (RELAX) AND (3) WERE REQUIRED TO GIVE A MODERATED PRESENTATION (HIGH STRESS). THE PARTICIPANTS WERE FROM DIFFERENT COUNTRIES WITH DIVERSE CULTURES. Emotion Regulation in the Context of Stress Management Description of the Data Collection Procedure EMOTION REGULATION IN THE CONTEXT OF STRESS MANAGEMENT Yoga has become a global phenomenon and is widely practiced in many different forms. Generally, all types of yoga include some elements of relaxation. Additionally, some forms include mainly pranayama and others are more physical in nature. One such practice is vinyasa flow which involves using the inhale and exhale of the breathing pattern to move through a variety of yoga postures; this leads to the movement becoming meditative. The practice often includes pranayama followed by standing postures linked together with a movement called vinyasa, (similar to a sun salutation) which helps to keep the body moving and increases fitness, flexibility and helps maintain linkage with the breath. The practice also often includes a range of seated postures, an inversion (such as headstand or shoulder stand) and final relaxation ‘savasana’. As yoga evolved, physical movement in the form of postures was included and integrated with yogic breathing ‘prana’ and elements of relaxation. The underlying purpose is to create physical flexibility, reduce pain and unpleasant stimuli and reduce negative thoughts and emotions to calm the mind and body, thereby improving well-being. In the healthcare literature, the benefits are reported to be far-reaching both for mental and physical health conditions such as anxiety, depression, cardiovascular disease, cancer and respiratory symptoms. It is also reported to reduce muscular-skeletal problems and physical symptoms through increasing the awareness of the physical body. Numerous psychological scientists have investigated perceived stress. Individuals who display a mismatch between contextual demands and perceived resources constantly (rather than during a specific moment in time) are referred to as experiencing chronic stress. Chronic stress has not only been shown to be very relevant in people’s well-being and quality of life, but also important in the appearance and maintenance of several physical and mental diseases [14]. TEFFECT OF DIFFERENT PHYSIOLOGICAL SIGNALS ON STRESS DETECTION Therefore, from whole psychotherapeutic treatments to single self-applied applications, studies in the literature have focused on how people can better regulate their emotions and manage their stress levels. Among many other techniques, cognitive behavioral therapy, autogenic training, biofeedback, breathing exercises, relaxation techniques, guided imagery, mindfulness, yoga, or Tai-Chi, are some of the stress management interventions that have received attention from researchers. EDA Feature Extraction Methods After the artifact removal phase, features were extracted from the EDA signal. This signal has two components phasic and tonic; features from both components were extracted (see Table 2). The cvxEDA tool [42] was used for the decomposition of the signal into these components. This tool uses convex optimization to estimate the Autonomic Nervous System (ANS) activity that is based on Bayesian statistics. Our stress detection system developed in [32] allows users to be aware of their stress levels during their daily activities without creating any interruption or restriction. The only requirement to use this system is the need to wear a smart band. Participants in this study wore the Empatica E4 smart band on their non-dominant hand. The smart band provides Blood Volume Pressure, ST, EDA, IBI (Interbeat Interval) and 3D Acceleration. The data are stored in the memory of the device. Then, the artifacts of physiological signals were detected and handled. The features were extracted from the sensory signals and fed to the machine learning algorithm for prediction. In order to use this system, pre-trained machine learning models are required. For training the models, feature vectors and collected class labels were used. This paper describes emotion regulation in the context of stress management and how yoga and mindfulness can be used for regulating emotions (Section 2). Methods of detecting stress and analyzing context based on physical activity are described (Section 3) and data are presented related to our method for stress level detection with the use of smart-bands (Section 4). Experimental results and discussion are also presented (Section 5) and we present the conclusions and future works of the study (Section 6). In this section, we compared the effect of stress management tools such as yoga and mindfulness on blood pressure. It is expected that blood pressure sensors will be part of unobtrusive wrist-worn wearable sensors soon. We plan to integrate a blood pressure (BP) module to our system when they are available. Therefore, by using the measurements of a medical-grade blood pressure monitor, we provided insights about how stress reaction affects BP. We further applied and tested the prominent emotion regulation model of James Gross by analyzing these measurements in the context of stress management. We measured the diastolic and systolic BP and pulse using a medical-grade blood pressure monitor before and after the yoga and mindfulness sessions. In order to ensure that the participants were relaxed and that an accurate BP was recorded, BP was measured three times with the mean as the recorded result. A one-sample t-test was applied to the difference between mean values. The results are shown in Table 6. For calculating physical activity intensity, we used the EDAExplorer tool [41]. The stillness metric is used for this purpose. It is the percentage of periods in which the person is still or motionless. Total acceleration must be less than a threshold (default is 0.1 [41]) for 95 percent of a minute in order for this minute to count as still [41]. Then, the ratio of still minutes in a session can be calculated. For the ratio of still minutes in a session, we labeled sessions below 20% as still, above 20% as active and suggested relaxation method accordingly (see Figure 3). MACHINE LEARNING CLASSIFIER ALGORITHMS FOR CALCULATING PHYSICAL ACTIVITY INTENSITY, WE USED THE EDAEXPLORER TOOL [41]. THE STILLNESS METRIC IS USED FOR THIS PURPOSE. IT IS THE PERCENTAGE OF PERIODS IN WHICH THE PERSON IS STILL OR MOTIONLESS. TOTAL ACCELERATION MUST BE LESS THAN A THRESHOLD (DEFAULT IS 0.1 [41]) FOR 95 PERCENT OF A MINUTE IN ORDER FOR THIS MINUTE TO COUNT AS STILL [41]. THEN, THE RATIO OF STILL MINUTES IN A SESSION CAN BE CALCULATED. FOR THE RATIO OF STILL MINUTES IN A SESSION, WE LABELED SESSIONS BELOW 20% AS STILL, ABOVE 20% AS ACTIVE AND SUGGESTED RELAXATION METHOD ACCORDINGLY (SEE FIGURE 3). APPLICATION OF JAMES GROSS’S PROMINENT EMOTION REGULATION MODEL IN THE CONTEXT OF STRESS MANAGEMENT AND MEASURING THE PHYSIOLOGICAL COMPONENT WITH SMART BANDS. CONTEXT IS A BROAD TERM THAT COULD CONTAIN DIFFERENT TYPES OF INFORMATION SUCH AS CALENDARS, ACTIVITY TYPE, LOCATION AND ACTIVITY INTENSITY. PHYSICAL ACTIVITY INTENSITY COULD BE USED TO INFER CONTEXTUAL INFORMATION. IN MORE RESTRICTED ENVIRONMENTS SUCH AS OFFICE, CLASSROOMS, PUBLIC TRANSPORTATION AND PHYSICAL ACTIVITY INTENSITY COULD BE LOW, WHEREAS, IN OUTDOOR ENVIRONMENTS, PHYSICAL ACTIVITY INTENSITY COULD INCREASE. THEREFORE, AN APPROPRIATE RELAXATION METHOD WILL CHANGE ACCORDING TO THE CONTEXT OF INDIVIDUALS. EDA PREPROCESSING ARTIFACT DETECTION AND REMOVAL METHODS MINDFULNESS HAS BEEN SHOWN TO BE OF BENEFIT TO PHYSICAL AND MENTAL HEALTH. IT IS CURRENTLY RECOMMENDED BY THE NATIONAL INSTITUTE FOR CLINICAL EXCELLENCE [22] AS ADJUNCTIVE THERAPY TO COGNITIVE BEHAVIOURAL THERAPY (CBT) FOR THE PREVENTION OF RELAPSE DEPRESSION. Description of the Data Collection Procedure Dimensionality Reduction Yoga and Mindfulness As Tools for Emotion Regulation Relaxation Method Suggestion by Analyzing the Physical Activity-Based Context BEFORE AND AFTER PHYSIOLOGICAL MEASUREMENTS FOR EVALUATING PERFORMANCE OF YOGA AND MINDFULNESS WITH BLOOD PRESSURE Researchers have created the ability to detect stress in laboratory environments with medical-grade devices [25,26,27,28]; smartwatches and smart bands started to be used for stress level detection studies [29,30,31]. These devices provide high comfort and rich functionality for the users, but their stress detection accuracies are lower than medical-grade devices due to low signal quality and difficulty obtaining data in intense physical activity. If data are collected for long periods, researchers have shown that their detection performance improves [32]. During movement periods, the signal can be lost (gap in the data) or artifacts might be generated. Stress level detection accuracies for 2-classes by using these devices are around 70% [29,30,33,34]. Application of James Gross’s prominent emotion regulation model in the context of stress management and measuring the physiological component with smart bands. TEXPERIMENTAL RESULTS AND DISCUSSION Mindfulness Mindfulness involves being more present at the moment by acknowledging the here and now, often referred to as ‘being present’ rather than focussing on the past or future [8]. Being present may include being aware of our surroundings and the environment, or of what we are eating and drinking and physical sensations such as the sun or wind on our skin. Stress constitutes a complex process that is activated by a physical or mental threat to the individuals’ homeostasis, comprising a set of diverse psychological, physiological and behavioral responses [1]. Although it is usually considered a negative response, stress actually constitutes a key process for ensuring our survival. However, when a stress response is repeatedly triggered in the absence of a challenging stimulus, or if there is constant exposure to challenging situations, stress can become harmful. Evidence suggests that, in either of these two contexts, stress is a persistent factor for the development of psycho-pathological conditions [2,3]. In this study, by using our automatic stress detection system with the use of Empatica-E4 smart-bands, we detected stress levels and suggested appropriate relaxation methods (i.e., traditional or mobile) when high stress levels are experienced. Our stress detection framework is unobtrusive, comfortable and suitable for use in daily life and our relaxation method suggestion system makes its decisions based on the physical activity-related context of a user. To test our system, we collected eight days of data from 16 individuals participating in an EU research project training event. Individuals were exposed to varied stressful and relaxation events (1) training and lectures (mild stress), (2) yoga, mindfulness and mobile mindfulness (PAUSE) (relax) and (3) were required to give a moderated presentation (high stress). The participants were from different countries with diverse cultures. In addition, 1440 h of mobile data (12 h in a day) were collected during this eight-day event from each participant measuring their stress levels. Data were collected during the training sessions, relaxation events and the moderated presentation and during their free time for 12 h in a day, demonstrating that our study monitored daily life stress. EDA and HR signals were collected to detect physiological stress and a combination of different modalities increased stress detection, performance and provided the most discriminative features. We first applied James Gross ER model in the context of stress management and measured the blood pressure during the ER cycle. When the known context was used as the label for stress level detection system, we achieved 98% accuracy for 2-class and 85% accuracy for 3-class. Most of the studies in the literature only detect stress levels of individuals. The participants’ stress levels were managed with yoga, mindfulness and a mobile mindfulness application while monitoring their stress levels. We investigated the success of each stress management technique by the separability of physiological signals from high-stress sessions. We demonstrated that yoga and traditional mindfulness performed slightly better than the mobile mindfulness application. Furthermore, this study is not without limitations. In order to generalize the conclusions, more experiments based on larger sample groups should be conducted. As future work, we plan to develop personalized perceived stress models by using self-reports and test our system in the wild. Furthermore, attitudes in the psychological field constitute a topic of utmost relevance, which always play an instrumental role in the determination of human behavior [58]. We plan to design a new experiment which accounts for the attitudes of participants towards relaxation methods and their effects on the performance of stress recognition systems. After detecting the stress level of individuals, researchers should recover from the stressed state to the baseline state. To the best of our knowledge, there are very few studies that combine automatic stress detection (using physiological data) with recommended appropriate stress management techniques. Ahani et al. [35] examined the physiological effect of mindfulness. They used the Biosemi device which acquires electroencephalogram (EEG) and respiration signals. They successfully distinguished control (non-meditative state) and meditation states with machine learning algorithms. Karydis et al. [36] identified the post-meditation perceptual states by using a wearable EEG measurement device (Muse headband). Mason et al. [37] examined the effect of yoga on physiological signals. They used PortaPres Digital Plethtsmograph for measuring blood pressure and respiration signals. They also showed the positive effect of yoga by using these signals. A further study validated the positive effect of yoga with physiological signals; researchers monitored breathing and heart rate pulse with a piezoelectric belt and a pulse sensor [21]. They demonstrated the effectiveness of different yogic breathing patterns to help participants relax. There are also several studies showing the effectiveness of mobile mindfulness apps by using physiological signals [20,38,39]. However, it may be challenging for some individuals to do this with a multitude of distractions around them and, therefore, they may choose to identify a particular time and place when and where they can sit in a comfortable position to start to become aware of their breathing and bodily sensations. CONTACTS Name * Your email * Message * Send