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Effectiveness of Yoga * Before and After * Validation of Different * Effectiveness of Yoga * EDA Preprocessing Artifact * Effect of Different BEFORE AND AFTER PHYSIOLOGICAL MEASUREMENTS FOR EVALUATING PERFORMANCE OF YOGA AND MINDFULNESS WITH BLOOD PRESSURE 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. 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’. Individuals may be reluctant to use a system with cables, electrodes and boards in their daily life. Therefore, a comparison of different states with such systems could not be used in daily life. There is clearly a need for a suggestion and comparison of ancient and mobile meditation methods by using algorithms that could run on unobtrusive devices. An ideal system should detect high stress levels, suggest relaxation methods and control whether users are doing these exercises right or not with unobtrusive devices. Our algorithm is suitable to be embedded in such daily life applicable systems that use physiological signals such as skin temperature (ST), HRV, EDA and accelerometer (ACC). In this paper, we present the findings of our pilot study that tested the use of our algorithm during general daily activities, stress reduction activities and a stressful event. VALIDATION OF DIFFERENT PERCEIVED STRESS LEVELS BY USING THE SELF-REPORTS SPECIFICALLY, EMOTION REGULATION HAS BEEN DEFINED AS THE STUDY OF “THE PROCESSES BY WHICH WE INFLUENCE WHICH EMOTIONS WE HAVE WHEN WE HAVE THEM, AND HOW WE EXPERIENCE AND EXPRESS THEM” [4]. A LARGE BODY OF EVIDENCE HAS SHOWN THAT THERE ARE VERY DIFFERENT CONSEQUENCES DEPENDING ON THE EFFECTIVENESS PEOPLE ACHIEVE TO REGULATE THEIR EMOTIONS. NATURALLY, BOTH AT AN IMPLICIT OR EXPLICIT LEVEL, PEOPLE REGULATE EMOTIONS IN ORDER TO MAINTAIN THOSE ALLOSTERIC LEVELS PREVIOUSLY MENTIONED. THEREFORE, WHEN THERE ARE SPECIFIC STRESSORS THAT DEMAND A PARTICULAR COGNITIVE OR PHYSICAL RESPONSE, THE EMOTIONAL REACTIVITY MAY BE STRONGER AND THE NEED FOR A PROPER REGULATION MORE RELEVANT. INDEED, EMOTION REGULATION HAS SHOWN TO BE A TRANSDIAGNOSTIC FACTOR THAT IS PRESENT AT A WIDE RANGE OF MENTAL DISORDERS. IN OTHER WORDS, THE WAY PEOPLE INITIATE, IMPLEMENT AND MONITOR THEIR EMOTIONAL PROCESSES, IN ORDER TO REACH MORE DESIRABLE STATES, HAS A SIGNIFICANT IMPACT ON THE STRESS LEVELS. SOME EMOTION REGULATION (ER) STRATEGIES HAVE SHOWN TO BE CORRELATED WITH MENTAL HEALTH ISSUES. AMONG THESE STRATEGIES, COGNITIVE REAPPRAISAL, PROBLEM-SOLVING, OR ACCEPTANCE SHALL BE MENTIONED AS STRATEGIES THAT ARE NEGATIVELY CORRELATED WITH PSYCHOPATHOLOGY, WHILE RUMINATION, EXPERIENTIAL AVOIDANCE, OR SUPPRESSION ARE POSITIVELY CORRELATED WITH PSYCHOPATHOLOGY [16]. IN THIS REGARD, HINGING ON THE DIFFERENT ER STRATEGIES DEPLOYED, ER CAN CONSTITUTE A PROTECTIVE FACTOR TO FACE STRESS RESPONSES THAT ALL INDIVIDUALS EXPERIENCE AFTER MINOR OR MAJOR STRESSORS. ADDITIONALLY, AN ADAPTIVE REGULATION OF EMOTIONS, BY MANAGING STRESS, MAY ALSO BE BENEFICIAL FOR CLINICAL POPULATIONS, SUCH AS PEOPLE SUFFERING FROM AFFECTIVE DISORDERS. 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]. Physiological Stress Level Detection with Wearables by Using Context Labels as the Class Label Experimental Results and Discussion 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. Physiological Stress Level Detection with Wearables by Using Context Labels as the Class Label In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants’ daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a ‘stressful’ event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided. Effectiveness of Yoga Mindfulness and Mobile Mindfulness 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. Emotion Regulation in the Context of Stress Management Skin Temperature Failure to address triggers of stress has been shown to lead to chronic stress, anxiety and depression, and attributed to serious physical health conditions such as cardiovascular disease [6]. The World Health Organization concluded that psychological stress is one of the most significant health problems in the 21st-century and is a growing problem [7]. There are various interventions to minimize stress based on individual preferences and requirements. Stress management techniques including ancient practices such as Tai Chi [8] and yoga [9] as well as other physical activities [10] are often cited as being helpful in combating stress. Likewise traditional meditation, mindfulness [11] and cognitive behavioural therapy (CBT) [12] all have established benefits. These techniques are not applicable in office or social environments, or during most daily routines. Therefore, a smart device based stress management application may be of benefit. Recently, smartphone applications such as Calm, Pause, Heartmath and Sway have been developed for indoor environments. However, these applications are not individualized nor do they include biofeedback and studies that validate their effects are limited [13]. Validation of Different Perceived Stress Levels by using the Self-Reports 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. Yoga and Mindfulness As Tools for Emotion Regulation 2.2.3. Mobile Mindfulness Inspired By Tai-Chi—Pause Tai-Chi is an internal Chinese martial art practiced for both its defense training, its health benefits and meditation. There is good evidence of benefits for depression, cardiac and stroke rehabilitation and dementia [23]. The term Tai-Chi refers to a philosophy of the forces of yin and yang, related to the moves. An iPhone application Pause inspired by Tai-Chi is used for guided mindfulness which draws upon the principles of mindfulness meditation to trigger the body’s rest and digest response, quickly restoring attention [24]. 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. Unobtrusive Stress Detection System 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. The Weka machine learning toolkit [54] is used for identifying stress levels. The Weka toolkit has several preprocessing features before classification. Our data set was not balanced when the number of instances belonging to each class was considered. We solved this issue by removing samples from the majority class. We selected random undersampling because it is the most commonly applied method [55]. In this way, we prevented classifiers from biasing towards the class with more instances. In this study, we employed five different machine learning classification algorithms to recognize different stress levels: MultiLayer Perceptron (MLP), Random Forest (RF) (with 100 trees), K-nearest neighbors (kNN) (n = 1–4), Linear discriminant analysis (LDA), Principal component analysis (PCA) and support vector machine (SVM) with a radial basis function. These algorithms were selected because they were the most commonly applied and successful classifiers for detecting stress levels [30,48]. In addition, 10-fold stratified cross-validation was then applied and hyperparameters of the machine learning algorithms were fine-tuned with grid search. The best performing models have been reported. DIMENSIONALITY REDUCTION 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]. We applied correlation-based feature selection (CBFS) technique which is available in the Weka machine learning package for combined signal [56]. The CBFS method removes the features that are less correlated with the output class. For every model, we selected the ten most important features. This method is applied for MLP, RF, kNN and LDA. In order to create an SVM based model, we applied PCA based dimensionality reduction where the covered variance is selected as 0.95 (the default setting). In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants’ daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a ‘stressful’ event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided. TYOGA AND MINDFULNESS AS TOOLS FOR EMOTION REGULATION 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. Application of James Gross’s prominent emotion regulation model in the context of stress management and measuring the physiological component with smart bands. 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]. The Weka machine learning toolkit [54] is used for identifying stress levels. The Weka toolkit has several preprocessing features before classification. Our data set was not balanced when the number of instances belonging to each class was considered. We solved this issue by removing samples from the majority class. We selected random undersampling because it is the most commonly applied method [55]. In this way, we prevented classifiers from biasing towards the class with more instances. In this study, we employed five different machine learning classification algorithms to recognize different stress levels: MultiLayer Perceptron (MLP), Random Forest (RF) (with 100 trees), K-nearest neighbors (kNN) (n = 1–4), Linear discriminant analysis (LDA), Principal component analysis (PCA) and support vector machine (SVM) with a radial basis function. These algorithms were selected because they were the most commonly applied and successful classifiers for detecting stress levels [30,48]. In addition, 10-fold stratified cross-validation was then applied and hyperparameters of the machine learning algorithms were fine-tuned with grid search. The best performing models have been reported. 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]. EFFECT OF DIFFERENT PHYSIOLOGICAL SIGNALS ON STRESS DETECTION ACKNOWLEDGING THE THOUGHTS AND BODY ARE ALSO ASPECTS OF MINDFULNESS. EACH DAY HUMANS EXPERIENCE THOUSANDS OF THOUGHTS, THE MAJORITY BEING OF NO CONSEQUENCE. IN SOME INSTANCES, THESE THOUGHTS ARE REPETITIVE AND NEGATIVE IN NATURE WHICH CAN LEAD TO INCREASED STRESS AND THE RELATED UNPLEASANT PHYSICAL SYMPTOMS SUCH AS FEELING ANXIOUS, NAUSEA AND TENSION HEADACHES. BEING MINDFUL INCLUDES AN AWARENESS OF OUR THINKING AND WHETHER WE ARE CAUGHT UP WITH OUR THOUGHTS RATHER THAN BEING AWARE OF THE MOMENT. ADDITIONALLY, ON A DAILY BASIS, AWARENESS OF THE PHYSICAL BODY MAY BE MINIMAL; BEING MINDFUL INCLUDES INCREASING THIS AWARENESS THROUGH BECOMING MORE CONNECTED WITH THE SENSATIONS IN THE BODY. THIS MIGHT INCLUDE EXPERIENCING THE LEGS MOVING WHEN WALKING, OR FEELING THE GROUND UNDER THE FEET OR THE NATURAL WAY OF THE BODY WHILST STANDING. WHEN FACED WITH STRESSFUL EVENTS, PEOPLE MAKE AUTONOMIC AND CONTROLLED EFFORTS TO REDUCE THE NEGATIVE IMPACT AND MAXIMIZE THE POSITIVE IMPACT THAT EVERY SPECIFIC SITUATION MAY PROVOKE. GENERALLY, THIS PROCESS IS DENOMINATED AS EMOTION REGULATION, FORMALLY DEFINED AS THE PROCESS BY WHICH INDIVIDUALS CAN INFLUENCE WHAT EMOTIONS THEY HAVE, WHEN THEY HAVE THEM AND HOW THEY EXPERIENCE AND EXPRESS THOSE EMOTIONS [4]. IT HAS BEEN SUGGESTED THAT THE TERM EMOTION REGULATION CAN BE UNDERSTOOD AS A BROAD TAG THAT COMPRISES THE REGULATION OF ALL RESPONSES THAT ARE EMOTIONALLY CHARGED, FROM BASIC EMOTIONS TO COMPLEX MOOD STATES AS WELL AS REGULATION OF EVERYDAY LIFE [5]. CONTACTS Name * Your email * Message * Send