How laboratory experiments can be exploited for monitoring stress in the wild: a bridge between laboratory and daily life

Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies

How to Relax in Stressful Situations: A Smart Stress Reduction System

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. 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

personal stress level clustering and decision level smoothing to enhance the performance of ambulatory stress detection with smartwatches

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Certain behavioral characteristics and stress responses of out-of-breeding ewes and rams during an intensive fattening program

The aim was to investigate behaviors and stress responses of rams and ewes in an intensive fattening period. Out-of-breeding rams (Hemsin = 10, Karakul = 10) and ewes (Hemsin = 8, Karakul = 10) were used in the study. Sheep and rams belonging to each breed were placed into four different pens. Behavioral observations (individual, feeding, abnormal self-grooming behaviors) were performed 2 days a week for two groups a day for 1 h. Blood samples were collected at the beginning, at the 4th week, and at the end of the fattening period. Karakul ewes displayed significantly more feeding and rumination behavior than Hemsin ewes, while there was no significant difference between ram groups. Ewes displayed more lying and rumination behavior than rams in the current study. On the other hand, rams were more active than ewes and also showed more abnormal behavior (butting other animals) during the fattening period. Sheep breed had no influence on packed cell volume (PCV), hemoglobin (Hb) concentration, or cortisol level at any sampling time. PCV, Hb, and cortisol levels at the middle of the fattening period were higher in ewes than rams. In conclusion, the behavioral repertoire of Hemsin and Karakul breeds in intensive fattening does not reveal any stress responses

Certain behavioral characteristics and stress responses of out-of-breeding ewes and rams during an intensive fattening program

The aim was to investigate behaviors and stress responses of rams and ewes in an intensive fattening period. Out-of-breeding rams (Hemsin = 10, Karakul = 10) and ewes (Hemsin = 8, Karakul = 10) were used in the study. Sheep and rams belonging to each breed were placed into four different pens. Behavioral observations (individual, feeding, abnormal self-grooming behaviors) were performed 2 days a week for two groups a day for 1 h. Blood samples were collected at the beginning, at the 4th week, and at the end of the fattening period. Karakul ewes displayed significantly more feeding and rumination behavior than Hemsin ewes, while there was no significant difference between ram groups. Ewes displayed more lying and rumination behavior than rams in the current study. On the other hand, rams were more active than ewes and also showed more abnormal behavior (butting other animals) during the fattening period. Sheep breed had no influence on packed cell volume (PCV), hemoglobin (Hb) concentration, or cortisol level at any sampling time. PCV, Hb, and cortisol levels at the middle of the fattening period were higher in ewes than rams. In conclusion, the behavioral repertoire of Hemsin and Karakul breeds in intensive fattening does not reveal any stress responses

Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods