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The number of fitness tracker users increases every day. Most of the applications require authentication to protect privacy-preserving operations. Biometrics such as face images have been used widely as login tokens, but they have privacy issues. Moreover, occlusions like face masks used for COVID may reduce their effectiveness. Smartbands can track heart rate, movements, and electrodermal activities. They have been widely used for health-related applications. The use of smartbands for authentication is in the exploratory stage. Physiological signals gathered from smartbands may be used to create a multi-modal and multi-sensor authentication system. The popularity of smartbands enables us to deploy new applications without a need to buy additional hardware. In this study, we explore the multi-modal physiological biometrics with end-to-end deep learning and feature-based traditional systems. We collected multi-modal physiological data of 80 people for five days using modern smartbands. We applied a deep learning approach to the multi-modal physiological data and used feature-based traditional machine learning classifiers. The CNN-LSTM model achieved a 9.31% equal error rate and outperformed other models in terms of authentication performance.

An automatic stress detection system that uses unobtrusive smart bands will contribute to human health and well-being by alleviating the effects of high stress levels. However, there are a number of challenges for detecting stress in unrestricted daily life which results in lower performances of such systems when compared to semi-restricted and laboratory environment studies. The addition of contextual information such as physical activity level, activity type and weather to the physiological signals can improve the classification accuracies of these systems. We developed an automatic stress detection system that employs smart bands for physiological data collection. In this study, we monitored the stress levels of 16 participants of an EU project training every day throughout the eight days long event by using our system. We collected 1440 hours of physiological data and 2780 self-report questions from the participants who are from diverse countries. The project midterm presentations (see Figure 3) in front of a jury at the end of the event were the source of significant real stress. Different types of contextual information, along with the physiological data, were recorded to determine the perceived stress levels of individuals. We further analyze the physiological signals in this event to infer long term perceived stress levels which we obtained from baseline PSS-14 questionnaires. Session-based, daily and long-term perceived stress levels could be identified by using the proposed system successfully.

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual&rsquo, 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&rsquo, 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 &lsquo, stressful&rsquo, 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.

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.

Researchers strive hard to develop effective ways to detect and cope with enduring high-level dailystressas early as possible to prevent serious health consequences. Although research has traditionally been conducted in laboratory settings, a set of new studies have recently begun to be conducted in ecological environments with unobtrusive wearable devices. Since patterns ofstressare ideographic, person-independent models have generally lower accuracies. On the contrary, person-specific models have higher accuracies but they require a long-term data collection period. In this study, we developed a hybrid approach ofpersonallevelstressclusteringby using baselinestressself-reports to increase the success of person-independent models without requiring a substantial amount ofpersonaldata. We further addeddecision levelsmoothingto our unobtrusive smartwatch basedstresslevel differentiation system to increase theperformanceby correcting false labels assigned by the machine learning algorithm. In order to test and evaluate our system, we collected physiological data from 32 participants of a summer school with wrist-worn unobtrusive wearable devices. This event is comprised of baseline, lecture, exam and recovery sessions. In the recovery session, astressmanagement method was applied to alleviate thestressof the participants. The perceivedstressin the form of NASA-TLX questionnaires collected from the users as self-reports and physiologicalstresslevels extracted using wearable sensors are examined separately. By using our system, we were able to differentiate the 3-levels ofstresssuccessfully. We further substantially increase ourperformancebypersonalstresslevelclusteringand by applying high-level accuracy calculation anddecision levelsmoothingmethods. We also demonstrated the success of thestressreduction methods by analyzing physiological signals and self-reports.

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.