A comparison of wearable heart rate sensors for HRV biofeedback in the wild:an ethnographic study

Biofeedback has been consistently used to manage stress and anxiety in clinical and non-clinical settings. Existing research on the use of biosignals to provide sensory feedback has been mostly limited to laboratory settings. In this study, we performed an autoethnographic study to analyze the heart rate variability (HRV) data recorded by two wearable biosignal monitors, the polar H10 heart rate monitor chest strap and Empatica E4 wristband. Data acquisition was conducted during the daily activities of two researchers in real-life settings. Data recorded during the activities and the effects of movement artifacts of each subject were compared qualitatively against each other for HRV stress management

Exploring Personalized Vibrotactile and Thermal Patterns for Affect Regulation

The growing HCI interest in wellbeing has led to the emerging area of haptics for affect regulation. In such technologies, distinct haptic patterns are usually designed by researchers; however, current work provides a limited reflection on the rationale for the implemented patterns or the choice of haptic modality. We also know little about how people may benefit from engagement in designing such patterns and what design principles underpin them. We explored vibrotactile and thermal modalities to address these gaps and report on a study with 23 participants. These created haptic patterns for affect regulation during stress elicitation. Findings indicate that subjective and objective measures of anxiety and stress were lower in participants who received haptic patterns than those who did not, and highlighted key experiential qualities of vibrotactile and thermal patterns, and their potential for affect regulation. These open up new design opportunities for affect regulation technologies, including supporting implicit affect regulation through entrainment of slow bodily rhythms, decoupling it from predominant vibrotactile modality, designing thermal biofeedback patterns, and supporting personalized and adaptive patterns

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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Biosensing and Actuation—Platforms Coupling Body Input-Output Modalities for Affective Technologies

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer’s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of “orchestration”. By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users’ body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies

Telsiz sensör ağlarında yönlendirme ve güvenlik, önerilen güven bazlı bir yönlendirme protokolünün deneysel değerlendirilmesi.

Satisfactory results obtained from sensor networks and the ongoing development in electronics and wireless communications have led to an impressive boost in the number of applications based on WSNs. Along with the growth in popularity of WSNs, previously implemented solutions need further improvements and new challenges arise which need to be solved. One of the main concerns regarding WSNs is the existence of security threats against their routing operations. Likelihood of security attacks in a structure suffering from resource constraints makes it an important task to choose proper security mechanisms for the routing decisions in various types of WSN applications. The main purpose of this study is to survey WSNs, routing protocols, security attacks against routing layer of a WSN, introduction of Trust based models which are an effective defense mechanism against security attacks in WSNs and finally, to implement a proposed Trust based routing protocol in order to overcome security attacks. The study begins with a survey of Sensor Networks, after the introduction of WSNs and their related routing protocols, the issue of security attacks against the network layer of a Sensor Network is described with a presentation of different types of attacks and some of Trust based related works. In the final chapters of this research, a novel Trust based AODV protocol will be proposed, implemented and examined in a simulation environment. For this purpose, multiple number of scenarios will be simulated on the AODV protocol with and without Trust mechanism, then the achieved results will be compared to derive a conclusion.M.S. - Master of Scienc

HRV and Stress:A Mixed-Methods Approach for Comparison of Wearable Heart RateSensors for Biofeedback

Stress is one of the most significant health problems in today’s world. Existing work has usedheart rate variability (HRV) to detect stress and provide biofeedback in order to regulate it. There has beena growing interest in using wearable biosensors to measure HRV. Each of these sensors acquires heart ratedata using different technologies for various bodily locations, therefore posing a challenge for researchersto decide upon a particular device in a research experiment. Previous work has only compared differentsensing devices against a gold standard in terms of data quality, thus overlooking qualitative analysis for theusability and acceptability of such devices. This paper introduces a mixed-methods approach to comparethe data quality and user acceptance of the six most common wearable heart rate monitoring biosensors.We conducted a 70-minute data collection procedure to obtain HRV data from 32 participants followed bya 10-minute semi-structured interview on sensors’ wearability and comfort, long-term use, aesthetics, andsocial acceptance. We performed quantitative analysis consisting of correlation and agreement analysis onthe HRV data and thematic analysis on qualitative data obtained from interviews. Our results show that theelectrocardiography (ECG) chest strap achieved the highest correlation and agreement levels in all sessionsand had the lowest amount of artifacts, followed by the photoplethysmography (PPG) wristband, ECG sensorboard kit and PPG smartwatch. In all three sessions, wrist-worn devices showed a lower amount of agreementand correlation with the reference device. Qualitative findings from interviews highlight that participantsprefer wrist and arm-worn devices in terms of aesthetics, wearability, and comfort, followed by chest-worndevices. Moreover, participants mentioned that the latter are more likely to invite social judgment fromothers, and they would not want to wear it in public. Participants preferred the chest strap for short-term useand the wrist and arm-worn sensors over long-time

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

Application Level Performance Evaluation of Wearable Devices for Stress Classification with Explainable AI

Stress has become one of the most prominent problems of modern societies and a key contributor to major health issues. Dealing with stress effectively requires detecting it in real-time, informing the user, and giving instructions on how to manage it. Over the past few years, wearable devices equipped with biosensors that can be utilized for stress detection have become increasingly popular. Since they come with various designs and technologies and acquire biosignals from different body locations, choosing a suitable device for a particular application has become a challenge for researchers and end-users. This study compares seven common wearable biosensors for stress detection applications. This was accomplished by collecting physiological sensor data during Baseline, Stress, Recovery, and Cycling sessions from 32 participants. Machine learning algorithms were used to classify four stress classes, and the results obtained from all wearables were compared. Following this, a state-of-the-art explainable artificial intelligence method was employed to clarify our models’ predictions and investigate the influence different features have on the models’ outputs. Despite the results showing that ECG wearables perform slightly better than the rest of the devices, adding a second biosignal (EDA) improved the results significantly, tipping the balance toward multisensor wearables. Finally, we concluded that although the output results of each model can be affected by various factors, in most cases, there is no significant difference in the accuracy of stress detection by different wearables. However, the decision to select a particular wearable for stress detection applications must be made carefully considering the trade-off between the users’ expectations and preferences and the pros and cons of each device

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