Papers by Niaz Chalabianloo
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 ... more 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.
Smart watch based stress detection in real life
Software-Defined Edge Defense Against IoT-Based DDoS
2017 IEEE International Conference on Computer and Information Technology (CIT)
Exploring Personalized Vibrotactile and Thermal Patterns for Affect Regulation
Designing Interactive Systems Conference 2021
Healthcare
Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a r... more 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 da...
Sensors
Research in the use of ubiquitous technologies, tracking systems and wearables within mental heal... more 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 tha...
IEEE Sensors Journal
An automatic stress detection system that uses unobtrusive smart bands will contribute to human h... more An automatic stress detection system that uses unobtrusive smart bands will contribute to human health and wellbeing 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.
Sensors
Chronic stress leads to poor well-being, and it has effects on life quality and health. Society m... more 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...
IEEE Access
Researchers strive hard to develop effective ways to detect and cope with enduring high-level dai... more Researchers strive hard to develop effective ways to detect and cope with enduring high-level daily stress as 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 of stress are 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 of personal level stress clustering by using baseline stress self-reports to increase the success of person-independent models without requiring a substantial amount of personal data. We further added decision level smoothing to our unobtrusive smartwatch based stress level differentiation system to increase the performance by 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, a stress management method was applied to alleviate the stress of the participants. The perceived stress in the form of NASA-TLX questionnaires collected from the users as self-reports and physiological stress levels extracted using wearable sensors are examined separately. By using our system, we were able to differentiate the 3-levels of stress successfully. We further substantially increase our performance by personal stress level clustering and by applying high-level accuracy calculation and decision level smoothing methods. We also demonstrated the success of the stress reduction methods by analyzing physiological signals and self-reports.
Sensors
The negative effects of mental stress on human health has been known for decades. High-level stre... more 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 ...
IEEE Access
Stress is one of the most significant health problems in today's world. Existing work has used he... more Stress is one of the most significant health problems in today's world. Existing work has used heart rate variability (HRV) to detect stress and provide biofeedback in order to regulate it. There has been a growing interest in using wearable biosensors to measure HRV. Each of these sensors acquires heart rate data using different technologies for various bodily locations, therefore posing a challenge for researchers to decide upon a particular device in a research experiment. Previous work has only compared different sensing devices against a gold standard in terms of data quality, thus overlooking qualitative analysis for the usability and acceptability of such devices. This paper introduces a mixed-methods approach to compare the 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 by a 10-minute semi-structured interview on sensors' wearability and comfort, long-term use, aesthetics, and social acceptance. We performed quantitative analysis consisting of correlation and agreement analysis on the HRV data and thematic analysis on qualitative data obtained from interviews. Our results show that the electrocardiography (ECG) chest strap achieved the highest correlation and agreement levels in all sessions and had the lowest amount of artifacts, followed by the photoplethysmography (PPG) wristband, ECG sensor board kit and PPG smartwatch. In all three sessions, wrist-worn devices showed a lower amount of agreement and correlation with the reference device. Qualitative findings from interviews highlight that participants prefer wrist and arm-worn devices in terms of aesthetics, wearability, and comfort, followed by chest-worn devices. Moreover, participants mentioned that the latter are more likely to invite social judgment from others, and they would not want to wear it in public. Participants preferred the chest strap for short-term use and the wrist and arm-worn sensors over long-time.
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Papers by Niaz Chalabianloo