35 research outputs found
A Model for Using Physiological Conditions for Proactive Tourist Recommendations
Mobile proactive tourist recommender systems can support tourists by
recommending the best choice depending on different contexts related to herself
and the environment. In this paper, we propose to utilize wearable sensors to
gather health information about a tourist and use them for recommending tourist
activities. We discuss a range of wearable devices, sensors to infer
physiological conditions of the users, and exemplify the feasibility using a
popular self-quantification mobile app. Our main contribution then comprises a
data model to derive relations between the parameters measured by the wearable
sensors, such as heart rate, body temperature, blood pressure, and use them to
infer the physiological condition of a user. This model can then be used to
derive classes of tourist activities that determine which items should be
recommended
How stress affects functional near-infrared spectroscopy (fNIRS) measurements of mental workload
Recent work has demonstrated that functional Near-Infrared Spectroscopy has the potential to measure changes in Mental Workload with increasing ecological validity. It is not clear, however, whether these measurements are affected by anxiety and stress of the workload, where our informal observations see some participants enjoying the workload and succeeding in tasks, while others worry and struggle with the tasks. This research evaluated the effects of stress on fNIRS measurements and performance, using the Montreal Imaging Stress Task to manipulate the experience of stress. While our results largely support this hypothesis, our conclusions were undermined by data from the Rest condition, which indicated that Mental Workload and Stress were often higher than during tasks. We hypothesize that participants were experiencing anxiety in anticipation of subsequent stress tasks. We discuss this hypothesis and present a revised study designed to better control for this result
An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.
Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use
A physiological signal database of children with different special needs for stress recognition
This study presents a new dataset AKTIVES for evaluating the methods for stress detection and game reaction using physiological signals. We collected data from 25 children with obstetric brachial plexus injury, dyslexia, and intellectual disabilities, and typically developed children during game therapy. A wristband was used to record physiological data (blood volume pulse (BVP), electrodermal activity (EDA), and skin temperature (ST)). Furthermore, the facial expressions of children were recorded. Three experts watched the children's videos, and physiological data is labeled "Stress/No Stress" and "Reaction/No Reaction", according to the videos. The technical validation supported high-quality signals and showed consistency between the experts.Scientific and Technological Research Council of Turkey Technology and Innovation Funding Programmes Directorat
Evaluating Mental Stress Among College Students Using Heart Rate and Hand Acceleration Data Collected from Wearable Sensors
Stress is various mental health disorders including depression and anxiety
among college students. Early stress diagnosis and intervention may lower the
risk of developing mental illnesses. We examined a machine learning-based
method for identification of stress using data collected in a naturalistic
study utilizing self-reported stress as ground truth as well as physiological
data such as heart rate and hand acceleration. The study involved 54 college
students from a large campus who used wearable wrist-worn sensors and a mobile
health (mHealth) application continuously for 40 days. The app gathered
physiological data including heart rate and hand acceleration at one hertz
frequency. The application also enabled users to self-report stress by tapping
on the watch face, resulting in a time-stamped record of the self-reported
stress. We created, evaluated, and analyzed machine learning algorithms for
identifying stress episodes among college students using heart rate and
accelerometer data. The XGBoost method was the most reliable model with an AUC
of 0.64 and an accuracy of 84.5%. The standard deviation of hand acceleration,
standard deviation of heart rate, and the minimum heart rate were the most
important features for stress detection. This evidence may support the efficacy
of identifying patterns in physiological reaction to stress using smartwatch
sensors and may inform the design of future tools for real-time detection of
stress
Предварителна обработка и математически анализ на PPG сигнали
The report presents the main research trends in the preprocessing and mathematical analysis of photoplethysmographic signals. The use of PPG in recent years has grown in parallel with the deepening penetration of modern technology in people's daily lives. At the same time, the modernization of technology has led to the miniaturization of optical sensors. A detailed overview of the state of PPG technology today and the possibilities of using PPG sensors for reporting and long-term monitoring of the health of people in their daily lives is offered. The report considers methods for noise reduction in photoplethysmographic signals based on the use of discrete wavelet transform and threshold processing of the obtained coefficients. A comparison is made between the presented methods on the basis of evaluation parameters.Научното изследване е проведено като част от проекта „Изследване на приложението на нови математически методи за анализ на кардиологични данни“ № КП-06-Н22/5 от 07.12.2018 г., финансиран от фонд „научни изследвания“
Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable Sensors
Life events can dramatically affect our psychological state and work
performance. Stress, for example, has been linked to professional
dissatisfaction, increased anxiety, and workplace burnout. We explore the
impact of positive and negative life events on a number of psychological
constructs through a multi-month longitudinal study of hospital and aerospace
workers. Through causal inference, we demonstrate that positive life events
increase positive affect, while negative events increase stress, anxiety and
negative affect. While most events have a transient effect on psychological
states, major negative events, like illness or attending a funeral, can reduce
positive affect for multiple days. Next, we assess whether these events can be
detected through wearable sensors, which can cheaply and unobtrusively monitor
health-related factors. We show that these sensors paired with embedding-based
learning models can be used ``in the wild'' to capture atypical life events in
hundreds of workers across both datasets. Overall our results suggest that
automated interventions based on physiological sensing may be feasible to help
workers regulate the negative effects of life events.Comment: 10 pages, 4 figures, and 3 table