Stress Detection Using Smart Phone Data
Dimitri Konstantas2016, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
In today's society, working environments are becoming more stressful. The problem of occupational stress is generally recognized as one of the major factors leading to a wide spectrum of health problems. However work should, ideally, be a source of health, pride and happiness, in the sense of enhancing motivation and strengthening personal development. In this work, we present StayActive, a system which aims to detect stress and burnout risks by analyzing the behaviour of the users via their smartphone. The main purpose of StayActive is the use of the mobile sensor technology for detecting stress. Then a mobile service can recommend and present various relaxation activities "just in time" in order to allow users to carry out and solve everyday tasks and problems at work. In particular, we collect data from people's daily phone usage gathering information about the sleeping pattern, the social interaction and the physical activity of the user. We assign a weight factor to each of these three dimensions of wellbeing according to the user's personal perception and build a stress detection system. We evaluate our system in a real world environment with young adults and people working in the transportation company of Geneva. This paper highlights the architecture and model of this innovative stress detection system. The main innovation of this work is addressed in the fact that the way the stress level is computed is as less invasive as possible for the users.
Related papers
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep
BioNanoScience, 2013
Work should be a source of health, pride, and happiness, in the sense of enhancing motivation and strengthening personal development. Healthy and motivated employees perform better and remain loyal to the company for a longer time. But, when the person constantly experiences high workload over a longer period of time and is not able to recover, then work may lead to prolonged negative effects and might cause serious illnesses like chronic stress disease. In this work, we present a solution for assessing the stress experience of people, using features derived from smartphones and wearable chest belts. In particular, we use information from audio, physical activity, and communication data collected during workday and heart rate variability data collected at night during sleep to build multinomial logistic regression models. We evaluate our system in a real work environment and in daily-routine scenarios of 35 employees over a period of 4 months and apply the leave-one-day-out cross-validation method for each user individually to estimate the prediction accuracy. Using only smartphone features, we get an accuracy of 55 %, and using only heart rate variability features, we get an accuracy of 59 %. The combination of all features leads to a rate of 61 % for a three-stress level (low, moderate, and high perceived stress) classification problem.
Stress Detection Using Smartphone and Wearable Devices: A Review
ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY
Stress is the mental condition of the human body that causes it's dis-functioning. It affects adversely on body parts resulting in health disorders. Traditional method of stress detection includes lab tests done by doctor. Besides traditional techniques, sensors are used to measure physiological signals, as these signals make it easy to detect stress. Based on techniques of data collection, this paper is divided into two types, one for In-lab experiment, in which participants wear various sensors on their body which is invasive for real time application while in second, data was collected from sensors which are already available in the handy devices of participant such as smartphone, wearable devices etc. Different types of sensors and their uses are explained in this paper. Automatic real time stress detection systems can be developed. This paper lists various algorithms used to gain more accuracy in detecting stress. This paper is helpful for the fellow researchers who will be working on automatic stress detection. Various studies in this domain have been reviewed and this is a primary effort in summarizing the highlights of the previous research done in stress detection domain.
A novel mobile platform for stress prediction
Proceedings of the 2022 Workshop on Emerging Devices for Digital Biomarkers
Public health surveillance is typically done through self-report surveys. Personal smart devices that collect near real-time and zeroeffort health data can support traditional surveillance efforts by providing novel and diverse data, which can be used to predict the prevalence of conditions in a population using advanced analytics. Apple Health is one of the most popular sources of personal health data, supporting a variety of devices that collect a wide range of information from heart rate to blood pressure and sleep. This paper introduces a mobile health platform that extracts Apple Health data to support public health monitoring based on personal devices, as well as a protocol for a study that utilizes this platform to predict stress in a population. Preliminary results are also presented: Random Forests and Support Vector Machines are used to predict the participant's stress levels and achieved an accuracy of 85% and 70%, respectively. Implications for public health, challenges, limitations, and future work are also discussed. The system described in this paper is one of the first works to leverage health data from consumer-level personal devices for public health.
Unobtrusive stress detection on the basis of smartphone usage data
Personal and Ubiquitous Computing, 2018
Stress has become an important health problem, but existing stress detectors are inconvenient in long-term real-life use because users either have to wear dedicated devices or expend notable interaction efforts in system adaptation to specifics of each person. Adaptation is necessary because individuals significantly differ in their perception of stress and stress responses, but typical adaptation employs supervised learning methods and hence requires fairly large sets of labelled data (i.e. information on whether each reporting period was stressful or not) from every user. To address these problems, we propose a novel unsupervised stress detector, based on using a smartphone as the only device and using discrete hidden Markov models (HMM) with maximum posterior marginal (MPM) decisions for analysis of phone data. Our detector requires neither additional hardware nor data labelling and hence is truly unobtrusive and suitable for lifelong use. Its accuracy was evaluated using two real-life datasets: in the first case, adaptation was based on very short (a few days) phone interaction histories of each individual, and in the second case-on longer histories. In these tests, the proposed HMM-MPM achieved 59 and 70% accuracies, respectively, which is comparable with results of fully supervised methods, reported by other works.
Stress Monitoring Using Mobile Phone and Wearable Technology: Stress Catcher
Journal of Telecommunication, Electronic and Computer Engineering, 2017
Individuals nowadays suffered from stress due to high workload from works or studies. However, most of them could not identify their stress level or some of them did not even know that they were exposed to consideration amount of stress. A study was carried out in order to study the mobile application and wearable technology towards the development of stress monitoring application, namely as ‘Stress Catcher’. Through the study, stress-monitoring application was developed based on users’ heart beat rate and users’ perception was evaluated to see how people reacted towards the application. In order to develop the stress monitoring application, Mobile-D methodology was applied. After the study, the stress monitoring application was expected to measure users’ heart beat rate and compared to the heart rate from the signal of pulse that sent to the wearable device, which was Mio Alpha. Mobile application will display the stress level through the display screen. It was hope that Stress Cat...
Stress Detection Smartwatch
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
When a person is unable to handle their circumstances, responsibilities, and workload, stress is a natural emotion that is produced. A person's physical and mental health may suffer when the body is triggered, which can be deadly. The physical impacts of stress on a person's body can include an increase in blood pressure, a rapid heartbeat, increased muscle tension, headaches, a decrease in bodily immunity functions, and a decrease in sleepiness, among other things. The latest technology, known as smartwatches, provides the user with easy access to mobile features. Users can employ the stress-detecting capabilities of high-end smartwatches. Although they can be used to understand things better, these stress applications for smartwatches are not precise in how they operate. Heart rate variability, or HRV, is used by smartwatches and involves the intervals between each heartbeat that the sensor records. A person who has a low HRV is likely under stress. Although stress applications may not be as precise as medical equipment, they are dependable when necessary because there is a good likelihood that the data is accurate. An Electro Dermal Activity (EDA) sensor, found in some smartwatches, monitors tension by electrically altering the amount of sweat on our skin. You must spend two minutes with your palm on the watch dial to achieve the same. As an increase in heart rate is a direct outcome of stress, stress is recognized in the project utilizing heart rate. Since it is also an immediate outcome of stress, heart rate is used in the implementation. In this sector, mobile applications give users a way to explore this data graphically or in greater detail. The user of mobile applications might utilize them for medical purposes and to understand the data.
Designing a mobile system for coping with stress
In the Affective Health project we explore mobile services that empowers people to monitor and understand their own stress levels vis-à-vis their everyday activities. Our design aims to create open surfaces for users to interpret, appropriate and change over time, making the look and experience of the system their own, even after it has been deployed, letting the participatory process continue where PD traditionally leaves. Here we discuss our design process and the problem of getting design input from a sensitive and hard to reach target group. We present the ways we worked around the problems, the questions that arose, and thoughts we have for our future work.
TECHNICAL CONTRIBUTION Personalized Stress Management: Enabling Stress Monitoring
2016
Stress is one of the major triggers for many diseases. Improving stress balance is therefore an important prevention step. With advances in wearable sensors, it becomes possible to continuously monitor and analyse user’s behavior and arousal in an unobtrusive way. In this paper, we report on a case study in which users (21 teachers of a vocational school) were provided with wearable sen-sors and could view their arousal information put in the context of their life events during the period of four weeks using our software tool in an unsupervised setting. The goal was to evaluate user engagement and enabling of self-coaching abilities. Our results show that users actively explored their arousal data during the study. Further qualitative evaluation conducted with 15 of 21 users indi-cated that 12 of 15 users were able to learn about their stress patterns based on the information they obtained, but only 5 of them were able to come up with practical inter-ventions for improving their str...
mStress: Supporting Continuous Collection of Objective and Subjective Measures of Psychosocial Stress on Mobile Devices
2010
Excessive, chronic, and repeated exposure to psychological stress can lead to significant health problems. However, new methods for better coping with stress that could significantly improve health and quality of life, cannot be developed and evaluated without scientifically valid datasets describing the experience of stress in everyday life. In prior research, scientifically valid datasets have been difficult to capture from natural environments. Sensors, which continuously capture objective information about physiology and behavior, are prone to noise and failure. In addition, aspects of everyday life (e.g., conversation, exercise, etc.) interfere with the physiological response to stress, making it difficult to tease out the effect of stress from changes in physiology. To overcome the challenges of assessing both exposures and responses to stressful events, new wireless sensing systems are needed to capture scientifically valid datasets describing the experience of stress in natural environments.
Can Smartphones Detect Stress-Related Changes in the Behaviour of Individuals
—We describe initial results from an ongoing project to use mobile phone sensors to detect stress related situations 1. The questions that we address in this stage of our work in progress is whether differences between stressful and non stressful periods can be detected in information readily available on a smartphone such as location traces, BlueTooth devices seen during the day and phone call patterns. We present an experiment with 7 students who were monitored during a two week exam session (stressful situation) and the two following weeks (non stressful period). The results show that a behaviour modification can clearly be seen, although the exact interpretation and generalization requires further work and larger scale experiments.
Related topics
Related papers
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Proceedings of the ACM International Conference on Multimedia - MM '14, 2014
Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced lowdimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
Stress catcher application for mobile stress monitoring using questionnaire-based
Indonesian Journal of Electrical Engineering and Computer Science, 2019
Nowadays, stress has become the main reason to cause health problems. The human’s lifestyle has been increasing due to the fast development of technologies which help to improve performance and productivity indirectly increased the burden of human lifestyle. Many studies have done to identify the cause of stress and the effect of stress among university students. However, stress monitoring is not well mentioned in the previous works especially stress monitoring with questionnaire-based. Thus, this research tried to come out with a mobile application that fit to use to monitor the stress level by using a questionnaire. Mobile-D used to identify and develop the mobile application, namely as Stress Catcher. Mobile-D approach allows Test-driven development and it is suitable to use for mobile applications development. A prototype of Stress Catcher will function to prove the usefulness in human lifestyle.
HealthyOffice: Mood Recognition At Work Using Smartphones and Wearable Sensors
—Stress, anxiety and depression in the workplace are detrimental to human health and productivity with significant financial implications. Recent research in this area has focused on the use of sensor technologies, including smartphones and wearables embedded with physiological and movement sensors. In this work, we explore the possibility of using such devices for mood recognition, focusing on work environments. We propose a novel mood recognition framework that is able to identify five intensity levels for eight different types of moods every two hours. We further present a smartphone app ('HealthyOffice'), designed to facilitate self-reporting in a structured manner and provide our model with the ground truth. We evaluate our system in a small-scale user study where wearable sensing data is collected in an office environment. Our experiments exhibit promising results allowing us to reliably recognize various classes of perceived moods.
Design of a Mobile Phone App Prototype for Reflections on Perceived Stress
2012
In working life today, people often experience high levels of stress and display strong reactions to different stressors. Those who are exposed to high levels of stress for a long time face an increased risk for deterioration in physical and mental health often leading to sick leave and high consumption of healthcare. To prevent this, continuous support is needed. Development of IT-tools for continuous stress management is, however, in its early stages. We present a prototype of a mobile phone app for self-reflection, a tool that is also integrated in a larger web based stress management system built on research of social networks for increased well-being. The mobile phone app aims at helping people become more aware of patterns of stressful events. It logs and displays basic information about self-perceived stress situations, such as location and time, and the user can add information about the situations and perceived stress levels. As the app constitutes a part of the web based stress management system, not only self-reflection but also reflection together with peers and stress experts is possible. The prototype of the mobile phone app has been qualitatively evaluated using stress management criteria, and the paper also exemplifies its utility in the context of the larger system.
Smart Technologies for Long-Term Stress Monitoring at Work
Due to the growing pace of life, stress became one of the major factors causing health problems. We have developed a framework for measuring stress in real-life conditions continuously and unobtrusively. In order to provide meaningful, useful and actionable information, we present stress information, derived from sensor measurements, in the context of person's activities. In this paper, we describe our framework, discuss how we address arising challenges and evaluate our approach on basis of the field studies we have conducted. The main results of the evaluation are that the results of long-term measurements of stress reveal people information about their behavioral patterns that they perceive as meaningful and useful, and trigger their ideas about behavioral changes necessary to achieve a better stress balance.
A User-Friendly Tool for Detecting the Stress Level in a Person’s Daily Life
Springer eBooks, 2011
Mental health care represents over a third of the cost of health care to all EU nations and, in USA, it is estimated to be around the 2.5% of the gross national product. Depression and Stress related disorders are the most common mental illnesses. The European project OPTIMI will develop tools to make predictions through the early identification on the onset of the disease. In this paper, we present a user-friendly application developed in the OPTIMI project to detect the stress level in a person's daily life. The results of a first usability study of this application are also presented.
Behavioral and contextual data for stress analysis
2015
The spread of mobile phones in the world, increasing quantity and quality of sensors embedded in modern smartphones open up new opportunities for more individual and less obtrusive stress analysis in real-life situations. One of the aims of our research is to develop a real-life stress recognition method by measuring behavioral data and context, through gathering data from smartphones, and without the use of additional wearable sensors. The parameters collected from smartphones are audio, accelerometer, gyroscope, external lighting, screen light on/off, and self-reports (current stress level assessment). In a binary classification (stress or relax) we achieved over 81% accuracy using activity level information (accelerometer and gyroscope features) and decision tree algorithms. For a 3-class stress classification (low, medium, high) we achieved a 70% accuracy with the application of all features.
Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters
Proceedings of the 50th Hawaii International Conference on System Sciences (2017), 2017
The paper describes a mobile solution for the early recognition and management of stress based on continuous monitoring of heart rate variability (HRV) and contextual data (activity, location, etc.). A central contribution is the automatic calibration of measured HRV values to perceived stress levels during an initial learning phase where the user provides feedback when prompted by the system. This is crucial as HRV varies greatly among people. A data mining component identifies recurrent stress situations so that people can develop appropriate stress avoidance and coping strategies. A biofeedback component based on breathing exercises helps users relax. The solution is being tested by healthy volunteers before conducting a clinical study with patients after alcohol detoxification.
Towards Smartphone-Based Assessment of Burnout
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2014
In this paper, we present work in progress on VITAL-IN, a pervasive mobile application that aims to operationalize and assess multi-dimensional risk factors increasing a person's chance of developing the burnout syndrome. To date, there are no conclusive scientific results of what causes burnout, yet some factors are evident. We propose VITAL-IN application, enabling the analysis of distributed, variable order, sensor input and ecological momentary selfassessment towards "just-in-time" inference of an individual's behaviour and state, and future burnout risk prediction. Understanding the risk factors and the developmental trajectories leading to burnout could facilitate its early recognition and help to determine the most effective strategies and the most appropriate time for prevention and intervention efforts.
SmartCoping: A mobile solution for stress recognition and prevention
The paper describes the development of a mobile solution based on smartphones and sensors for the early recognition of stress. The solution is based on real-time capture and analysis of vital data such as heart rate variability as well as activity and contextual data such as location and time of day. Individual recognition patterns for stress are derived from combining vital and contextual data by using subjective stress assessments via mood maps as additional input during an initial learning phase. The reliability of stress alerts and therapeutic impact will be tested in a clinic specialised on the treatment of alcoholics since stress tends to cause craving and therefore trigger relapses
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.