Stress Monitoring Using Mobile Phone and Wearable Technology: Stress Catcher (original) (raw)
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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.
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.
Mobile Based Quantitative Measure of Stress (Preprint)
2017
BACKGROUND The aim of the present study was to show the validity of a mobile based application (“Serenita”) , as a tool for measuring stress level quantitatively. In this interactive app, the user places his finger on the mobile`s camera lens, through which information related to the user’s blood flow, heart rate, and heart rate variability (HRV) is extracted. Physiological signals are then being filtered and processed through a certain machine- algorithm, resulting in a quantitative estimation of the user’s stress level. Method: a mixed sex group of 50 volunteers were recruited to participate in a standardized laboratory experiment, where a psychosocial stress protocol (Trier Social Stress Test-TSST) was implemented. Throughout the course of the experiment, physiological stress response was measured using both salivary cortisol level and Serenita app, hence, using a within subject design. Results: Serenita algorithm was able to effectively detect changes in the participant`s estima...
Stress detection and relief using wearable physiological sensors
TELKOMNIKA Telecommunication Computing Electronics and Control, 2019
The aim of the paper was to present a concept and to develop a prototype in the form of a cap which uses a combination of physiological sensors that work in concert to not only detect high stress levels in a person during his daily routine and working env ironment, but also initiate immediate relief measures. The parameters used to detect stress were compared with resting heart rate and brainwave activity to determine whether the person wearing the cap is in a stressed condition. Stress alleviation was achieved using Auditory Stimulation and a Scalp Massage. Early detection of stress and its immediate remedy or reduction can play an important role in preventing mental health disorders. In order to make the product cost effective, the concept of sensing optimum amount of data to trigger a remedial action was given more importance than extensive data collection using large number of sensors. Integrating an IOT device will further allow information to be recorded and transmitted to a caregiver/doctor to prescribe remedial action and thus prevent the condition to take a pathological form or get complicated. The detailed analysis of the collected data can help people identify the precipitating factors for stress and thus aims at reduction of stress related illnesses.
Survey on Stress Detection Using Multiple Sensors through Wearable Devices
International Journal of Advanced Trends in Computer Science and Engineering, 2021
An Individual method of living on with a daily existence it directly influences on your overall health. Since stress is the significant infection of our human body. Like depression, heart attack and mental illness. WHO says "Globally, more than 264 million people of all ages suffer from depression."[8]. Also the report says that most of the time people are stressed because of their work. 10.7% of People disorder with stress, anxiety and depression [8]. There are different method to discovering stress ex. Smart watches, chest belt, and extraordinary machine. Our principle objective is to figure out pressure progressively utilizing smart watches through their Sensor. There are different kinds of sensor available to find stress such as PPG, GSR, HRV, ECG and temperature. Smart watches contain a wide range of data through various sensor. This kind of gathered information are applied on various machine learning method. Like linear regression, SVM, KNN, decision tree. Technique have distinct, comparing accuracy and chooses best Machine learning model. This paper investigation have different analysis to find and compare accuracy by various sensors data. It is also check whether using one sensor or multiple sensors such as HRV, ECG or GSR and PPG to predict the better accuracy score for stress detection.