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Book ChapterDOI

Novel Approach for Stress Detection Using Smartphone and E4 Device

30 Jul 2019-pp 736-745
TL;DR: A novel methodology is proposed by creating a personalized model from the generalized model for stress detection to give more accuracy as two devices are used with the novel approach of model building.
Abstract: Stress reduction is important for maintaining overall human health. There are different methodologies for detecting stress including clinical tests, traditional methods and various sensors and systems developed using either a smartphone, wearable devices or sensors connected to the human body. In this paper, a novel methodology is proposed by creating a personalized model from the generalized model because stress differs from person to person for the same work profile. A generalized model for stress detection is developed from smartphone and E4 device data of all the available individuals. A generalized model is used to build a personalized model that is a person-specific model and will be build up over a time of time when enough amount of person-specific data gets collected. This proposed methodology intends to give more accuracy as two devices are used with the novel approach of model building. Various machine learning algorithms such as ANN, xgboost, and SVM are implemented with the E4 device dataset while the LASSO regression model is used for smartphone data. ANN worked best than xgboost and SVM with 93.71% accuracy. In LASSO, 0.6556 RMSE is achieved.
Citations
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Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors designed two classes of controllers: (1) an inhibitory controller for reducing cognitive stress and (2) an excitatory controller for increasing cognitive stress.
Abstract: Keeping cognitive stress at a healthy range can improve the overall quality of life: helping subjects to decrease their high levels of arousal, which will make them relaxed, and elevate their low levels of arousal, which could increase their engagement. With recent advances in wearable technologies, collected skin conductance data provides us with valuable information regarding ones’ cognitive stress-related state. In this research, we aim to create a simulation environment to control a cognitive stress-related state in a closed-loop manner. Toward this goal, by analyzing the collected skin conductance data from different subjects, we model skin conductance response events as a function of simulated environmental stimuli associated with cognitive stress and relaxation. Then, we estimate the hidden stress-related state by employing Bayesian filtering. Finally, we design a fuzzy control structure to close the loop in the simulation environment. Particularly, we design two classes of controllers: (1) an inhibitory controller for reducing cognitive stress and (2) an excitatory controller for increasing cognitive stress. We extend our previous work by implementing the proposed approach on multiple subjects’ profiles. Final results confirm that our simulated skin conductance responses are in agreement with experimental data. In a simulation study based on experimental data, we illustrate the feasibility of designing both excitatory and inhibitory closed-loop wearable-machine interface architectures to regulate the estimated cognitive stress state. Due to the increased ubiquity of wearable devices capable of measuring cognitive stress-related variables, the proposed architecture is an initial step to treating cognitive disorders using non-invasive brain state decoding.

8 citations

Proceedings ArticleDOI
27 Jun 2020
TL;DR: This paper mainly investigates whether physiological data can be considered and used as a form of implicit user feedback, and highlights the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicituser feedback.
Abstract: Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identify some factors and activities as emotional triggers, which were used for understanding the user context. Our results highlight the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicit user feedback.

3 citations

References
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Journal ArticleDOI
TL;DR: Data from the smartphone's built-in accelerometer is used to detect behavior that correlates with subjects stress levels, and a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models are achieved, relying solely on data from a single accelerometer.
Abstract: Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.

182 citations

Proceedings ArticleDOI
09 Oct 2016
TL;DR: The main results point out that the Empatica E4 wristband had a significant loss in terms of detected interbeat intervals, but that time-domain features such as the mean heart rate and standard deviation of the heart rate were still well estimated, with good stress discrimination power.
Abstract: To test the stress detection performance of a wearable sensor system, the signals related to heart activity and electrodermal activity of the Empatica E4 wristband have been compared to stationary electrocardiogram and finger skin conductivity electrodes of high sampling rates during the classical laboratory stress protocol Trier Social Stress Test. The comparison has been done on both signal level and in terms of features for stress detection on a total of seven subjects. The main results point out that the Empatica E4 wristband had a significant loss in terms of detected interbeat intervals, but that time-domain features such as the mean heart rate and standard deviation of the heart rate were still well estimated, with good stress discrimination power. Furthermore, the skin conductivity signals measured at different locations (wrist versus finger) show no visual resemblance and it appears that the signal from the Empatica E4 wristband yielded higher stress discrimination power than the signal measured at the fingers.

100 citations

Journal ArticleDOI
TL;DR: An attempt is made to determine the best feature set that results in maximum classification accuracy and the result indicates feature vector with best features having a strong influence in stress identification.

98 citations

Journal ArticleDOI
01 Oct 2017
TL;DR: An automatic stress detection and alleviation system, called SoDA, that takes advantage of emerging wearable medical sensors, specifically, electrocardiogram, galvanic skin response, respiration rate, blood pressure, and blood oximeter, to continuously monitor human stress levels and mitigate stress as it arises is presented.
Abstract: Long-term exposure to stress may lead to serious health problems such as those related to the immune, cardiovascular, and endocrine systems Once having arisen, these problems require a considerable investment of time and money to recover from With early detection and treatment, however, these health problems may be nipped in the bud, thus improving quality of life We present an automatic stress detection and alleviation system, called SoDA, to address this issue SoDA takes advantage of emerging wearable medical sensors (WMSs), specifically, electrocardiogram (ECG), galvanic skin response (GSR), respiration rate, blood pressure, and blood oximeter, to continuously monitor human stress levels and mitigate stress as it arises It performs stress detection and alleviation in a user-transparent manner, ie, without the need for user intervention When it detects stress, SoDA employs a stress alleviation technique in an adaptive manner based on the stress response of the user We establish the effectiveness of the proposed system through a detailed analysis of data collected from 32 participants A total of four stressors and three stress reduction techniques are employed In the stress detection stage, SoDA achieves 958 percent accuracy with a distinct combination of supervised feature selection and unsupervised dimensionality reduction In the stress alleviation stage, we compare SoDA with the ‘no alleviation’ baseline and validate its efficacy in responding to and alleviating stress

85 citations

Journal ArticleDOI
TL;DR: With the emergence of the Internet of Things, devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time.
Abstract: In today's world, many people feel stressed out from school, work, or other life events Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time Additionally, there is added convenience via the connectivity and portability of these IoT devices They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life

71 citations