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.
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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
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
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01 May 2017
TL;DR: The iCare-stress application was created to assist a person to know his/her own state of stress and handle properly using the user's brainwave detected by Emotiv device as discussed by the authors.
Abstract: The human life becomes increasingly stressful and not everyone can manage his/her own life well. Most people are not aware of stress even though stress is a common illness that impacts on daily life, including family, relationships, and studying. Moreover, stress affects health, both physically and mentally at all ages. When people suffer from stress repeatedly, stress will turn to be multiple physical conditions and psychological problems such as anxiety and depression. iCare-stress application was created to assist a person to know his/her own state of stress and handle properly using the user's brainwave detected by Emotiv device. In addition, the application provides an interesting technique called Neurofeedback which is used to train human brain activity. Neurofeedback is widely accepted in medicinal studies to reduce the symptoms of many diseases. Therefore, it is chosen to be the technique to manage and reduce stress in the application. Moreover, there are three interventions for doing the different activities such as Concentration, Relaxation, and Music Therapy. In the case of measuring stress without the device, the application provides a set of questionnaire which is called General Health Questionnaire or GHQ that a user can apply to check his/her stress level. The result from both doing interventions and GHQ are shown in the form of human language that anyone can understand.
7 citations
01 Aug 2017
TL;DR: In this article, the authors have focused on the parameter heart rate variability (HRV) as a main approach for detecting stress and analyzed it by using time domain, frequency domain method as well as geometrical method.
Abstract: Mental stress is common in today's world and it influences many physiological functions of the body predominantly the cardiovascular system. This stress detection is very important because it devotes to diverse pathological changes including sudden cardiac death, myocardial infarction and wall motion abnormalities etc. There are various parameters on which stress can be identified. Here we have taken the stress database of person who is in stress before the day of his seminar presentation and examination. In this paper we have focused on the parameter heart rate variability (HRV) as a main approach for detecting stress. Heart rate variability (HRV) analyzed by using time domain, frequency domain method as well as geometrical method. From Time domain method, the features like SDRR (The RR intervals standard deviation), RMSSD (The square root of the mean of the sum of the squares of differences between consecutive RR interval's) and pRR50 (RR50 count divided by the total number of all RR intervals) were calculated. And Frequency domain features have determined by calculating power spectral density of HRV and the spectral response divided into three main components that are, very low frequency (VLF) component, low frequency (LF) component and high frequency (HF) component. Whereas the geometrical features are obtained from poincare plot.
6 citations