Predict Daily Life Stress based on Heart Rate Variability

Department of Human Factors EngineeringThe purpose of this study is to investigate the feasibility of predicting a daily mental stress level from analyzing Heart Rate Variability (HRV) by using a Photoplethysmography (PPG) sensor which is integrated in the wristband-type wearable device. In this experiment, each participant was asked to measure their own PPG signals for 30 seconds, three times a day (at noon, 6 P.M, and 10 minutes before going to sleep) for a week. And 10 minutes before going to sleep, all participants were asked to self-evaluate their own daily mental stress level using Perceived Stress Scale (PSS). The recorded signals were transmitted and stored at each participant???s smartphone via Bluetooth Low Energy (BLE) communication by own-made mobile application. The preprocessing procedure was used to remove PPG signal artifacts in order to make better performance for detecting each pulse peak point at PPG signal. In this preprocessing, three- level-bandpass filtering which consisted three different pass band range bandpass filters was used. In this study, frequency domain HRV analysis feature that the ratio of low-frequency (0.04Hz ~ 0.15Hz) to high-frequency (0.15Hz ~ 0.4Hz) power value was used. In frequency domain analysis, autoregressive (AR) model was used, because this model has higher resolution than that of Fast Fourier Transform (FFT). The accuracy of this prediction was 86.35% on average of all participants. Prediction result was calculated from the leave-one-out validation. The IoT home appliances are arranged according to the result of this prediction algorithm. This arrangement is offering optimized user???s relaxation. Also, this algorithm can help acute stress disorder patients to concentrate on getting treatment.clos

Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging

Automatically monitoring and quantifying stress-induced thermal dynamic information in real-world settings is an extremely important but challenging problem. In this paper, we explore whether we can use mobile thermal imaging to measure the rich physiological cues of mental stress that can be deduced from a person's nose temperature. To answer this question we build i) a framework for monitoring nasal thermal variable patterns continuously and ii) a novel set of thermal variability metrics to capture a richness of the dynamic information. We evaluated our approach in a series of studies including laboratory-based psychosocial stress-induction tasks and real-world factory settings. We demonstrate our approach has the potential for assessing stress responses beyond controlled laboratory settings

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

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 low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

A Data Fusion System to Study Synchronization in Social Activities

As the world population gets older, the healthcare system must be adapted, among others by providing continuous health monitoring at home and in the city. The social activities have a significant role in everyone health status. Hence, this paper proposes a system to perform a data fusion of signals sampled on several subjects during social activities. This study implies the time synchronization of data coming from several sensors whether these are embedded on people or integrated in the environment. The data fusion is applied to several experiments including physical, cognitive and rest activities, with social aspects. The simultaneous and continuous analysis of four subjects cardiac activity and GPS coordinates provides a new way to distinguish different collaborative activities comparing the measurements between the subjects and along time.Comment: Healthcom 201

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

Can wearable devices reduce burnout by making people aware of stress?

Wearable fitness technology is advancing in its capabilities. Every new sensor collects new health data, and it becomes important to study how effectively this data can be utilized to help people lead healthier lives. The American Psychological Association found that Americans live with stress levels higher than what is considered healthy. Poorly managed stress can lead to burnout, which leads to unproductive workers. Burnout is known to cost businesses considerable money. The goal of this research study was to determine if burnout could be reduced through the use of a consumer wearable device along with smartphone apps that alerted wearers of their stress levels. Thirteen undergraduate students served as research subjects. They each used a wearable fitness band in conjunction with two Android mobile applications that enabled continuous stress monitoring. The data collected from the students was analyzed using a mixed methodology. The results suggested that the experiment was effective in making students more aware of their stress levels. Larger studies are recommended to determine if similar results would be realized

Wearable and app-based resilience modelling in employees:exploring the possibilities to model psychological resilience using wearable-measured heart rate variability and sleep

Stress has a major impact on both an individual and a societal level. Early recognition of the negative impact of stress or reduced resilience can be used in personalized interventions that enable the user to break the identified pattern through timely feedback, and thus limit the emergence of stress-related problems. The emergence of wearable sensor technology makes it possible to continuously monitor relevant behavioral and physical parameters such as sleep and heart rate variability (HRV). Sleep and HRV have been linked to stress and resilience in population studies, but knowledge on whether these relationships also apply within individuals, which is necessary for the aforementioned personalization, is lacking. This thesis introduces a cyclical conceptual model for resilience and four observational studies that test relationships between sleep, HRV and subjective resilience-related outcomes within participants using different types of data analysis at different timeframes. The relationships from the conceptual model and the related hypotheses are broadly confirmed in these studies. Participants tended to have more favorable subjective stress- and resilience-related outcomes on days with a relatively high resting HRV or long total sleep duration. Also, having a resting HRV that fluctuates relatively little from day to day was related to less stress and somatization. However, the strength of the relationships found was modest. The current findings can therefore not yet be directly implemented to initiate meaningful feedback, but they do provide starting points for future research and take a relevant step towards the possible future development of automated resilience interventions