Limits...
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep.

Muaremi A, Arnrich B, Tröster G - Bionanoscience (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Wearable Computing Lab, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

An exemplary profile of LTS over a period of 2 months
© Copyright Policy - OpenAccess
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4269214&req=5

Fig6: An exemplary profile of LTS over a period of 2 months

Mentions: The long-term stress score LTS is a continuous value between 0 and 1 and estimates the chronic stress level of a person. Using a first-order low-pass filter, LTS at day d is updated according to the rule8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \text{LTS}_{d+1} = \text{LTS}_{d} + \alpha \cdot (c([\textbf{x}_{\text{P}}, \textbf{x}_{\text{H}}])/2- \text{LTS}_{d}), $$\end{document}with the filter coefficient indicating the maximum change of LTS that may occur from day d to . is the output class of the common logit model using as input all features at day . If either of the modalities is missing, then is reduced to or . In case the common trained model is not available for the classification, then is modified to as9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ c^{*}(\textbf{x}_{\text{P}}, \textbf{x}_{\text{H}}) = \arg\max_{i\in\{1,2,3\}}{(q_i)} \in\{0,1,2\}, $$\end{document}with . The initial value LTS0 is the average of the daily stress scores DS during the training days. Figure 6 shows an exemplary profile of LTS over a period of 60 days with .Fig. 6


Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep.

Muaremi A, Arnrich B, Tröster G - Bionanoscience (2013)

An exemplary profile of LTS over a period of 2 months
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4269214&req=5

Fig6: An exemplary profile of LTS over a period of 2 months
Mentions: The long-term stress score LTS is a continuous value between 0 and 1 and estimates the chronic stress level of a person. Using a first-order low-pass filter, LTS at day d is updated according to the rule8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \text{LTS}_{d+1} = \text{LTS}_{d} + \alpha \cdot (c([\textbf{x}_{\text{P}}, \textbf{x}_{\text{H}}])/2- \text{LTS}_{d}), $$\end{document}with the filter coefficient indicating the maximum change of LTS that may occur from day d to . is the output class of the common logit model using as input all features at day . If either of the modalities is missing, then is reduced to or . In case the common trained model is not available for the classification, then is modified to as9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ c^{*}(\textbf{x}_{\text{P}}, \textbf{x}_{\text{H}}) = \arg\max_{i\in\{1,2,3\}}{(q_i)} \in\{0,1,2\}, $$\end{document}with . The initial value LTS0 is the average of the daily stress scores DS during the training days. Figure 6 shows an exemplary profile of LTS over a period of 60 days with .Fig. 6

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Wearable Computing Lab, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus