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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

One full data collection cycle and the questionnaires shown during the day
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Related In: Results  -  Collection


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Fig1: One full data collection cycle and the questionnaires shown during the day

Mentions: In general, we follow the approach of estimating changes of subjective self-perception of stress using smartphone sensor measures and information derived for the HRV signal during night. From 8 a.m. to 8 p.m., the day is divided into four sections, and randomly within each section, a notification is shown which asks the user to fill in a self-assessment questionnaire. In parallel to that, smartphone data are being collected during the day in the background. Before going to sleep, the user answers an additional stress question and puts on the Wahoo chest belt which collects HRV data during night until the next morning. After getting up, a new cycle of data collection begins. Figure 1 shows schematically one such full data collection cycle. The idea now is to use these smartphone and wearable device data to estimate the self-assessment stress score.Fig. 1


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

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

One full data collection cycle and the questionnaires shown during the day
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: One full data collection cycle and the questionnaires shown during the day
Mentions: In general, we follow the approach of estimating changes of subjective self-perception of stress using smartphone sensor measures and information derived for the HRV signal during night. From 8 a.m. to 8 p.m., the day is divided into four sections, and randomly within each section, a notification is shown which asks the user to fill in a self-assessment questionnaire. In parallel to that, smartphone data are being collected during the day in the background. Before going to sleep, the user answers an additional stress question and puts on the Wahoo chest belt which collects HRV data during night until the next morning. After getting up, a new cycle of data collection begins. Figure 1 shows schematically one such full data collection cycle. The idea now is to use these smartphone and wearable device data to estimate the self-assessment stress score.Fig. 1

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