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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

The implemented PANAS questionnaire reduced to 10 items: relaxed, tired, happy, stressed, concentrated, sleepy, interested, active, angry, and depressed (five PA items and five NA items). The questions are answered by moving a scrolling bar resulting in a continuous response value. The last question asks the participants to respond verbally about what he is currently doing in his native language. The voice is recorded by pressing the Record and the Stop buttons
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Related In: Results  -  Collection


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Fig2: The implemented PANAS questionnaire reduced to 10 items: relaxed, tired, happy, stressed, concentrated, sleepy, interested, active, angry, and depressed (five PA items and five NA items). The questions are answered by moving a scrolling bar resulting in a continuous response value. The last question asks the participants to respond verbally about what he is currently doing in his native language. The voice is recorded by pressing the Record and the Stop buttons

Mentions: Beside answering to the PANAS questionnaire items, the user is asked to provide a voice message in which he speaks about what he is currently doing using his native language. The voice recording is performed by pressing a start and an end button. In this way, the privacy aspect is not a critical point since the user is conscious that his voice is being recorded. The questionnaire is shown in Fig. 2.Fig. 2


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

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

The implemented PANAS questionnaire reduced to 10 items: relaxed, tired, happy, stressed, concentrated, sleepy, interested, active, angry, and depressed (five PA items and five NA items). The questions are answered by moving a scrolling bar resulting in a continuous response value. The last question asks the participants to respond verbally about what he is currently doing in his native language. The voice is recorded by pressing the Record and the Stop buttons
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: The implemented PANAS questionnaire reduced to 10 items: relaxed, tired, happy, stressed, concentrated, sleepy, interested, active, angry, and depressed (five PA items and five NA items). The questions are answered by moving a scrolling bar resulting in a continuous response value. The last question asks the participants to respond verbally about what he is currently doing in his native language. The voice is recorded by pressing the Record and the Stop buttons
Mentions: Beside answering to the PANAS questionnaire items, the user is asked to provide a voice message in which he speaks about what he is currently doing using his native language. The voice recording is performed by pressing a start and an end button. In this way, the privacy aspect is not a critical point since the user is conscious that his voice is being recorded. The questionnaire is shown in Fig. 2.Fig. 2

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