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

Data visualization of one user over 2-day time span. The size of the triangle indicates the call length
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Fig7: Data visualization of one user over 2-day time span. The size of the triangle indicates the call length

Mentions: Phone calls, questionnaire events, motion, and battery level can be visualized on a plot such as shown in Fig. 7, where data from 2 days of one exemplary user are depicted. The battery level graph indicates that there is data gap between the 2 days. When the battery level falls below 50 %, the accelerometer is disabled, and when the battery undergoes the 30 % threshold, the GPS sensor is disabled which forces the app to be inactive and to disable the data collection in the background. Data collection is reactivated when the battery exceeds that threshold again and the app is put in the foreground, by either directly opening the app or when the user clicks the next notification message which asks him to fill in the next PANAS questionnaire. Beside that, the background sensing is stopped when the user starts an HRV night session and is reactivated when the user wakes up in the morning.Fig. 7


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

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

Data visualization of one user over 2-day time span. The size of the triangle indicates the call length
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Data visualization of one user over 2-day time span. The size of the triangle indicates the call length
Mentions: Phone calls, questionnaire events, motion, and battery level can be visualized on a plot such as shown in Fig. 7, where data from 2 days of one exemplary user are depicted. The battery level graph indicates that there is data gap between the 2 days. When the battery level falls below 50 %, the accelerometer is disabled, and when the battery undergoes the 30 % threshold, the GPS sensor is disabled which forces the app to be inactive and to disable the data collection in the background. Data collection is reactivated when the battery exceeds that threshold again and the app is put in the foreground, by either directly opening the app or when the user clicks the next notification message which asks him to fill in the next PANAS questionnaire. Beside that, the background sensing is stopped when the user starts an HRV night session and is reactivated when the user wakes up in the morning.Fig. 7

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