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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

Signal processing chain: from raw sensor data to the final stress score
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Related In: Results  -  Collection


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Fig3: Signal processing chain: from raw sensor data to the final stress score

Mentions: An overview of the signal processing chain of the app is depicted in Fig. 3. In order to reduce the battery consumption, the accelerometer and the GPS sensor are sensed only every 5 min for 30 s. The microphone signal is accessed as part of the self-assessment questions. The contacts (address book) and the calender events are read once before the data are uploaded to the web server. The current battery level is stored in 5 % intervals, and a call event is registered as soon as the phone call happens. The computed RR intervals on the Wahoo device are sent in real time to the smartphone which then continuously stores the values locally during the whole night.Fig. 3


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

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

Signal processing chain: from raw sensor data to the final stress score
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Signal processing chain: from raw sensor data to the final stress score
Mentions: An overview of the signal processing chain of the app is depicted in Fig. 3. In order to reduce the battery consumption, the accelerometer and the GPS sensor are sensed only every 5 min for 30 s. The microphone signal is accessed as part of the self-assessment questions. The contacts (address book) and the calender events are read once before the data are uploaded to the web server. The current battery level is stored in 5 % intervals, and a call event is registered as soon as the phone call happens. The computed RR intervals on the Wahoo device are sent in real time to the smartphone which then continuously stores the values locally during the whole night.Fig. 3

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