Towards Personal Stress Informatics: Comparing Minimally Invasive Techniques for Measuring Daily Stress in the Wild

Adams, Phil; Rabbi, Mashfiqui; Rahman, Tauhidur; Matthews, Mark; Voida, Amy; Choudhury, Tanzeem; Voida, Stephen

doi:10.4108/icst.pervasivehealth.2014.254959

Cited by 89 publications

(70 citation statements)

References 36 publications

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“…average, variance) per block. The one-minute granularity has been the standard in lab and ambulatory physiological monitoring [16, 17, 20, 21, 22, 36, 2] because this level of aggregation allows relatively robust and stable feature statistics. Using blocks of less than 1 minute increases variability, which may lead to degraded model performance.…”

Section: Modeling Overviewmentioning

confidence: 99%

See 1 more Smart Citation

cStress

Hovsepian

Al’Absi

Ertin

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

274

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Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wearable sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collection, screening, cleaning, filtering, feature computation, normalization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently collected data sets — in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5% on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.

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Section: Modeling Overviewmentioning

confidence: 99%

“…Fortunately, wearable sensors have progressed to the point that they can continuously measure physiology and wirelessly stream the data to a smartphone for real-time analysis. This, coupled with computational modeling advances, has led to several recent works on continuous measurement of stress in the mobile environment [22, 36, 2]. …”

Section: Introductionmentioning

confidence: 99%

cStress

Hovsepian

Al’Absi

Ertin

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

274

View full text Add to dashboard Cite

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“…These phenomena have been documented in numerous studies evaluating the measurable stress responses in humans during driving, public speaking, or simple everyday activities that people report as stressful [6][7][8]. These responses are notably less dramatic that those associated with drug administration.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Mobile Detection of Drug Use with Wearable Biosensors: A Pilot Study

et al. 2014

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While reliable detection of illicit drug use is paramount to the field of addiction, current methods involving selfreport and urine drug screens have substantial limitations that hinder their utility. Wearable biosensors may fill a void by providing valuable objective data regarding the timing and contexts of drug use. This is a preliminary observational study of four emergency department patients receiving parenteral opioids and one individual using cocaine in a natural environment. A portable biosensor was placed on the inner wrist of each subject, to continuously measure electrodermal activity (EDA), skin temperature, and acceleration. Data were continuously recorded for at least 5 min prior to drug administration, during administration, and for at least 30 min afterward. Overall trends in biophysiometric parameters were assessed. Injection of opioids and cocaine use were associated with rises in EDA. Cocaine injection was also associated with a decrease in skin temperature. Opioid tolerance appeared to be associated with a blunted physiologic response as measured by the biosensor. Laterality may be an important factor, as magnitude of response varied between dominant and nondominant wrists in a single patient with bilateral wrist measurements. Changes in EDA and skin temperature are temporally associated with intravenous administration of opioids and cocaine; the intensity of response, however, may vary depending on history and extent of prior use.

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“…Researchers have used various sensors to monitor changes and predict trends of physiological and physical signals [4, 30]. Wearable sensing technologies that detect galvanic skin response (GSR) signals, movement, respiration and heart rate measurements have also been found to provide valuable physiological data on challenges like stress and anxiety [32, 2]. Recently, the StudentLife Project [55, 8] leveraged passive and active sensing techniques through the use of smartphones among college students.…”

Section: Related Workmentioning

confidence: 99%

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

Choudhury

Kumar

Weber

2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user’s behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user’s diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.

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Towards Personal Stress Informatics: Comparing Minimally Invasive Techniques for Measuring Daily Stress in the Wild

Cited by 89 publications

References 36 publications

cStress

cStress

Real-Time Mobile Detection of Drug Use with Wearable Biosensors: A Pilot Study

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

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