What question did this study set out to answer?

The aim is to identify physiological and behavioral markers for stress using various data collection methods.

September 1, 2013

Stress Recognition Using Wearable Sensors and Mobile Phones

Key Points

The aim is to identify physiological and behavioral markers for stress using various data collection methods.
Collected 5 days of data from 18 participants using wrist sensors and mobile phone usage data.
Applied correlation analysis and machine learning for stress classification based on collected data.
Compared accuracy of machine learning classification to baseline survey accuracy of 87.5%.
Achieved over 75% accuracy in stress classification using mobile data features.
Correlation analysis indicated higher stress levels were linked to increased activity levels and SMS usage.
Some mobile data features outperformed the baseline accuracy in classifying stress.

Abstract

In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.

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Stress Recognition Using Wearable Sensors and Mobile Phones

Key Points

Abstract

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