Stress Detection Using Low Cost Heart Rate Sensors

Abstract EN

The automated detection of stress is a central problem for ambient assisted living solutions.

The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt.

In the device validation study ( n = 5 ), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor.

With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing.

The protocol for the clinical study ( n = 46 ) included a relax phase followed by a phase with provoked mental stress, 10 minutes each.

We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal.

The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data.

Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.