Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

Figure 1. Recorded physiological signals before and after the start of the stimuli. The increase in EDA signal level and number of peaks and irregularities and sudden increases in HRV can be seen in this figure.

Figure 2. The block diagram of the stress level detection system for Samsung Gear S and S2 and Empatica E4. Since the sensors and platforms are different, please note that EDA and temperature signals are only available for E4.

Figure 3. The example filtered EDA signal according to changes in the accelerometer signal. Note that red components were deleted because of the high activity intensity.

Figure 4. Activity intensity is shown by using the accelerometer sensor X, Y, and Z components corresponding to the example EDA signal in Figure 3 Note that this example was recorded during a highly intensive activity.

Figure 5. Decomposed EDA Signal from Empatica E4 wristband by applying cvxEDA tool.

Figure 6. Gaps due to movement and loosely worn wristband from PPG (Photoplethysmography) data (Left) are filled with cubic interpolation function (Right).

Figure 7. A view of smartwatches and wristbands after data extraction, charged and ready to use.

Figure 8. The daily schedule and data collection procedure during the algorithmic programming contest.

Figure 9. Percentage of the remaining data (for both device types) after the artifacts are removed versus different percentage thresholds of artifact detection.