Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

Abstract

Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.

Keywords: Intervention; Mobile Health (mHealth); Stress Management.

Figures

Figure 1

ECG RR interval decreases due to activity which recovers exponentially during stationary period.

Figure 2

Heart-rate increases due to activity. Exponential recovery parameter τ is learnt for each participant. 99% exponential recovery curve (equation 1) is shown. Before the heart rate is recovered another activity happened. So baseline heart rate is carry forwarded.

Figure 3

F1 score between self-report and sensor assessment range from 0.130 to 0.917 with median 0.717. Bottom 5 have unacceptable self-report consistency score with median cronbach’s alpha score 0.335 while overall consistency score is 0.843.

Figure 4

Timing of just-in-time stress intervention for momentary and significant stress episode. Starting of a rectangular region indicates precise proactive intervention timings generated by MACD.

Figure 5

The likelihood of stress follow beta distribution with shape parameter α = 0.222 and β = 1.027. Significant stress threshold is 0.782 (p=0.95).

Figure 6

Stress episode with high likelihood of stress (95th percentile) (see figure 5) and a duration of more than duration threshold is marked as a significant stress episode. For a duration threshold 7.3 minute leads to one expected significant stressful episode per day (10+ hours of sensor wearing time).

Figure 7

Next stress duration as a function of current stress duration. Surprisingly, the correlation observed here is 0.4243.

Figure 8

(a) Overall participants stress. We observe that there exist wide between person variation. (b) Day wise stress for the participant with maximum stress density. We observe that there exist wide between day variation.

Figure 9

Role of temporal and activity on stress density. Here morning is defined as before 8 AM, day time as 8 AM to 7 PM, and night as after 7 PM. Red line represents the overall stress density.

Figure 10

Effect on stress density across different location contexts detected with κ > 0.7. Noisy environment is highly associated with stress.

Figure 11

The likelihood of stress for one participant overlaid on the disorder map. Disorder here is the aggregated posterior probability value for top 10 NIfETy variables (see figure 10) with κ > 0:70.

Figure 12

Tradeoff analysis for triggering frequency of stress intervention. The x-axis represents model proposed triggering frequency of stress intervention per day and two y-axes represent precision and recall for predicting SSEs.