Modeling arousal phases in daily living using wearable sensors

Abstract

In this work, we introduce methods for studying psychological arousal in naturalistic daily living. We present an activityaware arousal phase modeling approach that incorporates the additional heart rate (AHR) algorithm to estimate arousal onsets (activations) in the presence of physical activity (PA). In particular, our method filters spurious PA-induced activations from AHR activations, e.g., caused by changes in body posture, using activity primitive patterns and their distributions. Furthermore, our approach includes algorithms for estimating arousal duration and intensity, which are key to arousal assessment. We analyzed the modeling procedure in a participant study with 180 h of unconstrained daily life recordings using a multimodal wearable system comprising two acceleration sensors, a heart rate monitor, and a belt computer. We show how participants' sensor-based arousal phase estimations can be evaluated in relation to daily activity and self-report information. For example, participant-specific arousal was frequently estimated during conversations and yielded highest intensities during office work. We believe that our activity-aware arousal modeling can be used to investigate personal arousal characteristics and introduce novel options for studying human behavior in daily living.

Unobtrusive stress detection on the basis of smartphone usage data

“…To the best of our knowledge, only one work proposed stress detection methods, requiring no data labelling from the end users. Kusserow et al [16] detected arousal as the temporal deviation of current HRV values from their average values. This system employs a chest belt with physiological and accelerometer sensors, which requires careful attachment and tightening, plus an additional accelerometer on a thigh to differentiate between physical activity-induced and arousal-induced deviations, and this setup is probably too complicated for a long-term real-life use.…”

Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions

“…Automated assessment of stress in daily life, using physiological sensors, is rapidly growing [9,17,29]. Several research groups have deployed lightweight, wearable sensors in the field for that purpose [20,29].…”

“…Several research groups have deployed lightweight, wearable sensors in the field for that purpose [20,29]. Kusserow et al proposed an activity-aware stress model (from accelerometer, heart-rate monitor, and belt computer) that detects the duration and intensity of the stress in the field [17]. Plarre et al proposed a continuous measure of stress (from ECG and respiration data) that is well-correlated with stress self-reports [29].…”

Are we there yet?

“…However, long-term collection of continuous physiological data using wearable wireless physiological sensors in the free-living environment are rare as acknowledged in recent literature [26]. Amount of data collected have ranged from 23.7 hours per participant from 19 participants [18] and 24.8 hours per participant from 21 participants [40], to 45.6 hours per participant from 4 participants [26]. In contrast, we report 317 hours per participant from 40 participants.…”

“…In 2011, [40] reported an average of 24.8 hours per participant of good quality data from 21 participants over two days. Recently, [26] reported 45.6 hours per participant of physiological data collected from 4 participants. Although the amount of data collected per participant has been increasing, it is still not known whether physiological data can be collected in the natural field setting for a longer duration from a larger number of participants.…”