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FEEL: A System for Frequent Event and Electrodermal Activity Labeling

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Ayzenberg, Y. ; Picard, R.W.

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

The wide availability of low-cost wearable biophysiological sensors enables us to measure how the environment and our experiences impact our physiology. This creates a challenge: in order to interpret the longitudinal data, we require the matching contextual information as well. Collecting continuous biophysiological data makes it unfeasible to rely solely on our memory for contextual information. In this paper, we first present an architecture and implementation of a system for the acquisition, processing, and visualization of biophysiological signals and contextual information. Next, we present the results of a user study: users wore electrodermal activity wrist sensors that measured their autonomic arousal. These users uploaded the sensor data at the end of each day. At first, they annotated their events at the end of each day; then, after a two-day break, they annotated the data from two days earlier. One group of users had access to both the signal and the contextual information collected by the mobile phone and the other group could only access the biophysiological signal. At the end of the study, the users filled in a system usability scale and user experience surveys. Our results show that the system enables the users to annotate biophysiological signals at a greater effectiveness than the current state of the art while also providing very good usability.

Read more at IEEE Xplore

Tags: Context, mobile computing, pervasive computing, wearable sensors

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