Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement

TL;DR

This paper presents a system for real-time collection and analysis of physiological and contextual data from wearables to improve personalized stress detection in everyday settings, addressing challenges of data labeling and user interaction.

Contribution

It introduces a novel system for collecting and analyzing multi-modal physiological data and self-reported labels in real-world environments for mental health applications.

Findings

Collected a diverse dataset of physiological signals and self-reports in daily life.

Analyzed the impact of activity and time of day on data quality and user responses.

Provided insights into user behavior and signal reliability in natural settings.

Abstract

Real-time physiological data collection and analysis play a central role in modern well-being applications. Personalized classifiers and detectors have been shown to outperform general classifiers in many contexts. However, building effective personalized classifiers in everyday settings - as opposed to controlled settings - necessitates the online collection of a labeled dataset by interacting with the user. This need leads to several challenges, ranging from building an effective system for the collection of the signals and labels, to developing strategies to interact with the user and building a dataset that represents the many user contexts that occur in daily life. Based on a stress detection use case, this paper (1) builds a system for the real-time collection and analysis of photoplethysmogram, acceleration, gyroscope, and gravity data from a wearable sensor, as well as self-reported stress labels based on Ecological Momentary Assessment (EMA), and (2) collects and analyzes a dataset to extract statistics of users' response to queries and the quality of the collected signals as a function of the context, here defined as the user's activity and the time of the day.