Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020

TL;DR

This study demonstrates that combining multi-modal physiological data from wearable devices and personal chronicles can effectively predict college students' next-day affective states with accuracy comparable to existing methods.

Contribution

It introduces a fully automatic, multi-modal approach using commercial wearables and phones for longitudinal affect prediction in college students.

Findings

Achieved accurate next-day affect prediction over a year

Utilized multiple commercial devices for data collection

Model performance comparable to state-of-the-art methods

Abstract

Monitoring and understanding affective states are important aspects of healthy functioning and treatment of mood-based disorders. Recent advancements of ubiquitous wearable technologies have increased the reliability of such tools in detecting and accurately estimating mental states (e.g., mood, stress, etc.), offering comprehensive and continuous monitoring of individuals over time. Previous attempts to model an individual's mental state were limited to subjective approaches or the inclusion of only a few modalities (i.e., phone, watch). Thus, the goal of our study was to investigate the capacity to more accurately predict affect through a fully automatic and objective approach using multiple commercial devices. Longitudinal physiological data and daily assessments of emotions were collected from a sample of college students using smart wearables and phones for over a year. Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.