Affect sensing from smartphones through touch and motion contexts

Affect state of a person has an impact on the intellectual processes that control human behavior. Experiencing negative affect escalates mental problems, and experiencing positive affect states improve imaginative reasoning and thereby enhances one’s behavior and discipline. Hence, this work centers around affect acknowledgment from typing-based context data during the pandemic. In this paper, we present a novel sensing scheme that perceives one’s affect state from their unique contexts. We also aim to study how affect states vary in smartphone users during the pandemic. We collected data from 52 participants over 2 months with an Android application. We exploited the Circumplex Model of Affect (CMA) to infer 25 affect states, leveraging built-in motion and touch sensors on smartphones. We conducted comprehensive experiments by developing machine learning models to predict 25 states. Through our study, we observe that the states of users are heavily pertinent to one’s typing and motion contexts. A thorough evaluation shows that affect prediction model yields an F1-score of 0.90 utilizing diverse contexts. To the best of our knowledge, our work predicts the highest number of affect states (25 states) with better performance compared to state-of-the-art methods.