EmoSense: Revealing True Emotions Through Microgestures

Abstract

Stress is a universally ubiquitous emotional state that takes place everywhere and microgestures (MGs) have been verified to indicate more accurate hidden emotions. However, only limited studies attempted to explore how MGs could reflect stress levels. Herein, EmoSense, an emerging technology for wearable systems containing a three‐layer stress detection mechanism, is proposed: 1) converting the MGs into digital signals; 2) training a machine learning‐based MG detection model; and 3) configuring the stress level based on the MG frequency. To detect the MGs, the swept frequency capacitive sensing technology to is adopted capture the MG signals and the random forest model to detect the MGs effectively is applied. 16 participants are recruited in the pilot study to verify the correlation between stress level and MG frequency. The experimental results further verify that stress level is highly related to other negative emotions that should be studied while handling high stress levels.

Keywords

• emotions
• human–computer interactions
• machine learning
• microgestures
• stress detection