WEARS: Wearable Emotion AI with Real-time Sensor data

TL;DR

This paper introduces WEARS, a wearable emotion AI system that predicts user emotions in real-time using smartwatch sensors, achieving high accuracy with machine learning models on physiological data.

Contribution

It presents a novel framework for real-time emotion prediction using smartwatch sensors and explores the impact of different physiological features on emotion classification.

Findings

Multi-Layer Perceptron achieved 93.75% accuracy.

Sensor data like Heart Rate, Accelerometer, Gyroscope influence mood detection.

Real-time emotion prediction from wearable sensors is feasible.

Abstract

Emotion prediction is the field of study to understand human emotions. Existing methods focus on modalities like text, audio, facial expressions, etc., which could be private to the user. Emotion can be derived from the subject's psychological data as well. Various approaches that employ combinations of physiological sensors for emotion recognition have been proposed. Yet, not all sensors are simple to use and handy for individuals in their daily lives. Thus, we propose a system to predict user emotion using smartwatch sensors. We design a framework to collect ground truth in real-time utilizing a mix of English and regional language-based videos to invoke emotions in participants and collect the data. Further, we modeled the problem as binary classification due to the limited dataset size and experimented with multiple machine-learning models. We also did an ablation study to understand the impact of features including Heart Rate, Accelerometer, and Gyroscope sensor data on mood. From the experimental results, Multi-Layer Perceptron has shown a maximum accuracy of 93.75 percent for pleasant-unpleasant (high/low valence classification) moods.