WEARS: Wearable Emotion AI with Real-time Sensor data
Dhruv Limbani Daketi Yatin Nitish Chaturvedi Vaishnavi Moorthy Pushpalatha M Harichandana BSS Sumit Kumar
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.