Effectiveness of Deep Neural Network Model in Typing-Based Emotion Detection on Smartphones
Document Type
Contribution to Book
Publication Date
10-2018
Publication Title
Proceedings of the International Conference on Mobile Computing and Networking (Mobicom)
DOI
10.1145/3241539.3267761
ISBN
978-1-4503-5903-0
Abstract
Typing characteristics on smartphones can provide clues for emotion detection. Collecting large volumes of typing data is also easy on smartphones. This motivates the use of Deep Neural Network (DNN) to determine emotion states from smartphone typing. In this work, we developed a DNN model based on typing features to predict four emotion states (happy, sad, stressed, relaxed) and investigate its performance on a smartphone. The evaluation of the model in a 3-week study with 15 participants reveals that it can reliably detect emotions with an average accuracy of 80% with peak CPU utilization less than 15%.
Recommended Citation
Ghosh, Surjya, Niloy Ganguly, Bivas Mitra, Pradipta De.
2018.
"Effectiveness of Deep Neural Network Model in Typing-Based Emotion Detection on Smartphones."
Proceedings of the International Conference on Mobile Computing and Networking (Mobicom): 750-752.
doi: 10.1145/3241539.3267761 isbn: 978-1-4503-5903-0
https://digitalcommons.georgiasouthern.edu/compsci-facpubs/206
