Effectiveness of Deep Neural Network Model in Typing-based Emotion Detection on Smartphones

被引：2

作者：

Ghosh, Surjya ^{[1
]}

Ganguly, Niloy ^{[1
]}

Mitra, Bivas ^{[1
]}

De, Pradipta ^{[2
]}

机构：

[1] IIT Kharagpur, Kharagpur, W Bengal, India

[2] Georgia Southern Univ, Statesboro, GA USA

来源：

MOBICOM'18: PROCEEDINGS OF THE 24TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING | 2018年

关键词：

Emotion detection; Typing; Deep neural network; Smartphone;

D O I：

10.1145/3241539.3267761

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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%.

引用

页码：750 / 752

页数：3