Surjya Ghosh is a postdoctoral researcher at Centrum Wiskunde and Informatica (aka national research institute for mathematics and computer science in the Netherlands) Amsterdam since Jul-2019. Before this, he has completed Ph.D. in Computer Science and Engineering from Indian Institute of Technology Kharagpur, India. His research interests are in the area of affective computing, human-computer interaction, and applied AI. He has been selected as a young researcher for the prestigious Heidelberg Laureate Forum (HLF) in 2020
Developing Smartphone Keyboard Interaction based Emotion Detection System
Keyboard interactions on communication applications, like WhatsApp, FB messenger can induce emotional exchanges. Moreover, monitoring keyboard interaction is unobtrusive and does not have high resource implications. As a result, exploring different types of keyboard interaction patterns (such as typing speed, touch pressure, error rate, special characters usage) can reveal emotion cues and aid in developing non-intrusive, resource- friendly emotion tracking applications. Additionally, the effects of emotion in one session of communication can persist across sessions, which if modeled jointly with keyboard interaction patterns, can further improve the emotion detection performance. We, in this work, discuss the design, development, and implementation of an Android application, TouchSense, which leverages keyboard interaction characteristics and implements a machine learning model to infer multiple emotion states (happy, sad, stressed, relaxed). We also discuss the approaches of automatically learning the keyboard interaction representation and adopting a Multi-task Learning strategy so that the keyboard interaction similarity among users can be leveraged for a superior performance.