In Affective Computing, different modalities, such as speech, facial expressions, physiological properties, smartphone usage patterns, and their combinations, are applied to detect the affective states of a user. In this project, we explore different challenges of developing smartphone based emotion detection application like modality selection, influence of Experience Sampling Method (ESM) on emotion detection, simplifying self-report collection.
- Emotion aware computing in smart mobile devices from input interactions
- Surjya Ghosh, Niloy Ganguly, Bivas Mitra and Pradipta De; Evaluating Effectiveness of Smartphone Typing as an Indicator of User Emotion, 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017), San Antonio, Texas, USA, Oct 2017.
- Surjya Ghosh, Niloy Ganguly, Bivas Mitra and Pradipta De; TapSense: Combining Self-Report Patterns and Typing Characteristics for Smartphone based Emotion Detection, MobileHCI, Vienna, Austria, Sep 2017.
- Surjya Ghosh, Niloy Ganguly, Bivas Mitra and Pradipta De; Towards Designing an Intelligent Experience Sampling Method for Emotion Detection, IEEE CCNC 2017, Las Vegas, USA.
- Surjya Ghosh, Vatsalya Chauhan, Niloy Ganguly, Bivas Mitra and Pradipta De; Impact of Experience Sampling Methods on Tap Pattern based Emotion Recognition, 4th ACM Workshop on Mobile Systems for Computational Social Science – MCSS (Ubicomp.15) Osaka, Japan.
- MHRD, India
Members & Collaborations:
- Prof. Niloy Ganguly
- Prof. Bivas Mitra
- Prof. Pradipta De, Georgia Southern University, USA (http://www3.cs.stonybrook.edu/~prade/)
- Surjya Ghosh