Please use this identifier to cite or link to this item:
Title: Multimodal group activity state detection for classroom response system using convolutional neural networks
Authors: Sebastian A.G.
Singh S.
Manikanta P.B.T.
Ashwin T.S.
Ram Mohana Reddy, Guddeti
Issue Date: 2019
Citation: Advances in Intelligent Systems and Computing, 2019, Vol.707, pp.245-251
Abstract: Human–Computer Interaction is a crucial and emerging field in computer science. This is because computers are replacing humans in many jobs to provide services. This has resulted in the computer being needed to interact with the human in the same way as the human does with another. When humans talk to each other, they gain feedback based on how the other person responds non-verbally. Since computers are now interacting with humans, they need to be able to detect these facial cues and accordingly adjust their services based on this feedback. Our proposed method aims at building a Multimodal Group Activity State Detection for Classroom Response System which tries to recognize the learning behavior of a classroom for providing effective feedback and inputs to the teacher. The key challenges dealt here are to detect and analyze as many students as possible for a non-biased evaluation of the mood of the students and classify them into three activity states defined: Active, passive, and inactive. © Springer Nature Singapore Pte Ltd. 2019
URI: 10.1007/978-981-10-8639-7_25
Appears in Collections:3. Book Chapters

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.