TrackEd: An emotion tracking tool for e-meeting platforms

Jamie McGrath; Nonso Nnamoko

doi:10.1016/j.simpa.2023.100560

TrackEd: An emotion tracking tool for e-meeting platforms

Jamie McGrath
, Nonso Nnamoko

Research output: Contribution to journal › Article (journal) › peer-review

3 Citations (Scopus)

434 Downloads (Pure)

Abstract

Online classrooms has seen an unprecedented spike in demand since the COVID-19 pandemic. This presents a challenge for educators to observe students’ body language, which is more easily done in a face-to-face setting. This paper presents TrackEd, a tool based on emotion classification that detects body language in form of emotions expressed facially. The tool extracts image frames of attendees on live online meeting stream, and tracks their facial emotion. This is facilitated by a convolutional neural network model trained on 40,254 images categorised into seven facial expressions. The complete source code is freely available on Github entitled ‘TrackEd’

Original language	English
Article number	100560
Pages (from-to)	1-5
Journal	Software Impacts
Volume	17
Early online date	5 Aug 2023
DOIs	https://doi.org/10.1016/j.simpa.2023.100560
Publication status	Published - 11 Aug 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education

Keywords

E-meeting platform
Affective computing
Facial expression recognition
Deep learning
Online classroom
Emotion detection
Artificial intelligence

Research Groups

SustainNET

Access to Document

10.1016/j.simpa.2023.100560Licence: CC BY-NC-ND

1-s2.0-S2665963823000970-mainFinal published version, 1.51 MBLicence: CC BY-NC-ND

Cite this

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title = "TrackEd: An emotion tracking tool for e-meeting platforms",

abstract = "Online classrooms has seen an unprecedented spike in demand since the COVID-19 pandemic. This presents a challenge for educators to observe students{\textquoteright} body language, which is more easily done in a face-to-face setting. This paper presents TrackEd, a tool based on emotion classification that detects body language in form of emotions expressed facially. The tool extracts image frames of attendees on live online meeting stream, and tracks their facial emotion. This is facilitated by a convolutional neural network model trained on 40,254 images categorised into seven facial expressions. The complete source code is freely available on Github entitled {\textquoteleft}TrackEd{\textquoteright}",

keywords = "E-meeting platform, Affective computing, Facial expression recognition, Deep learning, Online classroom, Emotion detection, Artificial intelligence",

author = "Jamie McGrath and Nonso Nnamoko",

year = "2023",

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day = "11",

doi = "10.1016/j.simpa.2023.100560",

language = "English",

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journal = "Software Impacts",

issn = "2665-9638",

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AU - Nnamoko, Nonso

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AB - Online classrooms has seen an unprecedented spike in demand since the COVID-19 pandemic. This presents a challenge for educators to observe students’ body language, which is more easily done in a face-to-face setting. This paper presents TrackEd, a tool based on emotion classification that detects body language in form of emotions expressed facially. The tool extracts image frames of attendees on live online meeting stream, and tracks their facial emotion. This is facilitated by a convolutional neural network model trained on 40,254 images categorised into seven facial expressions. The complete source code is freely available on Github entitled ‘TrackEd’

KW - E-meeting platform

KW - Affective computing

KW - Facial expression recognition

KW - Deep learning

KW - Online classroom

KW - Emotion detection

KW - Artificial intelligence

UR - https://doi.org/10.1016/j.simpa.2023.100560

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DO - 10.1016/j.simpa.2023.100560

M3 - Article (journal)

SN - 2665-9638

VL - 17

SP - 1

EP - 5

JO - Software Impacts

JF - Software Impacts

M1 - 100560

ER -

TrackEd: An emotion tracking tool for e-meeting platforms

Abstract

UN SDGs

Keywords

Research Groups

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