An Unobtrusive Approach to Emotion Detection

Chin, Jeannette and Klos, Dawid (2025) An Unobtrusive Approach to Emotion Detection. In: Handbook on Smart Health. Studies in Health Technology and Informatics Book Series . IOS Press.

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Abstract

Stress, a negative emotion state, is being pervasively experienced in mod- ern life, significantly impacting both physical and mental well-being, often going unnoticed until its effects become severe. Technological solutions for stress detec- tion offer a proactive approach to managing this pervasive problem. This paper in- vestigated a case study using web-based learning, an unobtrusive approach, to de- tecting stress. The solution involved the development of two Neural Network mod- els (ANN and CNN) to be deployed and processed on the client device through a Web application. For this, two features were developed (journal and selfie) and the models classified the user’s inputs as either ’stressed’ or ’not stressed’ in real time. Underpinning the work is Web learning, tensorflow.js, a framework devel- oped by Google. The application was evaluated by 7 users. Results: the application and models worked as intended, and an average score of 3.57 out of 4 for user’s subjective view on the accuracy was obtained. This result suggests the models were perceived as working well during user evaluation. The results also reviewed the users preferred the journal over the selfie feature. All users (100%) considered the features effective at monitoring and tracking stress.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Visual Computing and Signal Processing Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Cyber Intelligence and Networks Faculty of Science > Research Groups > Smart Emerging Technologies (former - to 2025)
Depositing User:	LivePure Connector
Date Deposited:	04 Apr 2025 15:30
Last Modified:	04 Apr 2025 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98963
DOI:

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