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Open AccessArticle

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation

1
Ubiquitous Computing Lab, Department of Computer Science and Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, Seoul 446-701, Korea
2
Department of Software, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1622; https://doi.org/10.3390/s18051622
Received: 16 March 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 18 May 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants. View Full-Text
Keywords: user experience evaluation; user experience measurement; eye-tracking; facial expression; galvanic skin response; EEG; interaction tracker; self-reporting; user experience platform; mix-method approach user experience evaluation; user experience measurement; eye-tracking; facial expression; galvanic skin response; EEG; interaction tracker; self-reporting; user experience platform; mix-method approach
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MDPI and ACS Style

Hussain, J.; Khan, W.A.; Hur, T.; Bilal, H.S.M.; Bang, J.; Hassan, A.U.; Afzal, M.; Lee, S. A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation. Sensors 2018, 18, 1622. https://doi.org/10.3390/s18051622

AMA Style

Hussain J, Khan WA, Hur T, Bilal HSM, Bang J, Hassan AU, Afzal M, Lee S. A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation. Sensors. 2018; 18(5):1622. https://doi.org/10.3390/s18051622

Chicago/Turabian Style

Hussain, Jamil; Khan, Wajahat Ali; Hur, Taeho; Bilal, Hafiz Syed Muhammad; Bang, Jaehun; Hassan, Anees Ul; Afzal, Muhammad; Lee, Sungyoung. 2018. "A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation" Sensors 18, no. 5: 1622. https://doi.org/10.3390/s18051622

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