Triptech: A Method for Evaluating Early Design Concepts

Séguin, Julie Anne; Scharff, Alec; Pedersen, Kyle

doi:10.1145/3290607.3299061

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…The patient would then be able to interact with the acquaintance with the acquaintance’s information in mind. The device’s usage is shown through the triptych storyboard [ 27 ] in Figure 1 c.…”

Section: Methodsmentioning

confidence: 99%

Domain-Specific Cognitive Prosthesis for Face Memory and Recognition

et al. 2022

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The present study proposes a cognitive prosthesis device for face memory impairment as a proof-of-concept for the domain-specific cognitive prosthesis. Healthy subjects (n = 6) and a patient with poor face memory were enrolled. An acquaintance face recognition test with and without the use of cognitive prosthesis for face memory impairment, face recognition tests, quality of life, neuropsychological assessments, and machine learning performance of the cognitive prosthesis were followed-up throughout four weeks of real-world device use by the patient. The healthy subjects had an accuracy of 92.38 ± 4.41% and reaction time of 1.27 ± 0.12 s in the initial attempt of the acquaintance face recognition test, which changed to 80.48 ± 6.23% (p = 0.06) and 2.11 ± 0.20 s (p < 0.01) with prosthesis use. The patient had an accuracy of 74.29% and a reaction time of 6.65 s, which improved to 94.29% and 3.28 s with prosthesis use. After four weeks, the patient’s unassisted accuracy and reaction time improved to 100% and 1.23 s. Functional MRI study revealed activation of the left superior temporal lobe during face recognition task without prosthesis use and activation of the right precentral motor area with prosthesis use. The prosthesis could improve the patient’s performance by bypassing the brain area inefficient for facial recognition and employing the area more efficiently for the cognitive task.

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Section: Methodsmentioning

confidence: 99%

Domain-Specific Cognitive Prosthesis for Face Memory and Recognition

et al. 2022

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“…There are only a few existing works related to our paper. In [21], the authors propose a solution to evaluate the design concept of a product which is quite different from a user interface. However, the proposed method in [21] is solely based on survey data and no machine learning methods are discussed.…”

Section: Related Workmentioning

confidence: 99%

“…In [21], the authors propose a solution to evaluate the design concept of a product which is quite different from a user interface. However, the proposed method in [21] is solely based on survey data and no machine learning methods are discussed. Authors in [12] also discuss how to design a user-friendly machine learning interface with the user experience and research scientist collaboration.…”

Section: Related Workmentioning

confidence: 99%

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Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

Zhou

Tang

Zhao

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.

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“…Only two studies [8,36] use sensors, such as eye trackers, to identify the area that the user is observing. Almost half of the studies [1,2,8,32,33,[41][42][43][44][45][46] use some form of questionnaire to obtain qualitative information about user experience or usability. The vast majority of studies perform in loco evaluation, with the specialist observing the test in the same location as the participant, except for References [8,30,36], where the specialist remotely observe user interaction.…”

Section: Related Workmentioning

confidence: 99%

UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

et al. 2019

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This paper presents UXmood, a tool that provides quantitative and qualitative information to assist researchers and practitioners in the evaluation of user experience and usability. The tool uses and combines data from video, audio, interaction logs and eye trackers, presenting them in a configurable dashboard on the web. The UXmood works analogously to a media player, in which evaluators can review the entire user interaction process, fast-forwarding irrelevant sections and rewinding specific interactions to repeat them if necessary. Besides, sentiment analysis techniques are applied to video, audio and transcribed text content to obtain insights on the user experience of participants. The main motivations to develop UXmood are to support joint analysis of usability and user experience, to use sentiment analysis for supporting qualitative analysis, to synchronize different types of data in the same dashboard and to allow the analysis of user interactions from any device with a web browser. We conducted a user study to assess the data communication efficiency of the visualizations, which provided insights on how to improve the dashboard.

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Triptech: A Method for Evaluating Early Design Concepts

Cited by 15 publications

References 7 publications

Domain-Specific Cognitive Prosthesis for Face Memory and Recognition

Domain-Specific Cognitive Prosthesis for Face Memory and Recognition

Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

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