The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Shuttleworth Kate - One of the best experts on this subject based on the ideXlab platform.

  • Card Sorting and User Scenarios: Usability Testing of SFU\u27s Scholarly Publishing and Open Access Webpages
    2018
    Co-Authors: Shuttleworth Kate
    Abstract:

    Academic libraries are leading changes in the scholarly publishing ecosystem, and librarians are responsible for clearly communicating with researchers about this developing area. The purpose of this research project was to update SFU’s Scholarly Publishing and Open Access webpages to make the structure, language and content accessible and discoverable for a wide-range of users. We were investigating the question: Can users find what they need on the Scholarly Publishing and Open Access webpages? Our research was based on commonly adopted Usability and information architecture principles, such as those described by Usability.gov, Rosenfeld, Morville, & Arango (2015), and Nielsen (2012). We conducted two phases of qualitative data collection: An open, moderated, paper card sorting activity to collect initial data about the structure of the pages; and a Usability-Lab Study with scenarios to test the resulting content. Data was manually coded into thematic groups, and webpage edits were prioritized based on respondent feedback. We anticipate conducting similar Usability testing on an iterative basis to keep the webpages current, and our experience will inform our approach for future studies

Shengdong Zhao - One of the best experts on this subject based on the ideXlab platform.

  • CCHI - Trade-off between Automation and Accuracy in Mobile Photo Recognition Food Logging
    Proceedings of the Fifth International Symposium of Chinese CHI on - Chinese CHI 2017, 2017
    Co-Authors: Brian Y. Lim, Xinni Chng, Shengdong Zhao
    Abstract:

    Food logging can help users understand their food choices and encourage healthier eating habits. However, current apps still pose many Usability challenges, including tedious manual text entry of food names. Recently, advances in computer vision and deep learning are enabling automatic food recognition for instant and convenient logging. However, as a nascent technology, this suffers from inaccuracy, which may lead to poor adoption or misuse. We investigated the trade-off between accuracy and convenience of automatic photo recognition in comparison to manual search logging. Specifically, we have developed a mobile app prototype that integrates both photo recognition and search logging capabilities, and conducted formative investigations on the Usability and usage of automatic photo recognition in food logging in a series of studies: online requirements survey, Usability Lab Study, and 1-week field trial in an Asian country. Participants were interested in convenient, automatic photo logging, but dominantly used manual search logging due to a lack of data coverage and accuracy. We identified reasons for poor accuracy and highlight complications in using inaccurate automatic photo logging. We further discuss opportunities for design and technology to address these challenges.

Brian Y. Lim - One of the best experts on this subject based on the ideXlab platform.

  • CCHI - Trade-off between Automation and Accuracy in Mobile Photo Recognition Food Logging
    Proceedings of the Fifth International Symposium of Chinese CHI on - Chinese CHI 2017, 2017
    Co-Authors: Brian Y. Lim, Xinni Chng, Shengdong Zhao
    Abstract:

    Food logging can help users understand their food choices and encourage healthier eating habits. However, current apps still pose many Usability challenges, including tedious manual text entry of food names. Recently, advances in computer vision and deep learning are enabling automatic food recognition for instant and convenient logging. However, as a nascent technology, this suffers from inaccuracy, which may lead to poor adoption or misuse. We investigated the trade-off between accuracy and convenience of automatic photo recognition in comparison to manual search logging. Specifically, we have developed a mobile app prototype that integrates both photo recognition and search logging capabilities, and conducted formative investigations on the Usability and usage of automatic photo recognition in food logging in a series of studies: online requirements survey, Usability Lab Study, and 1-week field trial in an Asian country. Participants were interested in convenient, automatic photo logging, but dominantly used manual search logging due to a lack of data coverage and accuracy. We identified reasons for poor accuracy and highlight complications in using inaccurate automatic photo logging. We further discuss opportunities for design and technology to address these challenges.

Xinni Chng - One of the best experts on this subject based on the ideXlab platform.

  • CCHI - Trade-off between Automation and Accuracy in Mobile Photo Recognition Food Logging
    Proceedings of the Fifth International Symposium of Chinese CHI on - Chinese CHI 2017, 2017
    Co-Authors: Brian Y. Lim, Xinni Chng, Shengdong Zhao
    Abstract:

    Food logging can help users understand their food choices and encourage healthier eating habits. However, current apps still pose many Usability challenges, including tedious manual text entry of food names. Recently, advances in computer vision and deep learning are enabling automatic food recognition for instant and convenient logging. However, as a nascent technology, this suffers from inaccuracy, which may lead to poor adoption or misuse. We investigated the trade-off between accuracy and convenience of automatic photo recognition in comparison to manual search logging. Specifically, we have developed a mobile app prototype that integrates both photo recognition and search logging capabilities, and conducted formative investigations on the Usability and usage of automatic photo recognition in food logging in a series of studies: online requirements survey, Usability Lab Study, and 1-week field trial in an Asian country. Participants were interested in convenient, automatic photo logging, but dominantly used manual search logging due to a lack of data coverage and accuracy. We identified reasons for poor accuracy and highlight complications in using inaccurate automatic photo logging. We further discuss opportunities for design and technology to address these challenges.