Peer-vasive computing: Leveraging peers to enhance the accuracy of self-reports in mobile human studies

Allan Berrocal, Katarzyna Wac

    7 Citations (Scopus)

    Abstract

    We discuss two methods designed to increase the accuracy of human-labeled data. First, Peer-ceived Momentary Assessment (Peer-MA), a novel data collection method inspired by the concept of Observer Reported Outcomes in clinical care. Second, mQoL-Peer, a platform aiming to equip researchers with tools to assess and maintain the accuracy of the data collected by participants and peers during mobile human studies. We describe the state of the research and specific contributions.

    Original languageEnglish
    Title of host publicationUbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
    Number of pages6
    PublisherAssociation for Computing Machinery
    Publication date2018
    Pages600-605
    ISBN (Electronic)9781450359665
    DOIs
    Publication statusPublished - 2018
    Event2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
    Duration: 8 Oct 201812 Oct 2018

    Conference

    Conference2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
    Country/TerritorySingapore
    CitySingapore
    Period08/10/201812/10/2018
    SponsorACM SIGCHI, ACM SIGMOBILE

    Keywords

    • Ecological Momentary Assessment
    • Observer's Assessment
    • Peer-ceived Momentary Assessment
    • Self-Assessment

    Fingerprint

    Dive into the research topics of 'Peer-vasive computing: Leveraging peers to enhance the accuracy of self-reports in mobile human studies'. Together they form a unique fingerprint.

    Cite this