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Apps to measure motor skills of vocational workers

Published: 12 September 2016 Publication History

Abstract

Motor skills are required in a large number of vocational jobs today. However, no automated means exist to test and provide feedback on these skills. In this paper, we explore the use of touch-screen surfaces and tablet-apps to measure these skills. We design novel gamified apps to predict the performance of candidates in doing manual tasks in the industry. We demonstrate two important results - we use the information captured on a touch-screen device to successfully predict the scores of traditional, non-automated motor skill tests. Further, we show that this information successfully predicts the performance of workers in their respective jobs. The results presented in this work make a strong case for using such automated, touchscreen based apps in job selection and to provide automatic feedback. To the best of the authors' knowledge, this is the first attempt at using touch-screen devices to scalably and reliably measure motor skills.

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    cover image ACM Conferences
    UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
    September 2016
    1288 pages
    ISBN:9781450344616
    DOI:10.1145/2971648
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 September 2016

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    Author Tags

    1. assessments
    2. blue collar jobs
    3. psychomotor skills
    4. tablets
    5. touch-screen devices

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    UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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