{"title":"Apps to measure motor skills of vocational workers","authors":"Bhanu Pratap Singh Rawat, V. Aggarwal","doi":"10.1145/2971648.2971739","DOIUrl":null,"url":null,"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.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"9 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2971648.2971739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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.