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Robot. AI"],"abstract":"<jats:p>In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted <jats:italic>in situ<\/jats:italic> using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator\u2019s skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.<\/jats:p>","DOI":"10.3389\/frobt.2023.1287417","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T04:16:04Z","timestamp":1704773764000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Using skeletal position to estimate human error rates in telemanipulator operators"],"prefix":"10.3389","volume":"10","author":[{"given":"Thomas","family":"Piercy","sequence":"first","affiliation":[]},{"given":"Guido","family":"Herrmann","sequence":"additional","affiliation":[]},{"given":"Angelo","family":"Cangelosi","sequence":"additional","affiliation":[]},{"given":"Ioannis Dimitrios","family":"Zoulias","sequence":"additional","affiliation":[]},{"given":"Erwin","family":"Lopez","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2241","DOI":"10.4271\/2001-01-2110","article-title":"Realistic posture prediction for maximum dexterity","volume":"110","author":"Abdel-Malek","year":"2001","journal-title":"SAE Trans."},{"key":"B2","first-page":"821","article-title":"Theoretical foundations of the potential function method in pattern recognition learning","volume":"25","author":"Aizerman","year":"1964","journal-title":"Automation Remote Control"},{"key":"B3","first-page":"283","article-title":"Head pose and movement analysis as an indicator of depression","author":"Alghowinem","year":"2013"},{"key":"B4","doi-asserted-by":"publisher","first-page":"103371","DOI":"10.1016\/j.jvcir.2021.103371","article-title":"Complex network-based features extraction in rgb-d human action recognition","volume":"82","author":"Barkoky","year":"2022","journal-title":"J. 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