Using skeletal position to estimate human error rates in telemanipulator operators
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 tele...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2023.1287417/full |
| _version_ | 1797361392066494464 |
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| author | Thomas Piercy Guido Herrmann Angelo Cangelosi Ioannis Dimitrios Zoulias Erwin Lopez |
| author_facet | Thomas Piercy Guido Herrmann Angelo Cangelosi Ioannis Dimitrios Zoulias Erwin Lopez |
| author_sort | Thomas Piercy |
| collection | DOAJ |
| description | 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 in situ 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’s 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. |
| first_indexed | 2024-03-08T15:53:02Z |
| format | Article |
| id | doaj.art-ce8d71e5df0b4bc0a33779bb35e0511e |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-03-08T15:53:02Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-ce8d71e5df0b4bc0a33779bb35e0511e2024-01-09T04:15:50ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442024-01-011010.3389/frobt.2023.12874171287417Using skeletal position to estimate human error rates in telemanipulator operatorsThomas Piercy0Guido Herrmann1Angelo Cangelosi2Ioannis Dimitrios Zoulias3Erwin Lopez4Faculty of Science and Engineering, The University of Manchester, Manchester, United KingdomFaculty of Science and Engineering, The University of Manchester, Manchester, United KingdomFaculty of Science and Engineering, The University of Manchester, Manchester, United KingdomRemote Applications in Challenging Environments, United Kingdom Atomic Energy Authority, Culham Science Centre, Oxford, United KingdomFaculty of Science and Engineering, The University of Manchester, Manchester, United KingdomIn 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 in situ 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’s 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.https://www.frontiersin.org/articles/10.3389/frobt.2023.1287417/fullbio-mechanical modellingfeedback systemspsychologysensory integrationapplications in industrial activities |
| spellingShingle | Thomas Piercy Guido Herrmann Angelo Cangelosi Ioannis Dimitrios Zoulias Erwin Lopez Using skeletal position to estimate human error rates in telemanipulator operators Frontiers in Robotics and AI bio-mechanical modelling feedback systems psychology sensory integration applications in industrial activities |
| title | Using skeletal position to estimate human error rates in telemanipulator operators |
| title_full | Using skeletal position to estimate human error rates in telemanipulator operators |
| title_fullStr | Using skeletal position to estimate human error rates in telemanipulator operators |
| title_full_unstemmed | Using skeletal position to estimate human error rates in telemanipulator operators |
| title_short | Using skeletal position to estimate human error rates in telemanipulator operators |
| title_sort | using skeletal position to estimate human error rates in telemanipulator operators |
| topic | bio-mechanical modelling feedback systems psychology sensory integration applications in industrial activities |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2023.1287417/full |
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