Investigating the Relationship Between Posture and Safety in Teleoperational Tasks: A Pilot Study in Improved Operational Safety Through Enhanced Human-Machine Interaction

Piercy, Thomas; Herrmann, Guido; Skilton, Robert; Cangelosi, Angelo; Romeo, Marta; Pulgarin, Erwin Jose Lopez

doi:10.1007/978-3-031-15908-4_3

Cited by 2 publications

(1 citation statement)

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“…In the development and prototyping process, operator feedback suggested a desire for training tools designed to encourage good habits and any mechanisms that might improve operator health and operational safety. Preliminary sensor data analysis suggests a potential link between operator pose and task load ( Piercy et al, 2022 ), laying the foundation for further investigation.…”

Section: Introductionmentioning

confidence: 99%

Using skeletal position to estimate human error rates in telemanipulator operators

Piercy,

Herrmann,

Cangelosi

et al. 2024

Front. Robot. AI

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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.

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Section: Introductionmentioning

confidence: 99%