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 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.

Synthesis of ZnMn2O4 Nanoparticles by a Microwave-Assisted Colloidal Method and their Evaluation as a Gas Sensor of Propane and Carbon Monoxide.

Spinel-type ZnMn2O4 nanoparticles were synthesized via a simple and inexpensive microwave-assisted colloidal route. Structural studies by X-ray diffraction showed that a spinel crystal phase of ZnMn2O4 was obtained at a calcination temperature of 500 °C, which was confirmed by Raman and UV-vis characterizations. Spinel-type ZnMn2O4 nanoparticles with a size of 41 nm were identified by transmission electron microscopy. Pellet-type sensors were fabricated using ZnMn2O4 nanoparticles as sensing material. Sensing measurements were performed by exposing the sensor to different concentrations of propane or carbon monoxide at temperatures in the range from 100 to 300 °C. Measurements performed at an operating temperature of 300 °C revealed a good response to 500 ppm of propane and 300 ppm of carbon monoxide. Hence, ZnMn2O4 nanoparticles possess a promising potential in the gas sensors field.