Selecting Learning Curve Models for Human Operator Performance

The objective of this research was to assess the effects of two biodynamic stressors, noise and acceleration, commonly experienced in the aircraft cockpit, on human operator performance and workload. Thirteen workload measures, including one subjective, four performance and eight physiological, were recorded on subjects performing a dual psychomotor task. The results indicate that biodyanmic stressors such as noise and acceleration can adversely affect subjective operator workload without affecting objective task performance.

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A telerobotic Human/Robot Interface for mobile manipulators: A study of human operator performance

2013 International Conference on Control, Decision and Information Technologies (CoDIT) ◽

10.1109/codit.2013.6689618 ◽

2013 ◽

Cited By ~ 4

Author(s):

A. Hentout ◽

M. R. Benbouali ◽

I. Akli ◽

B. Bouzouia ◽

L. Melkou

Keyword(s):

Human Operator ◽

Mobile Manipulators ◽

Operator Performance

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Modelling human operator performance — Possibilities and limitations

Applied Ergonomics ◽

10.1016/0003-6870(87)90156-6 ◽

1987 ◽

Vol 18 (4) ◽

pp. 343

Keyword(s):

Human Operator ◽

Operator Performance

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Visual Factors Affecting Human Operator Performance with a Helmet-Mounted Display

10.4271/911389 ◽

1991 ◽

Cited By ~ 1

Author(s):

Andrew Liu ◽

Gregory Tharp ◽

Michitaka Hirose ◽

Lawrence Stark

Keyword(s):

Human Operator ◽

Factors Affecting ◽

Operator Performance

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Human Operator Performance Testing Using an Input-Shaped Bridge Crane

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.2361321 ◽

2005 ◽

Vol 128 (4) ◽

pp. 835-841 ◽

Cited By ~ 78

Author(s):

Attir Khalid ◽

John Huey ◽

William Singhose ◽

Jason Lawrence ◽

David Frakes

Keyword(s):

Image Processing ◽

Processing System ◽

Performance Testing ◽

Human Operator ◽

Input Shaping ◽

Bridge Crane ◽

Georgia Institute ◽

Operator Performance ◽

Human Operators ◽

Institute Of Technology

The payload oscillation inherent to all cranes makes it challenging for human operators to manipulate payloads quickly, accurately, and safely. An input-shaping controller was implemented on a large bridge crane at the Georgia Institute of Technology to reduce crane payload oscillation. The crane was used to study the performance of human operators as they drove the crane through obstacle courses. An image processing system was implemented to track the movement of the crane payload. Data from these experiments show that operators performed manipulation tasks faster, safer, and more effectively when input shaping was utilized to reduce payload sway.

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