Describes the design and implementation of a task allocation for a human-computer interface that is capable of adapting the human's workload, online, according to his ability to perform tasks for systems, where the computer acts as a backup decision maker. During the operation of the system, the human and the computer's service and error rates are continually measured. An embedded human model estimates the change in the human's ability to service his tasks baled on the measured service and error rates. Future human and computer service rates and task arrival rates in the system are then predicted. Every time a new task arrives in the system the interface decides whether the human is capable of dealing with all current tasks before the next task arrives. If the human is unable to deal with the workload, the computer is switched on to aid him. The interface is broken down into modules, with each module's quantitative behavior defined separately. A probabilistic sensitivity analysis of the task allocation equations develops metrics that can be used to assess the task allocation's robustness with regard to uncertainties in the predicted human and system performances. The interface is implemented in a real time, human operated surveillance system. >

A quantitative model for adaptive task allocation in human-computer interfaces

The human factors involved in an unmanned aerial system (UAS) come in a variety of forms that has largely gone poorly represented in literature. In this paper, a holistic approach is taken to identify not only the individual aspects but the interconnection of the human-UAS interaction. First, an examination of human factors involved in a UAS are presented. Next, the metrics of human performance, such as cognitive load, situational awareness and complacency are introduced. Finally, a framework is developed to form the interconnection of human factors with human performance metrics in a UAS. With this framework in place, the intended goal is that an optimal level of cognitive workload for the humans involved in an UAS can be designed and implemented by the utilization of this framework during the development of advanced automation systems and human interface devices.

A framework for analyzing human factors in unmanned aerial systems

A decision-making human-computer interface capable of adapting to the human's changing performance with time is presented The interface's components are identified, and a suitable theoretical framework for each component is discussed The interface's algorithm is based on the following interaction of the components As the human performs tasks, his or her performance is measured and this knowledge is integrated with the estimated task arrival and service rates An optimal task allocation that determines which tasks are allocated to the human and to the computer is then chosen on the basis of the current human and system performance The algorithm was tested in a human-controlled surveillance system Results showed that the use of the interface enhanced the human's performance during periods of high mental workload >

A decision making human-computer interface

Presents a human-computer interface for a multi-sensor surveillance systems capable of adapting to the human's changing performance with time. The interface's algorithm works as follows. As the human performs tasks, his performance is measured and this knowledge is integrated together with the estimated task arrival and service rates. An optimal task allocation, based on the current human and system performances, is chosen which then determines which tasks are allocated to the human and to the computer respectively. The algorithm was tested within a human controlled surveillance system. Results showed that the use of the interface enhanced the human's performance during periods of high mental workload. >

A human-computer interface for a multi-sensor surveillance environment

An algorithm that is able to estimate adaptively the performance of a human operator in a series of overlapping tasks is presented. The algorithm has its foundations in the multiple resource pool model of human operator workload. Each task performed by the operator is split into a number of subtasks, with each subtask in turn modeled by a finite-impulse-response (FIR) filter channel. As each subtask is executed, the channel bandwidths are updated to provide the weights of the FIR filter and estimates of the correlation between the different channels. The correlation gives an indication of the ability to perform tasks simultaneously. The algorithm was tested in a real-time operator framework in which the operator had to perform different simple tasks simultaneously. >

W.D. Rencken

Papers

On-line estimation of human operator workload