Adaptive Aiding Research Articles

Performance Enhancement in an Uninhabited Air Vehicle Task Using Psychophysiologically Determined Adaptive Aiding

We show that psychophysiologically driven real-time adaptive aiding significantly enhances performance in a complex aviation task. A further goal was to assess the importance of individual operator capabilities when providing adaptive aiding. Psychophysiological measures are useful for monitoring cognitive workload in laboratory and real-world settings. They can be recorded without intruding into task performance and can be analyzed in real time, making them candidates for providing operator functional state estimates. These estimates could be used to determine if and when system intervention should be provided to assist the operator to improve system performance. Adaptive automation was implemented while operators performed an uninhabited aerial vehicle task. Psychophysiological data were collected and an artificial neural network was used to detect periods of high and low mental workload in real time. The high-difficulty task levels used to initiate the adaptive automation were determined separately for each operator, and a group-derived mean difficulty level was also used. Psychophysiologically determined aiding significantly improved performance when compared with the no-aiding conditions. Improvement was greater when adaptive aiding was provided based on individualized criteria rather than on group-derived criteria. The improvements were significantly greater than when the aiding was randomly provided. These results show that psychophysiologically determined operator functional state assessment in real time led to performance improvement when included in closed loop adaptive automation with a complex task. Potential future applications of this research include enhanced workstations using adaptive aiding that would be driven by operator functional state.

The Importance of Determining Individual Operator Capabilities When Applying Adaptive Aiding

Performance was significantly improved in an Uninhabited Air Vehicle (UAV) task when individually determined task difficulty levels were used to present psychophysiologically controlled adaptive aiding. Previous research in our laboratory demonstrated that the benefit of adaptive aiding varied according to the operator's skill level when a common task difficulty was used for all operators. In the present study the difficult task level was determined for each operator. An average task difficulty level was also used. The best performance occurred when adaptive aiding was presented based upon psychophysiological data submitted to an artificial neural network when the implementation level was individually determined for each operator. Performance improvement using the mean difficulty level was lower as were the results when the adaptive aiding was randomly presented. Individual cognitive capability must be considered to achieve optimal performance via adaptive aiding.

Psychophysiologically Determined Classification of Cognitive Activity

Psychophysiological measures and artificial neural networks were used to determine how well higher levels of cognitive activity, such as executive function, spatial and verbal working memory and global workload, could be assessed. A complex uninhabited air vehicle simulator was used in which subjects were responsible for four vehicles simultaneously. The subjects had to evaluate visual images and maintain the status of the vehicles. The results showed that the cognitive states, derived from subjective reports, could be accurately classified. These results have application in human factors environments which demand higher level cognitive processing and may be useful when implementing adaptive aiding in these systems.

Simulation Environment for Adaptive Aiding in Plant Operations

Simulation Environment for Adaptive Aiding in Plant Operations

Real-time assessment of mental workload using psychophysiological measures and artificial neural networks.

The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively. The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this research include test and evaluation and adaptive aiding implementation.

Taxonomy of Mitigation Strategies for Driver Distraction

Driver distraction can be described as the diversion of driver's attention from the primary task of driving and is one of the most common causes of crashes. Complex technologies that have either been introduced to the driving domain or are planned to be, raise the concern of high levels of distraction, by placing additional demands on drivers. Different mitigation strategies (e.g., warning and vehicle control) have been implemented in the vehicle to reduce driver distraction. However there has not been a clear definition or categorization of these strategies. This paper, therefore, proposes a taxonomy of mitigation strategies for driver distraction and relates the strategies to accumulated research in the areas of automation and adaptive aiding to define important design tradeoffs with each strategy. This taxonomy provides a framework that can guide research and address the driver distraction problem systematically.

Neuroergonomics: Opportunities and challenges of merging cognitive neuroscience with cognitive ergonomics

As with any merger of previously unrelated scientific disciplines, the usefulness of combining cognitive neuroscience and cognitive ergonomics to create the new field of 'neuroergonomics' depends on the significance of the research subjects that could not be addressed if the two fields remained separated. Situations characterized by a scarcity of overt operator behaviour and performance represent one driving force for combining the two fields. In those situations, neurobiological indices of cognitive processes and capacities may emerge as the main, or even exclusive, source of information about the operator's cognitive status and activities. This information is needed, for example, to support adaptive aiding and dynamic function allocation in the context of human-automation cooperation. The utilization of research approaches and measurements more typical of cognitive neuroscience will benefit the field of cognitive ergonomics to the extent that multidimensional psychological constructs (such as situation awareness and mental workload) are reduced to their underlying cognitive operations which are more isomorphic with activity changes in brain regions, circuits and distributed neuronal systems. The transformation of these constructs and paradigms will, in turn, enable cognitive ergonomics to contribute to progress in the field of cognitive neuroscience.

Physiological Self-Regulation and Adaptive Automation

Adaptive automation has been proposed as a solution to current problems of human-automation interaction. Past research has shown the potential of this advanced form of automation to enhance pilot engagement and lower cognitive workload. However, there have been concerns voiced regarding issues, such as automation surprises, associated with the use of adaptive automation. This study examined the use of psychophysiological self-regulation training with adaptive automation that may help pilots deal with these problems through the enhancement of cognitive resource management skills. Eighteen participants were assigned to 3 groups (self-regulation training, false feedback, and control) and performed resource management, monitoring, and tracking tasks from the Multiple Attribute Task Battery. The tracking task was cycled between 3 levels of task difficulty (automatic, adaptive aiding, manual) on the basis of the electroencephalogram-derived engagement index. The other two tasks remained in automatic mode that had a single automation failure. Those participants who had received self-regulation training performed significantly better and reported lower National Aeronautics and Space Administration Task Load Index scores than participants in the false feedback and control groups. The theoretical and practical implications of these results for adaptive automation are discussed.

Performance Enhancement with Real-Time Physiologically Controlled Adaptive Aiding

The realization of optimal system performance is the goal of both system designers and users. One critical component in attaining this goal is proper operator functioning. In contemporary systems the functional state of the operator is not considered during system operation. Degraded states of operator functioning can result from the demands of controlling complex systems, the work environment and internal operator variables. This, in turn, can lead to errors and overall suboptimal system performance. In the case of mental workload, system performance could be improved by reducing task demands during periods of operator overload. Accurate estimation of the operator's functional state is crucial to successful implementation of an adaptive aiding system. One method of determining operator functional state is by monitoring the operator's physiology. In the present study, physiological signals were used to continuously monitor subject's functional state and to adapt the task by reducing the number of subtasks when high levels of mental workload were detected. The goal was to demonstrate performance improvement with adaptive aiding. Because adaptive aiding during high mental workload has not been previously implemented its benefit has not be demonstrated. Application of adaptive aiding techniques reduced tracking task error by 44% and resource monitoring error by 33%. These results demonstrate the utility of adaptive aiding using physiological measures with artificial neural networks to determine the appropriate time to introduce the aiding.

The Application of Human Performance Models in the Design and Operation of Complex Systems

For years, systems designers have used models of hardware and software to support the system design process. Recently, there have been technological advances in modeling all aspects of human performance. The paper will describe four specific applications of human performance modeling that we are exploring to support both the design and operation of complex systems, such as future Navy ships. Specifically, this technology can be used during system design to generate team design concepts, to provide decision support for systems engineers trying to follow a human engineering process, and to allow simulation-based evaluation of human performance. During systems operation, this technology can be used to provide decision support, adaptive aiding, and automation to the operator(s). We believe that human performance modeling technology has matured to the point where it can play a significant role in both the design and operation of future complex systems, reducing both cost and risk.

Operator Functional State Classification Using Neural Networks with Combined Physiological and Performance Features

Operator functional state assessment is a critical component of adaptive aiding systems. A combination of physiological and performance variables were used with a neural network to determine operator functional state. A multiple task battery provided three levels of mental workload. The data were randomly divided into two data sets, one was used to train a neural net and the other to test the accuracy of the trained neural net. The results showed an overall correct classification of 86.8% for the test data set. For the three levels of task difficulty the correct classification was 90.5% for low, 81.7% for the medium and 88.3% for the high. These results support the use of combined physiological and performance data to obtain high levels of operator functional state classification accuracy. The optimal approach to utilizing this information during system operation will have to be developed. With current technology the development of small, wearable operator state classifiers is possible.

Modeling the dynamics of mental workload and human performance in complex systems

The relationship between subjective workload and human behaviors is considered. A model of the dynamics of this relationship is proposed. Results of three simulation experiments are reported that show simple linear identification algorithms are surprisingly robust in online identification of noisy, nonlinear versions of this model. The implications for developing adaptive aiding and decision support systems are noted. >

Inferring Operator Intent as a Basis for Aiding in Man-Machine Systems

Abstract Intent interpretation by observing and explaining operator actions provides a powerful means of implicit communication between a human operator and a man-machine system. While not always required in an operational system, knowledge of the intentions of the user may be quite useful as a basis for providing assistance via an intelligent interface. Using an model of operator intent, intelligent assistance can be provided while leaving the operator in total control. Specifically, assistance can be offered through information management, error monitoring, and adaptive aiding. In addition, the intent model facilitates implicit communications between the human operator and intelligent planners and situation assessors, allowing the human to guide the reasoning of the intelligent aiding systems without the added burden of explicitly communicating with them. The basic design of the intent inferencer is described from a high level. Knowledge representation used in the intent inferencer is discussed more extensively.

Adaptive Aiding for Human/Computer Control

Adaptive aiding is a human-machine system design concept that involves using aiding/automation only at those points in time when human performance in a system needs support to meet operational requirements---in the absence of such needs, human performance remains unaided/manual, and thereby humans remain very much "in the loop." This paper describes the evolution and results of an ongoing program of experimental and theoretical research in adaptive aiding. The development and proof of concept are first discussed, followed by consideration of human performance models, on-line assessment methods, and the psychology of human-aid interaction. The implications of these ideas and results are discussed relative to design of intelligent support systems in general and expert systems in particular. A framework for design is presented that includes a structured set of design questions that may be addressed in terms of principles of adaptation and principles of interaction.

The Minimal Manual

The conceptual design of a comprehensive support system for operators of complex systems is presented. Key functions within the support system architecture include information management, error monitoring, and adaptive aiding. One of the central knowledge sources underlying this functionality is an operator model that involves a combination of algorithmic and symbolic models for assessing and predicting an operator's activities, awareness, intentions, resources, and performance. Functional block diagrams are presented for the overall architecture as well as the key elements within this architecture. A variety of difficult design issues are discussed and ongoing efforts aimed at resolving these issues are noted.

An Architecture for Intelligent Interfaces: Outline of an Approach to Supporting Operators of Complex Systems

The conceptual design of a comprehensive support system for operators of complex systems is presented. Key functions within the support system architecture include information management, error monitoring, and adaptive aiding. One of the central knowledge sources underlying this functionality is an operator model that involves a combination of algorithmic and symbolic models for assessing and predicting an operator's activities, awareness, intentions, resources, and performance. Functional block diagrams are presented for the overall architecture as well as the key elements within this architecture. A variety of difficult design issues are discussed, and ongoing efforts aimed at resolving these issues are noted.

On the value of information in system design: A framework for understanding and aiding designers

Computer-based decision support systems for designers are considered. The nature of system design is discussed in terms of alternative philosophies, processes and methods. The design task is characterized as a form of problem solving, which leads to discussion of types of design problem and designer/problem-solver, as well as training of designers/problem-solvers. This general background serves as a basis for analyzing information requirements for design. The value of information is defined as having three attributes: (1) reduction of uncertainty, (2) task relevance and (3) appropriateness of form. These attributes, in conjunction with characterizations of stages/tasks of design and approaches to stages/tasks, are used to determine information requirements and the consequent nature of information seeking. The results of this requirements analysis are employed as a basis for consideration of computer-aided information seeking. Types of information/aiding discussed include: facts, fantasies and feedback, as well as adaptive aiding and training, all of which are espoused as the key elements of flexible systems for computer-aided design and computer-aided engineering.

Control Theory Measures of Tracking as Indices of Attention Allocation Strategies

In an intelligent man-machine control system, control theory measures describing the operator's tracking performance can provide useful information concerning an operator's attentional slate. This information may be used to implement adaptive aiding procedures. Research is reviewed that relates attentional manipulations to variation in control theory parameters, and an experiment is then described in which 29 subjects performed a tracking task alone, and concurrently with a serial reaction-time task. Within the time-sharing condition, relative priorities between the two tasks were manipulated. The results are interpreted in terms of the separate effects of time-sharing and of priority manipulations upon measures of tracking gain, remnant, time-delay and response “holds,” and the feasibility of on-line measurement of those variables.

System integration of pattern recognition, adaptive aided, upper limb prostheses

An integrated externally energized artificial arm which combines computer control techniques and sensory feedback is presented. The arm, which is intended for above elbow amputees, contains the functions of prehension, wrist rotation, elbow flexion and extension and humeral rotation. The paper outlines the nature of control problems in artificial limbs and reviews techniques for their solutions. A detailed description of the system involving myoelectric pattern control, adaptive computer aiding and electrocutaneous feedback is included with a discussion of the integration and interfaces of these factors.

Experimental study of man-machine interaction in adaptive computer aided control : Weltman, G. TRC Report No T74-04181 Nov, 1973, pp 56. Abstr in R and D Abstracts

Abstract : The report presents the background and results of an experimental study focusing on human factors aspects of adpative computer aiding. Included are (1) a rationale for shared decision and control, (2) a description of the adaptive aiding computer program and task simulation developed for the experimental study, (3) the experimental design, procedure, and measurement techniques along with a discussion of the results, and (4) the development and testing of a program providing on-line estimation of operator utilities for his own and machine control.