Engineering topics

Repperger, D. W.

Publications and source records attributed to Repperger, D. W..

12 records

NASA NTRS1992

The effects of multiple aerospace environmental stressors on human performance

An extended Fitt's law paradigm reaction time (RT) task was used to evaluate the effects of acceleration on human performance in the Dynamic Environment Simulator (DES) at Armstrong Laboratory, Wright-Patterson AFB, Ohio. This effort was combined with an evaluation of the standard CSU-13 P anti-gravity suit versus three configurations of a 'retrograde inflation anti-G suit'. Results indicated that RT and error rates increased 17 percent and 14 percent respectively from baseline to the end of the simulated aerial combat maneuver and that the most common error was pressing too few buttons.

Popper, S. E.↗

NASA NTRS1988

Quantification of reaction time and time perception during Space Shuttle operations

A microprocessor-based test battery containing simple reaction time, choice reaction time, and time perception tasks was flown aboard a 1985 Space Shuttle flight. Data were obtained from four crew members. Individual subject means indicate a correlation between change in reaction time during the flight and the presence of space motion sickness symptoms. The time perception task results indicate that the shortest duration task time (2 s) is progressively overestimated as the mission proceeds and is statistically significant when comparing preflight and postflight baselines. The tasks that required longer periods of time to estimate (8, 12, and 16 s) are less affected.

Ratino, D. A.↗

NASA NTRS1986

Active controllers and the time duration to learn a task

An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

Repperger, D. W.↗

NASA NTRS1984

Effects of Control Stick Parameters on Human Controller Response

A fixed base laboratory tracking study was conducted to determine the effects of stick displacement and stick force characteristics on human tracking performance. Three different levels of control stick force/displacement characteristics and stick electrical gain were varied to observe their influence on RMS (Root Mean Square) tracking error and RMS control activity (stick output). The results indicated that both RMS tracking error and RMS control activity were influenced by the three different levels of control stick force/displacement characteristics and stick electrical gain. The human neuromotor time constant was affected by the electrical control gain of the stick while the spring stiffness of the stick influenced the time delay characteristics of the human response behavior.

Repperger, D. W.↗

NASA NTRS1984

Active Sticks: a New Dimension in Controller Design

A smart stick controller was built which actively produces a force to interact with the subject's hand and to aid in tracking. When the human tracks in this situation, the man-machine system can be viewed as the combination of two closed loop feedback paths. The inner loop occurs as a result of a tactile information channel effecting the man-controller interaction through force with this stick in the active mode (the stick generates a force) and the passive mode (the stick not generating a force) are reported. The most noteworthy observation is a significant increase in apparent neuromotor bandwidth and consequently better tracking performance.

Repperger, D. W.↗

NASA NTRS1982

A performance analysis study of a complex G field experiment

A performance analysis on data from an experiment which illustrates the degradation of tracking performance as the human is subjected to an environmental stressor is presented. The performance changes are defined and the statistical properties of human tracking with and without the stress effects examined. Performance is evaluated using an extension of a phase plane technique. A quantiative determination of stress effects on performance is defined explicitly in terms of parameters of a density function identified from the empirical data. The distribution functions which characterize tracking in a phase plane representation of the closed loop error signal are determined.

Repperger, D. W.↗

NASA NTRS1981

A study on task difficulty and acceleration stress

The results of two experiments which relate to task difficulty and the effects of environmental stress on tracking performance are discussed and compared to subjective evaluations. The first experiment involved five different sum of sine tracking tasks which humans tracked both in a static condition and under a 5 Gz acceleration stress condition. The second experiment involved similar environmental stress conditions but in this case the tasks were constructed from deterministic functions with specially designed velocity and acceleration profiles. Phase Plane performance analysis was conducted to study potential measures of workload or tracking difficulty.

Repperger, D. W.↗

NASA NTRS1978

A method motion simulator design based on modeling characteristics of the human operator

A design criteria is obtained to compare two simulators and evaluate their equivalence or credibility. In the subsequent analysis the comparison of two simulators can be considered as the same problem as the comparison of a real world situation and a simulation's representation of this real world situation. The design criteria developed involves modeling of the human operator and defining simple parameters to describe his behavior in the simulator and in the real world situation. In the process of obtaining human operator parameters to define characteristics to evaluate simulators, measures are also obtained on these human operator characteristics which can be used to describe the human as an information processor and controller. First, a study is conducted on the simulator design problem in such a manner that this modeling approach can be used to develop a criteria for the comparison of two simulators.

Repperger, D. W.↗

NASA NTRS1977

A relationship between eye movement patterns and performance in a precognitive tracking task

Eye movements made by various subjects in the performance of a precognitive tracking task are studied. The tracking task persented by an antiaircraft artillery (AAA) simulator has an input forcing function represented by a deterministic aircraft fly-by. The performance of subjects is ranked by two metrics. Good, mediocre, and poor trackers are selected for analysis based on performance during the difficult segment of the tracking task and over replications. Using phase planes to characterize both the eye movement patterns and the displayed error signal, a simple metric is developed to study these patterns. Two characterizations of eye movement strategies are defined and quantified. Using these two types of eye strategies, two conclusions are obtained about good, mediocre, and poor trackers. First, the eye tracker who used a fixed strategy will consistently perform better. Secondly, the best fixed strategy is defined as a Crosshair Fixator.

Repperger, D. W.↗

NASA NTRS1977

Using model order tests to determine sensory inputs in a motion study

In the study of motion effects on tracking performance, a problem of interest is the determination of what sensory inputs a human uses in controlling his tracking task. In the approach presented here a simple canonical model (FID or a proportional, integral, derivative structure) is used to model the human's input-output time series. A study of significant changes in reduction of the output error loss functional is conducted as different permutations of parameters are considered. Since this canonical model includes parameters which are related to inputs to the human (such as the error signal, its derivatives and integration), the study of model order is equivalent to the study of which sensory inputs are being used by the tracker. The parameters are obtained which have the greatest effect on reducing the loss function significantly. In this manner the identification procedure converts the problem of testing for model order into the problem of determining sensory inputs.

Repperger, D. W.↗

NASA NTRS1975

Performance evaluation of tracking based on a low pass filter model

The performance of a human in a closed loop tracking task can be determined by using a simple low pass filter model with a least squares identification algorithm. The crossover model and the extended crossover model can be shown to be special cases of the low pass filter model presented here. Performance in tracking can be easily determined by mean square tracking error which can be written in terms of the parameters of the low pass filter model. A closed form expression for the effective time delay is also obtained. Experimental data from a roll axis tracking simulation is presented and simple prediction rules are determined. A comparison is made between this model and the crossover model with respect to their differences and similarities.

Repperger, D. W.↗

NASA NTRS1975

A multiloop approach to modeling motion sensor responses

By using a least squares identification algorithm, a multiloop approach is taken to effectively model the response characteristics of the motion sensors. The inputs to the model include the possible sources of information provided to the human via his motion sensing system. One input models the response of the angular acceleration sensors (second derivative of position) and the second input models the response of the linear sensors (sine of position). The third input is the visual display error provided to the human in the closed loop tracking experiment. Data from a roll axis tracking simulation were analyzed. The major source of pilot lead under the motion mode of operation is discussed for control of plants in which motion information improved performance and in which there was no improvement in performance.

Junker, A. M.↗