An Optimization Model of Self-Paced Tracking

1984 ◽  
Vol 28 (4) ◽  
pp. 398-402
Author(s):  
M. A. Montazer ◽  
Colin G. Drury
Keyword(s):  
Human Performance ◽  
Human Subjects ◽  
Line Drawing ◽  
Manual Control ◽  
Sampled Data ◽  
Control Task ◽  
Drawing Task ◽  
Model Predictions ◽  
Human Operators ◽  
Fixed Boundaries

A model which describes human performance in a self-paced tracking task was developed based on the notion that human operators are intermittent-acting or sampled-data servo-mechanisms. The model had a functional form in terms of the probability of success and failure resulting from the execution of a manual control task such as drawing a line between fixed boundaries. The human operator was modelled as an optimizer, balancing costs and penalties of speeds and errors to achieve a maximum expected payoff. The performance of the model was evaluated by simulating a line drawing task on a digital computer. Model predictions obtained via simulation were compared with the data collected from human subjects performing the actual task in a laboratory setting. The predictions of the model were confirmed, suggesting that human operators can in fact be modelled as optimizers when performing a manual control task.

Development and Evaluation of a Computational Human Performance Model of In-vehicle Manual and Speech Interactions

2018 ◽  
Vol 62 (1) ◽  
pp. 1642-1642
Author(s):  
Heejin Jeong ◽  
Yili Liu
Keyword(s):  
Completion Time ◽  
Human Performance ◽  
Human Subjects ◽  
Manual Control ◽  
Task Completion ◽  
Task Completion Time ◽  
Vehicle Systems ◽  
Lower Lumbar ◽  
Digital Simulations ◽  
Seat Adjustment

Usability evaluation traditionally relies on costly and time-consuming human-subject experiments, which typically involve developing physical prototypes, designing usability experiment, and recruiting human subjects. To minimize the limitations of human-subject experiments, computational human performance models can be used as an alternative. Human performance models generate digital simulations of human performance and examine the underlying psychological and physiological mechanisms to help understand and predict human performance. A variety of in-vehicle information systems (IVISs) using advanced automotive technologies have been developed to improve driver interactions with the in-vehicle systems. Numerous studies have used human subjects to evaluate in-vehicle human-system interactions; however, there are few modeling studies to estimate and simulate human performance, especially in in-vehicle manual and speech interactions. This paper presents a computational human performance modeling study for a usability test of IVISs using manual and speech interactions. Specifically, the model was aimed to generate digital simulations of human performance for a driver seat adjustment task to decrease the comfort level of a part of driver seat (i.e., the lower lumbar), using three different IVIS controls: direct-manual, indirect-manual, and voice controls. The direct-manual control is an input method to press buttons on the touchscreen display located on the center stack in the vehicle. The indirect-manual control is to press physical buttons mounted on the steering wheel to control a small display in the dashboard-cluster, which requires confirming visual feedback on the cluster display located on the dashboard. The voice control is to say a voice command, “ deflate lower lumbar” through an in-vehicle speaker. The model was developed to estimate task completion time and workload for the driver seat adjustment task, using the Queueing Network cognitive architecture (Liu, Feyen, & Tsimhoni, 2006). Processing times in the model were recorded every 50 msec and used as the estimates of task completion time. The estimated workload was measured by percentage utilization of servers used in the architecture. After the model was developed, the model was evaluated using an empirical data set of thirty-five human subjects from Chen, Tonshal, Rankin, & Feng (2016), in which the task completion times for the driver seat adjustment task using commercial in-vehicle systems (i.e., SYNC with MyFord Touch) were recorded. Driver workload was measured by NASA’s task load index (TLX). The average of the values from the NASA-TLX’s six categories was used to compare to the model’s estimated workload. The model produced results similar to actual human performance (i.e., task completion time, workload). The real-world engineering example presented in this study contributes to the literature of computational human performance modeling research.

scholarly journals Human Performance Consequences of Automated Decision Aids

2012 ◽  
Vol 6 (1) ◽  
pp. 57-87 ◽  
Author(s):  
Dietrich Manzey ◽  
Juliane Reichenbach ◽  
Linda Onnasch
Keyword(s):  
Supervisory Control ◽  
Human Performance ◽  
Prior Experience ◽  
Decision Aids ◽  
Manual Control ◽  
Control Task ◽  
Commission Errors ◽  
Performance Consequences ◽  
System Information ◽  
Manual Performance

Two experiments are reported that investigate to what extent performance consequences of automated aids are dependent on the distribution of functions between human and automation and on the experience an operator has with an aid. In the first experiment, performance consequences of three automated aids for the support of a supervisory control task were compared. Aids differed in degree of automation (DOA). Compared with a manual control condition, primary and secondary task performance improved and subjective workload decreased with automation support, with effects dependent on DOA. Performance costs include return-to-manual performance issues that emerged for the most highly automated aid and effects of complacency and automation bias, respectively, which emerged independent of DOA. The second experiment specifically addresses how automation bias develops over time and how this development is affected by prior experience with the system. Results show that automation failures entail stronger effects than positive experience (reliably working aid). Furthermore, results suggest that commission errors in interaction with automated aids can depend on three sorts of automation bias effects: (a) withdrawal of attention in terms of incomplete cross-checking of information, (b) active discounting of contradictory system information, and (c) inattentive processing of contradictory information analog to a “looking-but-not-seeing” effect.

Experimental Study of Virtual Tools With Attributes

1999 ◽  
Author(s):  
T. Kesavadas ◽  
Hari Subramaniam
Keyword(s):  
Virtual Environments ◽  
Marked Improvement ◽  
Human Performance ◽  
Human Subjects ◽  
Task Execution ◽  
Virtual Tools ◽  
Head Mounted Display ◽  
Human Operators ◽  
Welding Processes ◽  
Visual Interfaces

Abstract Use of synthetic and surrogate tools in training robots and planning events within virtual environments has tremendous potential in making programming of complex machines simple and easy. In our research we have experimented the use of attribute laden virtual tools in various tasks such as robotic-based grinding and welding processes. Such attribute laden virtual tools aid human operators in path planning as well as in making decisions about the process itself. In this paper we have tested our concepts of virtual tools and the use of attributes such as physical, reflex and command actions. Four sets of experiments were conducted with human subjects. Two kinds of virtual tools were used in these experiments, one with guide plane attributes and the other without them. One set of experiment tested human performance using the head mounted display interface and the effect of learning process in using the virtual tool. Results showed that there was marked improvement in task execution time using the tools laden with guide plane attributes over the unencumbered virtual tools. This paper discusses various future applications of these virtual tools in manufacturing. It is observed that unlike non-haptic visual interfaces, where no physical feed back is available to the user, attribute laden tools can provide a much easier interface to robots.

scholarly journals Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems

2019 ◽  
pp. 001872081988100 ◽  
Author(s):  
Miguel Martínez-García ◽  
Yu Zhang ◽  
Timothy Gordon
Keyword(s):  
Fractional Order ◽  
Finite Impulse Response ◽  
Human Subjects ◽  
Procedural Memory ◽  
Human Operator ◽  
Human Model ◽  
Manual Control ◽  
Control Task ◽  
Fir Model ◽  
Memory Pattern

Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor. Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a one-dimensional tracking task. Specifically, data recorded from human subjects controlling dynamic systems with different fractional order were investigated. Method: A finite impulse response (FIR) controller was fitted to the data, and pattern analysis of the fitted parameters was performed. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human–machine system in closed-loop was conducted. Results: It is shown that the FIR model can be used to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less than or equal to 1. Conclusion: For systems of different fractional order, the proposed control scheme—based on an FIR model—can effectively characterize the linear properties of manual control in humans. Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are the following: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.

Data-to-sound mapping to sonify ongoing system status in continuous manual control task

2011 ◽  
Author(s):  
Yukio Horiguchi ◽  
Keisuke Yasuda ◽  
Hiroaki Nakanishi ◽  
Tetsuo Sawaragi
Keyword(s):  
Manual Control ◽  
Control Task

scholarly journals Human manual control precision depends on vestibular sensory precision and gravitational magnitude

2018 ◽  
Vol 120 (6) ◽  
pp. 3187-3197 ◽  
Author(s):  
Marissa J. Rosenberg ◽  
Raquel C. Galvan-Garza ◽  
Torin K. Clark ◽  
David P. Sherwood ◽  
Laurence R. Young ◽  
...  
Keyword(s):  
Manual Control ◽  
Control Performance ◽  
Control Task ◽  
Altered Gravity ◽  
Control Precision ◽  
Functional Implications ◽  
Potential Risk Factors ◽  
And Performance ◽  
Human Exploration ◽  
Roll Tilt

Precise motion control is critical to human survival on Earth and in space. Motion sensation is inherently imprecise, and the functional implications of this imprecision are not well understood. We studied a “vestibular” manual control task in which subjects attempted to keep themselves upright with a rotational hand controller (i.e., joystick) to null out pseudorandom, roll-tilt motion disturbances of their chair in the dark. Our first objective was to study the relationship between intersubject differences in manual control performance and sensory precision, determined by measuring vestibular perceptual thresholds. Our second objective was to examine the influence of altered gravity on manual control performance. Subjects performed the manual control task while supine during short-radius centrifugation, with roll tilts occurring relative to centripetal accelerations of 0.5, 1.0, and 1.33 GC (1 GC = 9.81 m/s2). Roll-tilt vestibular precision was quantified with roll-tilt vestibular direction-recognition perceptual thresholds, the minimum movement that one can reliably distinguish as leftward vs. rightward. A significant intersubject correlation was found between manual control performance (defined as the standard deviation of chair tilt) and thresholds, consistent with sensory imprecision negatively affecting functional precision. Furthermore, compared with 1.0 GC manual control was more precise in 1.33 GC (−18.3%, P = 0.005) and less precise in 0.5 GC (+39.6%, P < 0.001). The decrement in manual control performance observed in 0.5 GC and in subjects with high thresholds suggests potential risk factors for piloting and locomotion, both on Earth and during human exploration missions to the moon (0.16 G) and Mars (0.38 G). NEW & NOTEWORTHY The functional implications of imprecise motion sensation are not well understood. We found a significant correlation between subjects’ vestibular perceptual thresholds and performance in a manual control task (using a joystick to keep their chair upright), consistent with sensory imprecision negatively affecting functional precision. Furthermore, using an altered-gravity centrifuge configuration, we found that manual control precision was improved in “hypergravity” and degraded in “hypogravity.” These results have potential relevance for postural control, aviation, and spaceflight.

Exploring the Design Space Using a Surrogate Model Approach With Digital Human Modeling Simulations

2018 ◽  
Author(s):  
Salman Ahmed ◽  
Mihir Sunil Gawand ◽  
Lukman Irshad ◽  
H. Onan Demirel
Keyword(s):  
Human Factors ◽  
Surrogate Model ◽  
Human Performance ◽  
Design Method ◽  
Human Subjects ◽  
Design Stage ◽  
Digital Human Modeling ◽  
Design Variables ◽  
Human Modeling ◽  
Digital Human

Computational human factors tools are often not fully-integrated during the early phases of product design. Often, conventional ergonomic practices require physical prototypes and human subjects which are costly in terms of finances and time. Ergonomics evaluations executed on physical prototypes has the limitations of increasing the overall rework as more iterations are required to incorporate design changes related to human factors that are found later in the design stage, which affects the overall cost of product development. This paper proposes a design methodology based on Digital Human Modeling (DHM) approach to inform designers about the ergonomics adequacies of products during early stages of design process. This proactive ergonomics approach has the potential to allow designers to identify significant design variables that affect the human performance before full-scale prototypes are built. The design method utilizes a surrogate model that represents human product interaction. Optimizing the surrogate model provides design concepts to optimize human performance. The efficacy of the proposed design method is demonstrated by a cockpit design study.

EXPRESS: Drawing Individual Images Benefits Recognition Accuracy in the DRM Paradigm

2021 ◽  
pp. 174702182110564
Author(s):  
Jacob Namias ◽  
Mark Huff ◽  
Allison Smith ◽  
Nicholas Maxwell
Keyword(s):  
Signal Detection ◽  
False Memory ◽  
False Recognition ◽  
Recognition Accuracy ◽  
Control Group ◽  
Control Task ◽  
Drm Paradigm ◽  
Memory Accuracy ◽  
Reduction Experiment ◽  
Drawing Task

We examined the effects of drawing on correct and false recognition within the Deese/Roediger-McDermott (DRM) false memory paradigm. In Experiment 1, we compared drawing of a word’s referent using either a standard black pencil or colored pencils relative to a read-only control group. Relative to reading, drawing in either black or colored pencil similarly boosted correct recognition and reduced false recognition. Signal-detection analyses indicated that drawing reduced the amount of encoded memory information for critical lures and increased monitoring, indicating that both processes contributed to the false recognition reduction. Experiment 2 compared drawing of individual images of DRM list items relative to drawing integrated images using sets of DRM list items. False recognition was lower for drawing of individual images relative to integrated images—a pattern that reflected a decrease in encoded memory information but not monitoring. Therefore, drawing individual images improves memory accuracy in the DRM paradigm relative to a standard read-control task and an integrated drawing task, which we argue is due to the recruitment of item-specific processing.

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