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The Man-Machine Integration Design & Analysis System (MIDAS): Recent Improvements . Sandra G. Hart Brian F. Gore Peter A. Jarvis NASA Ames Research Center Moffett Field, CA 94035 [email protected]/650 604 6072 10/19/04. Outline. Human Performance Modeling

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the man machine integration design analysis system midas recent improvements

The Man-Machine Integration Design & Analysis System (MIDAS): Recent Improvements

Sandra G. Hart

Brian F. Gore

Peter A. Jarvis

NASA Ames Research Center

Moffett Field, CA 94035

[email protected]/650 604 6072


  • Human Performance Modeling
  • MIDAS Phase 1: Initial design
  • Early applications
  • MIDAS Phase 2: Move from Lisp to C++
  • Recent applications
  • MIDAS Phase 3: PC Port/Integrate Apex
human performance models components
Human Performance Models: Components

Psychological Models


Sensory Models

Task Network

Anthropometric Models

Performance: WL

Biodynamic Models

Performance: Time

Model Architecture,

Library, Tools

Team/Org Models

Performance: SA

Vehicle Models

Performance: Errors

Equipment Models


Environment Models

FoV/Reach Envelope



human performance models architectures
Human Performance Models: Architectures

Anthropometric/Biodynamic: Physical characteristics of human body; static & dynamic; population characteristics; limitations [RAMSIS, JACK]

  • Task network: Top-down, based on sequences of human/system tasks (derived from task analysis

Psychological theories, mathematical models, descriptive functions

Cognitive: Bottom-up, combine theory-based models of memory, decision making, perception, attention, movement, etc

Vision: Computational representation of the way the human visual system processes an image to predict performance given image characteristics

human performance models can
Human Performance Models can…
  • Generate hardware, software, training requirements for tasks that will involve human operators
  • Depict operators performing tasks in prototype workspaces and/or in remote or risky environments
  • Perform tradeoff analyses among alternative designs and candidate procedures, saving time and money
  • Identify general human/system vulnerabilities to estimate overall system performance and reliability
  • Provide dynamic, animated examples for trainingand developers
  • Generate realistic schedules and procedures
Data InputOverview

A comprehensive suite of computational tools - - 3D rapid prototyping, models of perception, cognition, response, real- and fast-time simulation, performance analysis, visualization - - for designing and analyzing human/machine systems was developed primarily in Lisp on a fleet of SGIs

Data Analysis

Run Time Visualization

  • Pioneered the development of an engineering design environment with integrated tools for rapid prototyping, visualization, simulation and analysis
  • Advanced the capabilities and use of computational representations of human performance in design including a state of the art anthropometric model (Jack®)
  • Flexible enough to support a range of potential users and target applications


  • Component models written in Lisp, Fortran, C, C++
  • Required a suite of SGI machines
  • Modeled a single operator
  • Time based rather than event based; scheduler established optimal inter-leaving of task components
  • No emergent behaviors
richmond ca police 911 dispatch
Richmond, CA Police: 911 Dispatch
  • Goal: Upgrade the facilities and procedures used in the 911 dispatch facility
  • Accomplished:
    • Modeled control console and dispatch activities in MIDAS
    • Evaluated prototype graphical decision aid
us army air warrior
US Army Air Warrior
  • Goal: Establish baseline performance measures for crews flying Longbow Apache with and without MOPP gear
  • Accomplished:
    • Modeled copilot/gunner with Jack® (95th male <> 5th female)
    • Rendered cockpit using CAD files from manufacturer
    • Simulated performance of more than 400 activities
    • Measured reach, FoV, workload, timelines
short haul civil tiltrotor
Short Haul/Civil Tiltrotor
  • Goal: Evaluate crew performance/workload issues for steep (9º), noise abatement approaches into a vertiport
  • Accomplished:
    • Constructed MIDAS models of normal and aborted approaches
    • Contrasted impact of manual vs automated nacelle control modes
nasa shuttle upgrade
NASA Shuttle Upgrade
  • Goal: Support development of an advanced orbiter cockpit with an improved display/control design
  • Accomplished:
    • Created virtual rendition of current shuttle cockpit
    • Conducted simulation of first 8 min of nominal ascent
    • Provided quantitative measures of workload/SA, timing
  • Decreased model development from months to weeks
  • Increased run-time efficiency from 50x RT to near RT
  • Multiple operators
  • Modeled external vision, audition, situation awareness
  • Conditional behaviors emerging from interaction of top-down goals and environmentally driven contexts
  • Option of non-proprietary “head & hands” model


  • The interface still user un-friendly
  • SGI platform
  • Cognitive models no longer state of the art
  • Performance moderating functions not integrated
user interface
User Interface
  • The interactive graphical user interface is used to create models, specify and run simulations, and view data. It is organized into a hierarchical series of screens or editors that are navigated with tabs
  • Different views of the simulation are offered: Structure, Geometry, Outline, Animation, Real-time/post-hoc data
vehicle models
Vehicle Models
  • A modeled vehicle represents the combination of a guidance/ dynamics model and a visual representation
  • The guidance/dynamics model moves the vehicle along a prescribed route. MIDAS provides two:
    • NoE helicopter model
    • Simple point mass model (used to model arbitrary vehicles in a generic way)
  • The visual representation is CAD geometry chosen from the MIDAS library or developed by the modeler.
environment model
Environment Model
  • Tools are provided to model the environment outside the crew station (e.g., terrain, weather, etc)
  • Terrain is modeled as a single object
  • Features are simple objects that have no inherent behavior and do not move
  • One weather condition may be applied to the environment by specifying lighting/visibility (these are used by the visual perception model)
crew station equipment models
Crew Station/Equipment Models
  • The “crew station” is a collection of equipment with which operators interact
  • Crew station models may be given a graphical representation for animation
  • Multiple crew stations per vehicle and multiple operators per crew station possible
anthropometric models
Anthropometric Models
  • Anthropometric models provide an animated, 3D graphical representation of one or more modeled human operators for visualization
  • Jack ® (developed at U Penn/distributed by UGS): full-body figure & realistic movements
  • Head and Hands model: government-developed representation adequate for many purposes for users without a Jack license
vision models
Vision Models
  • Visual attention modeled as single “cone”, varying from 3-15º based on task type.
  • External vision:
    • Peripheral: 160 degrees
    • Foveal: 2.5 degrees
  • Perceivability: f(visibility, size, distance, local contrast ratio)
  • Perception level: f(dwell time, perceivability)
  • Levels of Perception:
    • Detection
    • Recognition
    • Identification
  • Internal vision:
    • Symbolic (check read)
    • Digital (exact value)
    • Text (character string)
auditory model
Auditory Model
  • Only within crew station
  • External sounds are represented only if channeled through equipment
  • Two Stages of Processing:
    • Detection
    • Comprehension
  • Content:
    • Verbal strings
    • Signals
  • All or none processing (Interruptions disrupt entire message)
symbolic operator models
Symbolic Operator Models
  • Significant advance over earlier version, which required specification of all activities at primitive task level
  • High-level scripting language, Operator Procedure Language (OPL) serves as front-end to a reactive planning system (RAPS)
  • User-supplied procedures are instructions for accomplishing tasks
  • Manages knowledge and beliefs, integrates human actions with scenario events
Memory Model
  • Simpler model than in MIDAS 1.0
  • Distinction between long-term/short-term memory was lost
  • Memories are represented as a database of assertions or beliefs that are symbolic expressions describing the property of objects
  • Memory can be examined by powerful tools in a querying language built into OPL
attention model
















Attention Model
  • Based on Wicken’s Multiple Resource Model.
  • Acts as a mediator that maintains an account of attention resources in six different “channels”
  • Necessary attention resources must be available before primitive tasks are initiated
  • Task onset may be delayed if insufficient resources
output behavior models
Output Behavior Models
  • If required resources are available an activity that corresponds to a primitive procedure is created
  • Physical actions and their effects on equipment or environmental objects are modeled, regulated by a motor control process
  • 60+ primitive tasks are available in a Procedure Library with pre-defined load values; easy to add more
simulation system
Simulation System
  • Engine/executive (uses discrete-event, fixed-time increment approach for advancing the simulation)
  • Data collection mechanisms for generating runtime data that is graphically displayed which the simulation runs and is saved for post-run analyses
  • Event generation mechanism provides a way for timed events to occur on schedule or with stochastic variance
  • Provisioning system allows users to change the simulation and re-run without re-loading/re-starting
workload situation awareness
Workload & Situation Awareness
  • Workload calculations based on McCracken & Aldrich (1988)
    • Load levels for Visual, Auditory, Cognitive, and Psychomotor dimensions are defined for task primitives on a scale of 1-7
    • Momentary load based on aggregation
  • SA calculations based on:
    • Ratio of operator’s relevant knowledge/required knowledge
    • Distinguishes actual SA from perceived SA

Situational elements can be objects in the crew station or the environment that define a “situation” or are in the operator’s memory and are operationally relevant.

  • WL and SA values offer a powerful way to simulate realistic errors
comparison of models to simulator data
Nominal baseline approach/landing and late runway reassignment (sidestep) with and without SVS display

1000' Lineup on Final

ATC-Commanded Runway Sidestep

850' Ceiling

650' Missed Approach

Parallel Runways

Comparison of Models to Simulator Data


U of Illinois

Rice University


BBN Technologies


San Jose State University


MAAD & CarnegieMellon

A - SA

U of Illinois

Goodness-of-fit of individual model outputs to empirical data

Preliminary timeline, SA, attention, wkld, analysis,task execution times error vulnerabilities



nominal approach landing simulation
Nominal Approach & Landing Simulation
  • PF scanning for TFX, runway
  • PNF monitoring PFD, Nav
  • PF/PNF monitoring radio
  • Flaps 30º/set & confirm
  • PF requests before landing checklist
  • PNF checks/responds hear down
  • PF confirms visually/verbally
  • PNF checks/responds flaps 30
  • PF confirms visually/verbally
  • PNF checks/responds speed brakes set
  • PF confirms visually/verbally
  • PNF declares checklist complete
  • PF sets/declares DA at 650
  • PNF visually confirms DA set
  • Note passing FAF
  • Confirms final descent initiated
traffic call during approach
Traffic Call During Approach
  • Final approach checklist is complete
  • ATC call with traffic advisory
  • Both pilots scan for traffic “I don’t see it”
  • Neither pilot notices as the decision altitude is passed
  • After the fact, the First Officer notices: “We’re past FAF and not descending”
  • Crew must decide whether to continue with the approach or abort
life sciences glove box
Life Sciences Glove Box

Virtual Glovebox

  • Challenges:
    • Astronauts must follow detailed instructions within strict time constraints; failure to do so introduces risk of science mission failure

Role of Computational Modeling

    • Predict interactive influences of microgravity (posture, bracing, precise movements, placing, moving, stowing) to develop/evaluate procedures
    • Watching an animated dry run enables efficient communication among scientists, implementers, astronauts; more effective training

Life Sciences Glovebox Payload Development Unit received at Ames from the National Aerospace Development Agency of Japan (NASDA)

Onboard KC-135

MIDAS rendering

life sciences glove box simulation
Life Sciences Glove Box Simulation
  • Goal: Predict astronauts’ performance of complex experiments designed to answer questions about living organisms’ adaptation to the space environment
  • Objectives:Evaluate feasibility of following proposed procedures within time/performance constraints; ID factors that will increase risk of mission failure [e.g., waiting too long to photograph slides; interruptions; task requires (unavailable) resource(s)]
  • The Task:
    • Turn on experimental equipment (monitor, microscope, camera)
    • Measure cell density/viability for each of 6 samples
      • Invert sample vial
      • Place aliquot of sample on slide
      • Place drop of viability stain in sample
      • Record time on sample record
      • Place cover slip on slide
      • Observe on microscope
      • Take photographs within specific time window
    • Dispose of trash, return vials to containers, turn equipment off
midas v3 0 features
MIDAS v3.0 Features
  • Runs on high-end PC
  • Simple model of microgravity influence on performance
  • Physics model of microgravity impact on objects available
  • Simple within-task fatigue model implemented
  • Fatigue state model (U Penn/Astronaut Scheduling Assistant) selected
  • Notion of task duration - - how long a task should take as well as how long it did take
  • Grasping, moving, manipulating objects in workspace
  • Apex will become the heart of the Task Manager and enable multi-tasking, task prioritization, shedding, deferral, resumption
  • Task primitive definitions include failure modes (time/quality) that enable the occurrence of emergent behaviors
  • Mission success/performance measures computed: vulnerability to error, slipped schedules; performance degradation
midas v3 0 structure
MIDAS v3.0 Structure

Task Manager






Task Network

List of Tasks/Procedures




Mission Environment

Physical Simulation





Workstation Geometry

Fit/Reach/Vis envelope

Dynamic models



Dynamic Animation


Primitive tasks

Human model

Task state

Operator state

Mission success

Cognitive simulation

Behavior modifiers

Situation Awareness

Error, Workload


Operator Characteristics

Performance measures

  • MIDAS 3.0 now operates on a PC platform and will soon incorporate significantly enhanced cognitive model (Apex)
  • MIDAS 3.0 gives users the ability to model the functional and physical aspects of a variety of operators, systems, and environments.
  • It brings these models together in an interactive, event-filled simulation for quantitative and visual analysis
  • The interplay between top-down and bottom-up processes and a suite of performance modifying functions enables the emergence of un-forseen, un-scripted behaviors
  • The government has done what it set out to do - - spur development of human performance modeling tools integrated into a design environment
  • Our goal is to continue to add functionality with each new application