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63 records · Page 4

Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System

While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. These reports can be searched in a variety of ways. This paper describes methods for systematically examining ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), various approaches and tools for such inquiries are described. The approach, process, challenges, and suggestions to building an operator-based description of resilient performance are discussed, and tools to facilitate data analysis to maximize learning from these reports are described.

ASRS narratives↗

Where to look? Automating attending behaviors of virtual human characters

This research proposes a computational framework for generating visual attending behavior in an embodied simulated human agent. Such behaviors directly control eye and head motions, and guide other actions such as locomotion and reach. The implementation of these concepts, referred to as the AVA, draws on empirical and qualitative observations known from psychology, human factors and computer vision. Deliberate behaviors, the analogs of scanpaths in visual psychology, compete with involuntary attention capture and lapses into idling or free viewing. Insights provided by implementing this framework are: a defined set of parameters that impact the observable effects of attention, a defined vocabulary of looking behaviors for certain motor and cognitive activity, a defined hierarchy of three levels of eye behavior (endogenous, exogenous and idling) and a proposed method of how these types interact.

NASA Discipline Space Human Factors↗

Considerations for Implementing Voice-Controlled Spacecraft Systems Through a Human-Centered Design Approach

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. There are still recognition challenges to overcome such as background noise, human speech variability, and task loading. However, implemented correctly, a voice-controlled spacecraft system (VCSS) can provide a useful, natural and efficient form of human-machine communications during complex tasks such as when the hands and eyes are busy or as an aid for vehicle situational awareness inquiries or collaborative human-robot tasks. This document provides considerations and guidance for the use of SRT in VCSS applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing. Available from

Salazar, George A.↗

Development Considerations for Implementing a Voice-Controlled Spacecraft System

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. This paper provides considerations and guidance for the use of SRT in voice-controlled spacecraft systems (VCSS) applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for the development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing.

Salazar, George↗

How I Love My 80 Percenters

Don't ever take your support staff for granted. By support staff, I mean the people in personnel, logistics, and finance; the ones who can make things happen with a phone call or a signature, or by the same token frustrate you to no end by their inaction; these are people you must depend on. I've spent a lot of time thinking about how to cultivate relationships with my support staff that work to the advantage of both of us. The most important thing that have learned working with people, any people--and I will tell you how I learned this in a minute--is there are some folks you just can't motivate, so forget it, don't try; others you certainly can with a little psychology and some effort; and the best of the bunch, what I call the 80 percenters, you don't need to motivate because they're already on the team and performing beautifully. The ones you can't change are rocks. Face up to it, and just kick them out of your way. I have a reputation with the people who don't want to perform or be part of the team. They don't come near me. If someone's a rock, I pick up on it right away, and I will walk around him or her to find someone better. The ones who can be motivated I take time to nurture. I consider them my projects. A lot of times these wannabes are people who want to help but don't know how. Listen, you can work with them. Lots of people in organizations have the mindset that all that matters are the regulations. God forbid if you ever work outside those regulations. They've got one foot on that regulation and they're holding it tight like a baby holds a blanket. What you're looking for is that first sign that their minds are opening. Usually you hear it in their vocabulary. What used to sound like "We can't do that ... the regulations won't allow it ... we have never done this before," well, suddenly that changes to "We have options ... let's take a look at the options ... let me research this and get back to you." The 80 percenters you want to nurture too, but they're already on the team. The 80 percenters know the regulation, but if there is a problem because of a regulation, they will still tell you, "Don't worry about it, I've got it under control." These people don't see themselves as guardians of the regulations. Instead, they see themselves as customer service representatives. Two 80 percenters on my team are Hettie Courtney at Goddard Space Flight Center and Debbie Randall at NASA Headquarters in Code B. Thank you, ladies, very much. You are the best.

Maturo, Anthony J.↗

Self-motion perception: assessment by real-time computer-generated animations

We report a new procedure for assessing complex self-motion perception. In three experiments, subjects manipulated a 6 degree-of-freedom magnetic-field tracker which controlled the motion of a virtual avatar so that its motion corresponded to the subjects' perceived self-motion. The real-time animation created by this procedure was stored using a virtual video recorder for subsequent analysis. Combined real and illusory self-motion and vestibulo-ocular reflex eye movements were evoked by cross-coupled angular accelerations produced by roll and pitch head movements during passive yaw rotation in a chair. Contrary to previous reports, illusory self-motion did not correspond to expectations based on semicircular canal stimulation. Illusory pitch head-motion directions were as predicted for only 37% of trials; whereas, slow-phase eye movements were in the predicted direction for 98% of the trials. The real-time computer-generated animations procedure permits use of naive, untrained subjects who lack a vocabulary for reporting motion perception and is applicable to basic self-motion perception studies, evaluation of motion simulators, assessment of balance disorders and so on.

Non-NASA Center↗

Small Vocabulary Recognition Using Surface Electromyography in an Acoustically Harsh Environment

This paper presents results of electromyographic-based (EMG-based) speech recognition on a small vocabulary of 15 English words. The work was motivated in part by a desire to mitigate the effects of high acoustic noise on speech intelligibility in communication systems used by first responders. Both an off-line and a real-time system were constructed. Data were collected from a single male subject wearing a fireghter's self-contained breathing apparatus. A single channel of EMG data was used, collected via surface sensors at a rate of 104 samples/s. The signal processing core consisted of an activity detector, a feature extractor, and a neural network classifier. In the off-line phase, 150 examples of each word were collected from the subject. Generalization testing, conducted using bootstrapping, produced an overall average correct classification rate on the 15 words of 74%, with a 95% confidence interval of [71%, 77%]. Once the classifier was trained, the subject used the real-time system to communicate and to control a robotic device. The real-time system was tested with the subject exposed to an ambient noise level of approximately 95 decibels.

Betts, Bradley J.↗

Scripting Module for the Satellite Orbit Analysis Program (SOAP)

This add-on module to the SOAP software can perform changes to simulation objects based on the occurrence of specific conditions. This allows the software to encompass simulation response of scheduled or physical events. Users can manipulate objects in the simulation environment under programmatic control. Inputs to the scripting module are Actions, Conditions, and the Script. Actions are arbitrary modifications to constructs such as Platform Objects (i.e. satellites), Sensor Objects (representing instruments or communication links), or Analysis Objects (user-defined logical or numeric variables). Examples of actions include changes to a satellite orbit ( v), changing a sensor-pointing direction, and the manipulation of a numerical expression. Conditions represent the circumstances under which Actions are performed and can be couched in If-Then-Else logic, like performing v at specific times or adding to the spacecraft power only when it is being illuminated by the Sun. The SOAP script represents the entire set of conditions being considered over a specific time interval. The output of the scripting module is a series of events, which are changes to objects at specific times. As the SOAP simulation clock runs forward, the scheduled events are performed. If the user sets the clock back in time, the events within that interval are automatically undone. This script offers an interface for defining scripts where the user does not have to remember the vocabulary of various keywords. Actions can be captured by employing the same user interface that is used to define the objects themselves. Conditions can be set to invoke Actions by selecting them from pull-down lists. Users define the script by selecting from the pool of defined conditions. Many space systems have to react to arbitrary events that can occur from scheduling or from the environment. For example, an instrument may cease to draw power when the area that it is tasked to observe is not in view. The contingency of the planetary body blocking the line of sight is a condition upon which the power being drawn is set to zero. It remains at zero until the observation objective is again in view. Computing the total power drawn by the instrument over a period of days or weeks can now take such factors into consideration. What makes the architecture especially powerful is that the scripting module can look ahead and behind in simulation time, and this temporal versatility can be leveraged in displays such as x-y plots. For example, a plot of a satellite s altitude as a function of time can take changes to the orbit into account.

Carnright, Robert↗

Using Natural Language to Enhance Mission Effectiveness

The availability of highly capable, yet relatively cheap, unmanned aerial vehicles (UAVs) is opening up new areas of use for hobbyists and for professional-related activities. The driving function of this research is allowing a non-UAV pilot, an operator, to define and manage a mission. This paper describes the preliminary usability measures of an interface that allows an operator to define the mission using speech to make inputs. An experiment was conducted to begin to enumerate the efficacy and user acceptance of using voice commands to define a multi-UAV mission and to provide high-level vehicle control commands such as "takeoff." The primary independent variable was input type - voice or mouse. The primary dependent variables consisted of the correctness of the mission parameter inputs and the time needed to make all inputs. Other dependent variables included NASA-TLX workload ratings and subjective ratings on a final questionnaire. The experiment required each subject to fill in an online form that contained comparable required information that would be needed for a package dispatcher to deliver packages. For each run, subjects typed in a simple numeric code for the package code. They then defined the initial starting position, the delivery location, and the return location using either pull-down menus or voice input. Voice input was accomplished using CMU Sphinx4-5prealpha for speech recognition. They then inputted the length of the package. These were the option fields. The subject had the system "Calculate Trajectory" and then "Takeoff" once the trajectory was calculated. Later, the subject used "Land" to finish the run. After the voice and mouse input blocked runs, subjects completed a NASA-TLX. At the conclusion of all runs, subjects completed a questionnaire asking them about their experience in inputting the mission parameters, and starting and stopping the mission using mouse and voice input. In general, the usability of voice commands is acceptable. With a relatively well-defined and simple vocabulary, the operator can input the vast majority of the mission parameters using simple, intuitive voice commands. However, voice input may be more applicable to initial mission specification rather than for critical commands such as the need to land immediately due to time and feedback constraints. It would also be convenient to retrieve relevant mission information using voice input. Therefore, further on-going research is looking at using intent from operator utterances to provide the relevant mission information to the operator. The information displayed will be inferred from the operator's utterances just before key phrases are spoken. Linguistic analysis of the context of verbal communication provides insight into the intended meaning of commonly heard phrases such as "What's it doing now?" Analyzing the semantic sphere surrounding these common phrases enables us to predict the operator's intent and supply the operator's desired information to the interface. This paper also describes preliminary investigations into the generation of the semantic space of UAV operation and the success at providing information to the interface based on the operator's utterances.

Trujillo, Anna C.↗