Richard J. Adams, Aaron Olowin, and Eileen Krepkovich

Barron Associates Incorporated, Charlottesville, Virginia

Blake Hannaford and Jack I. C. Lindsay

University of Washington, Seattle, Washington

Peter Homer

Flagsuit LLC, Southwest Harbor, Maine

James T. Patrie

Statistical Consultant, Charlottesville, Virginia

O. Scott Sands

Glenn Research Center, Cleveland, Ohio

Glove-Enabled Computer Operations (GECO):

Design and Testing of an Extravehicular Activity 

Glove Adapted for Human-Computer Interface

NASA/TM—2013-216546

August 2013
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Richard J. Adams, Aaron Olowin, and Eileen Krepkovich

Barron Associates Incorporated, Charlottesville, Virginia

Blake Hannaford and Jack I. C. Lindsay

University of Washington, Seattle, Washington

Peter Homer

Flagsuit LLC, Southwest Harbor, Maine

James T. Patrie

Statistical Consultant, Charlottesville, Virginia

O. Scott Sands

Glenn Research Center, Cleveland, Ohio

Glove-Enabled Computer Operations (GECO):

Design and Testing of an Extravehicular Activity 

Glove Adapted for Human-Computer Interface

NASA/TM—2013-216546

August 2013

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

43rd International Conference on Environmental Systems

sponsored by the American Institute for Aeronautics and Astronautics

Vail, Colorado, July 14–18, 2013
Acknowledgments

We gratefully acknowledge the support of NASA Glenn Research Center. Work on the Glove-Enabled Computer Operations 

(GECO) System was performed under contract NNX11CA55C. Signifi cant contributors to the GECO software development 

included John Reilly and Rebecca Bird (Barron Associates, Incorporated).

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

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Available electronically at http://www.sti.nasa.gov

Trade names and trademarks are used in this report for identifi cation 

only. Their usage does not constitute an offi cial endorsement, 

either expressed or implied, by the National Aeronautics and 

Space Administration.

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2013-216546 1 

Glove-Enabled Computer Operations (GECO): Design and 

Testing of an Extravehicular Activity Glove Adapted for 

Human-Computer Interface 

Richard J. Adams, Aaron Olowin, and Eileen Krepkovich 

Barron Associates Incorporated 

Charlottesville, Virginia 22901 

Blake Hannaford and Jack I. C. Lindsay 

University of Washington 

Seattle, Washington 98195 

Peter Homer 

Flagsuit LLC 

Southwest Harbor, Maine 04679 

James T. Patrie 

Statistical Consultant 

Charlottesville, Virginia 22903 

O. Scott Sands 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

The Glove-Enabled Computer Operations (GECO) system enables an extravehicular activity (EVA) 

glove to be dual-purposed as a human-computer interface device. This paper describes the design and 

human participant testing of a right-handed GECO glove in a pressurized glove box. As part of an 

investigation into the usability of the GECO system for EVA data entry, twenty participants were asked to 

complete activities including (1) a Simon Says Games in which they attempted to duplicate random 

sequences of targeted finger strikes and (2) a Text Entry activity in which they used the GECO glove to 

enter target phrases in two different virtual keyboard modes. In a within-subjects design, both activities 

were performed both with and without vibrotactile feedback. Participants’ mean accuracies in correctly 

generating finger strikes with the pressurized glove were surprisingly high, both with and without the 

benefit of tactile feedback. Five of the subjects achieved mean accuracies exceeding 99% in both 

conditions. In Text Entry, tactile feedback provided a statistically significant performance benefit, 

quantified by characters entered per minute, as well as reduction in error rate. Secondary analyses of 

responses to a NASA Task Loader Index (TLX) subjective workload assessments reveal a benefit for 

tactile feedback in GECO glove use for data entry. This first-ever investigation of employment of a 

pressurized EVA glove for human-computer interface opens up a wide range of future applications, 

including text “chat” communications, manipulation of procedures/checklists, cataloguing/annotating 

images, scientific note taking, human-robot interaction, and control of suit and/or other EVA systems. 

1.0 Introduction 

Future space suits may be equipped with on-board computing, networking, and helmet-mounted 

graphical displays (Refs. 1 and 2) to provide astronauts with access to some of the same “apps” that 

terrestrial smart phone users now take for granted. These may include textual and voice communications, 
NASA/TM—2013-216546 2 

map-based navigation, video/image acquisition, document viewing/editing, and news/weather alerts. 

Currently, few practical solutions exist to provide the point-and-click and rapid textual/numeric data entry 

capabilities that many of these applications require. The GECO system permits gloved hands to be used 

as human-computer interface devices by crewmembers during suited EVA, emulating the functionality 

normally provided by a standard mouse, keyboard, or other conventional input method. It is the embodiment 

of the tactile data entry capability identified in NASA’s integrated technology roadmap (Ref. 3). 

Traditional interfaces such as a keypad/keyboard, mouse, and touch screen displays are poorly 

adapted for use by a hand confined with a space suit glove. EVA gloves, dating from the Gemini program 

to those used today on the International Space Station, have been constructed of at least three layers. 

Closest to the hand is an impermeable bladder that maintains the pressurized environment inside of the 

suit. The pressure-induced loads are carried by a fabric restraint layer, augmented by metal reinforcements (such as a palm bar). Together, the bladder and restraint layer comprise the “pressure glove,” 

designed to achieve near-constant volume, and thus minimize joint torques and fatigue, during use. 

Outside of the pressure glove, a thermal and micrometeoroid garment (TMG) provides protection from 

punctures/abrasion, temperature extremes, and penetration by micrometeoroid particles (Ref. 4). 

Unfortunately, these elements which afford protection from environmental hazards impede the dexterity, 

tactility, and mobility required to manipulate traditional human-machine interfaces (Ref. 5). 

The GECO system takes a different approach, leveraging EVA glove design features as platforms for 

instrumentation and feedback elements that turn the gloves themselves into human-computer interface 

devices. Rather than being barriers between the human operator and a data entry device, the GECO gloves 

ARE data entry devices. Each finger of a glove can be thought of as a button that can be mapped to any 

number of functions (e.g., a mouse click, a keyboard strike, or a control toggle). Hand translation can be 

interpreted as the movement of a mouse, driving cursor position on a graphical user interface (GUI), or a 

change in hand position on a virtual keyboard, altering the mapping between fingers and keys. The 

addition of programmable vibrotactile feedback enriches the interface by replicating the haptic sensations 

that humans intuitively associate with an input device (e.g., the resistance/recoil of a button, the feel of an 

edge). Potential EVA applications include text chat communications (enabling time delayed and low 

bandwidth dialog), working with procedures/checklists (e.g., scrolling, moving a focus bar, marking 

steps/instruction as complete), cataloguing/annotating images (e.g., during geological exploration, survey, 

and sample collection), note taking, human-robot interaction (e.g., shared autonomy, supervisory control, 

and/or direct tele-manipulation), or simply replacing Apollo-era suit controls. 

The introduction of tactile feedback within the GECO design aims to increase user performance and 

reduce mental workload. User displays often over-rely on other perceptual channels (in particular visual), 

resulting in taxed cognitive resources in an EVA environment already saturated with information sources. 

The consequences of information overload can include poor task performance, poor multi-tasking 

efficiency, and higher incidence of operator errors (Ref. 6). Compared to visual and audio, touch is an 

underused sensory pathway. For EVA applications, this creates an opportunity for presentation of 

information with reduced cognitive loading. 

Speech-to-text and voice recognition present potential alternatives to the manual/touch-based 

approach enabled by GECO, with the possibility of providing hands-free and heads-up operation. 

Previous research employing the MX-2 neutral buoyancy space suit analogue included speech 

recognition, combined with feedback through both auditory and graphical display, to enable both 

advanced controls and human-robotic interaction (Refs. 7 and 8). Spoken dialogue system have been 

tested by NASA to support human-computer interaction, including recent strides in addressing robustness 

to in-suit noise and discernment of context (Refs. 9 and 10). Voice/aural-based systems have the potential 

to be effective in well-structured tasks in which the application software has been tailored to the strengths 

and weaknesses of the interface. They are, however, mal-suited for activities that involve spatial 

information. Common GUI interactions as fundamental as clicking a button, moving a slider bar, 

selecting an icon, or positioning a cursor in a text field range from problematic to extremely difficult 

using voice. These limitations explain, in part, why after decades of development such systems have yet 

to challenge the dominance of the keyboard and mouse in the consumer market. Speech-to-text and voice 
NASA/TM—2013-216546 3 

recognition systems will almost certainly be part of future EVA systems, but will be highly limiting if 

they are the only data entry mode available. The most effective solution will likely be a synergistic 

combination of human-machine interaction technologies, including audio, manual/tactile, and visual 

modalities. 

Humans’ ability to process visual information at an extremely rapid rate make graphical display an 

essential component of an effective computer interface. Helmet displays are identified in NASA’s 

integrated technology roadmap (Ref. 3) as part of the future suit architecture, and are the subject of 

significant past and current research (Ref. 2). Unfortunately, without an effective data entry method, the 

display is only half a solution. Taking advantage of a graphical environment for computer interface (e.g., 

Mac OS, Windows, and Ubuntu) requires the ability to point, select, and move objects. The modern 

computer mouse, in fact, emerged from this requirement. Its enduring popularity is a testimony to the 

primacy of the hand as the most effective medium through which spatial information and discrete events 

are communicated from a human to a computer. The emergence of the touchscreen as the primary input 

modality for smartphones only reinforces this premise. In the EVA environment, where neither the mouse 

nor the touch screen are practical, the gloves on a crewmember’s hands can fill this role. 

The GECO concept was initially explored in an investigation of data entry performance employing a 

soccer goalie glove (as a crude unpressurized EVA glove surrogate) with sensory and tactile feedback 

components (Ref. 11). In a text entry task performed by twenty participants, users entered characters 

significantly faster with tactile feedback than without. Both objective (character entry and error rates) and 

subjective (Likert-scale questionnaire) measures showed a statistically-significant relationship between 

tactile feedback and performance. The success of this initial prototyping and experimental activity led to 

development of a more flight-like space suit glove and experimentation under pressurized conditions, 

described herein. 

2.0 Design 

The GECO system employs a bladder-restraint pressure glove assembly based on a design by Flagsuit 

LLC (Southwest Harbor, ME) that was awarded first prize in two NASA Astronaut Glove Centennial 

Challenges (in 2007 and 2009). Finger mobility is provided via a patented fabric joint, providing lowtorque flexion/extension. Tactile actuators (C-10 Linear Resonant Actuator, from Precision Microdrives 

Inc.), custom integration features (Ref. 12), and wiring harnesses are all mounted on the outside of the 

restraint layer (dorsal side) above each finger to enable rendering of vibratory cues. The design, shown in 

Figure 1, takes advantage of the stiffness of the pressurized glove to carry vibrations from these actuators 

to the user’s finger tips, thus permitting placement of the components in a manner that does not interfere 

with the glove’s normal use for manual manipulation. Typical vibrotactile feedback for a “button click” 

event takes the form of two 175 Hz square wave pulses of 25 ms duration and a 25 ms inter-pulse delay. 

Figure 1.—GECO pressure glove with vibrotactile actuators. 
NASA/TM—2013-216546 4 

Figure 2.—Fully-integrated GECO glove. 

All other GECO components are integrated into the surrogate TMG outer garment. These include a 

small custom printed circuit board (PCB) encompassing a microprocessor, signal conditioning, tactor 

driver, and power electronics. The microprocessor (STM32F103V8 from STMicroelectronics, Geneva, 

Switzerland) includes a central processing unit, random access memory, non-volatile memory, digital 

input/output, built-in timers, integrated analog-to-digital (A/D) conversion, serial communications, interintegrated circuit communications, and a Joint Test Action Group port for programming/debugging. 

During normal operations, all external data and power connections are via a single Universal Serial Bus 

(USB) cable to a personal computer. The fully-integrated glove is shown in Figure 2. The PCB, 

instrumentation, and harnessing are all integrated such that they do not interfere with, and are in fact are 

not even noticeable during, manual glove usage. 

Finger flexion sensing is provided by 4.5 in. bend sensors (resistive ink patterned on a polyimide 

film) held within the surrogate TMG layer, aligned with each finger, using flexible tubing. Each sensor is 

connected to an operational amplifier voltage divider/filter circuit on the PCB through the GECO harness, 

providing the microprocessor, via its A/D channels, with a signal proportional to finger flexion angle. The 

microprocessor recognizes finger tapping motion by: (1) estimating individual finger speed as the change 

in finger position over a predetermined clock interval, (2) detecting speeds that exceed a predetermined 

positive speed threshold (rising edge), and (3) subsequently detecting a speed less than a predetermined 

negative speed threshold (falling edge), corresponding to an arresting motion. During the interval between 

detection of the rising and falling edge of an individual finger, the gesture recognition process is suspended for all other fingers on the associated hand. This temporary suspension prevents natural finger 

motion synergies (e.g., between the ring and little fingers) from generating erroneous events. The results 

of this algorithmic approach, applied to the instrumented GECO gloves pressurized at 4.3 pound per 

square inch differential (psid), are highlighted in Figure 3. The range of finger positions represent short 

tapping movements with each digit, progressing from thumb to little finger. Recognized finger “clicks” 

are shown as colored vertical lines; while finger position (bend sensor flexing) is shown as a series of 

asterisks representing normalized ADC counts. These data suggests a high rate of accuracy using this 

method, a hypothesis that was validated in the human subject testing. 

Light emitting diodes (LEDs) on the back of the surrogate TMG enable tracking of hand motion by a 

suit-mounted camera, with a control circuit on the PCB providing on/off and/or continuous dimming. As 

a surrogate for suit-integrated optics, we employed an infrared camera (OptiTrack V120:SLIM from 

NaturalPoint, Inc.) which provides MPEG-compressed 640480 pixel grayscale frames at 120 Hz through 

USB 2.0. Custom tracking software calculates hand position from the centroid locations of each glove’s 

LEDs in the image frames. 
NASA/TM—2013-216546 5 

Figure 3.—GECO finger sensor data (asterisks 

indicate a registered button event). 

Figure 4.—GECO system in KEYBOARD mode. 

A platform-agnostic Java-based GECO application programming interface (API) provides access to 

glove-generated events, sensor data, tactile event generation, and system control settings. The API has 

been used to implement automated test software (described below) and two different data entry modes: 

MOUSE and KEYBOARD. In MOUSE mode, the gloves emulate the functionality of a standard 

computer mouse, generating operating system events that are indistinguishable from the desktop 

analogue. Motion tracking permits hand position to drive the movement of a cursor on a visual display, 

while finger tapping motions are recognized and translated into mouse click events. Tactile feedback 

provides the physical sensation of button clicks, the edges of windows, and other GUI interactions. 

In KEYBOARD mode, GECO emulates the functionality of a standard QWERTY keyboard. Hand 

motion controls the mapping from the fingers on right and left hands to keys on a virtual keyboard. When 

KEYBOARD mode is initialized, the fingers on the left hand map (from little to index finger) to “a,” “s,” 

“d,” “f” and the fingers on the right hand to “j,” “k,” “l,” “;”. The thumb on both hands is always mapped 

to the space bar. Translation of hand position changes the mapping to mimic the change of finger position 

on a physical keyboard. A forward motion with the left hand would thus change the mapping so that the 

index finger rests on the “r” key. Finger tapping motions with any finger generates a keyboard stroke of 

the appropriate key. In this mode, tactile feedback to individual fingers provides the physical sensation of 

button clicks while a lighter, single-pulse “bump” effect indicates transition between finger-to-keyboard 

positions. Figure 4 illustrates use of the GECO system to generate the text “Hello!” in KEYBARD mode. 
NASA/TM—2013-216546 6 

3.0 Human Participant Testing 

Human participant testing was conducted at Barron Associates, Inc. in Charlottesville, Virginia between 

August and October 2012 to evaluate the effectiveness of the GECO gloves as a human-computer interface. 

The research plan was approved and supervised by the NASA Institutional Review Board (IRB). 

3.1 Participants 

Twenty volunteers participated in the experiment. Volunteers were over the age of 21 and without 

physical impairment that would prevent normal use of the employed arm, hand, or fingers. To ensure 

adequate fit for the single prototype GECO glove, participants were screened to have palm width between 

82 and 106 mm and hand length (from base of palm heel to tip of middle finger) between 197 and 226 mm. 

3.2 Hypotheses 

Hypotheses to be investigated included: 

Hypothesis 1—Participants make fewer errors with than without tactile feedback when using their gloved 

hand to perform simulated button clicks. 

This hypothesis is tested through the Simon Says Game described below. The ability to accurately 

generate events with all five fingers of the GECO glove is essential for use as a data entry device. If users 

are able to effectively employ their gloved fingers to activate virtual buttons, any number of humancomputer interface modalities may be enabled. These range from simple switches or buttons, to more 

complex, and powerful, chorded keyboards. 

Hypothesis 2—Participants are able to enter more characters with a lower error rate using their gloved 

hand with tactile feedback than without.

This hypothesis is tested in both the Hunt-and-peck and Touch-typing data entry modalities described 

below. Text entry is representative of a wide range of potential EVA applications, from text communications to geological sample cataloging. As the text entry activity requires translational hand movement and 

coordinated finger motion, it is also representative of other GUI interactions, such as icon clicking or 

widget controls. 

Additionally, a range of secondary effects, such as inter-subject variability, training effects, and hand 

synergies were investigated through the activities. 

3.3 Materials and Software 

To approximate the impact of suit pressurization on manual dexterity, a test system was fabricated to 

permit operation at the same pressure differential (between the pressure volume inside and outside the 

bladder) experienced in the current NASA space suit “Phase VI” gloves employed on the International 

Space Station (Ref. 4). The setup includes a custom glove box (produced by Flagsuit LLC) consisting of a 

pressure-sealed container (hexagonal cylinder, 30 in. face-to-face, 32 in. long) made of steel and acrylic 

sheet material; four access ports, into which space suit upper arm and glove assemblies can be inserted 

(Figure 5); and a diaphragm vacuum pump. Under normal operations, the inside of the glove box is 

evacuated to 4.3 psi below ambient atmospheric pressure, while the inside of the glove remains open to 

room ambient conditions, creating a 4.3 psid across the glove. When the glove is donned by a human 

subject, the human hand remains at room-level ambient pressure, but the glove feels and operates as it 

would on pressurized suit in the vacuum of space. A single right-handed GECO prototype glove was used 

for all evaluations. 
NASA/TM—2013-216546 7 

Figure 5.—GECO test setup. 

 

Figure 6.—Simon says game. 

To facilitate testing, Barron Associates developed a suite of software applications, including games 

and automated data entry tasks, which challenge a user’s ability to employ the GECO gloves as a data 

entry device. These include (1) a Simon Says Game, inspired by the electronic toy game, in which users 

attempt to follow cues for randomly-generated sequences of finger taps; and (2) Virtual Keyboard Data 

Entry, in which users employ the GECO glove in both MOUSE and KEYBOARD modes to enter 

randomly-generated word sequences using an on-screen virtual keyboard. 

The Simon Says Game was constructed as a simple, and familiar, activity to assess a user’s ability to 

correctly reproduce randomly-generated finger strike sequences (Figure 6). The computer plays the role 

of “Simon,” starting with a single target finger, and then progressively adding randomly-generated target 

fingers with each subsequent sequence. The target sequence is first presented to the user through visual, 

audio (unique tone), and finger-specific tactile feedback. The Simon Game was configured such that it 

can be played in two alternate conditions: non-tactile and tactile. In the tactile condition, the user receives 

localized (finger specific) tactile feedback indicating a finger strike has been registered. No visual or 
NASA/TM—2013-216546 8 

 

Figure 7.—Text entry activity. 

audio cues are provided for the user’s finger movement. If a mistake is made (i.e., registered finger does 

not match the target), the sequence is terminated and the game moves on to the subsequent sequence. In 

every case, the game proceeds until five sequences have been completed, resulting in up to fifteen total 

finger strikes per game. 

The Text Entry activity was designed around a Java-based virtual keyboard application that automates 

human subject data collection and generation of text entry performance measures (Figure 7). The virtual 

keyboard can be employed in a “hunt-and-peck” or “virtual touch typing” condition by placing the GECO 

glove in MOUSE or KEYBOARD mode, respectively. The virtual keyboard can also be employed with 

or without vibrotactile feedback, by activating or deactivating the glove actuators. During test execution, 

the software presents a target phrase randomly drawn from an internal database. The user employs the 

virtual keyboard to transcribe the phrase as quickly and accurately as possible. When entered, a new 

phrase appears, the cycle continuing for the duration of the test. On completion, software generates the 

three performance metrics: CPM (the average number of correct characters entered per minute), KSC 

(average number of keystrokes per correct character entered), and MSD (average mean string distance 

between the targeted and user-entered phrases) using published string processing algorithms (Ref. 13). 

3.4 Procedures and Statistical Design 

On arrival, volunteers received a pre-test briefing and overview of the NASA IRB-approved consent 

form. Test procedures were initiated upon provision of consent. The participant’s right hand was traced 

employing a standard template and size verified to be within the pre-established ranges. Participants 

completed the Simon Game activity in an initial visit and performed the Text Entry activities in a second 

visit. 

3.4.1 Simon Game 

Participants initially played four Simon Games for practice, half with and half without tactile 

feedback. They then played 20 games for record, again alternating between tactile conditions. The within 

subjects protocol design, evaluating each subject under both conditions, was motivated by anticipated 

high inter-subject variability in performance. The alternating presentation of conditions between games 

was introduced in anticipation of a significant training effect (Ref. 11). Test software automatically 

recorded the cued and user-entered finger strikes into a comma-separated-value file for subsequent 

statistical analysis. 

The primary objective of Simon Game data analysis was to determine whether participants’ accuracy 

in generating finger strikes was enhanced by tactile feedback. A secondary objective was to assess the 

expected overall accuracy participants may achieve in generating finger strikes. Although each game was 

played to a length of five sequential finger strikes, only the first three sequences in any game were 
NASA/TM—2013-216546 9 

included in the primary analysis to reduce the potential impact of memory-related errors on the outcome. 

This decision was based on the experience of previous research into working memory and sequence 

learning ability (Ref. 14). Additional objectives included assessing the association between accuracy and 

the targeted finger (thumb through little finger). Another was to examine whether there was an association 

between accuracy and the length of the targeted sequence (working memory/sequence learning effect). 

The final secondary objective was to examine if there was an association between accuracy and the 

number of games played (learning, or alternatively, fatigue effects). 

The Simon Game outcome data were analyzed by way of a binomial generalized estimating equation 

model (GEEM) (Ref. 15), to evaluate average performance; and a binomial generalized linear mixed 

model (GLMM) (Ref. 16), to evaluate subject-specific performance. An a priori p0.05 decision rule was 

established as the null hypothesis rejection criterion. The GEEM had a single predictor variable that 

distinguished between the tactile feedback trials and the non-tactile feedback trials. Since a participant’s 

game performance from trial to trial could not be assumed to be statistically independent, the Huber 

“sandwich” estimator was utilized to estimate the variance-components of the GEEM variance-covariance 

matrix. The GEEM link function was the logit, 

  Tactile Feedback No

1 log    1  















 



ij

ij

ij I (1) 

where ij is the probability that subject i finger of response on trial j was the target finger. 

The Wald chi-square test was used to test the null hypothesis that the odds of a target finger match 

(i.e., the finger of motion matched the target finger) is the same irrespective of tactile condition. 

95% confidence intervals were constructed to estimate the percentage of trials in which the finger of 

motion matched the target finger for each experimental condition. GEEM analyses were conducted using 

the GENMOD procedure of SAS software version 9.2 (SAS Institute Inc., Cary, NC). 

In evaluation of subject-specific performance, the GLMM fixed-effect was the experimental 

condition (tactile feedback versus no tactile feedback), and GLMM random-effect was the subject 

number. GLMM parameter estimation was based on the principle of restricted maximum likelihood and 

the GLMM link function was the logit, 

  ij i

ij

ij I b 1 Tactile Feedback No 1 1 log       















 



 (2) 

where b1i is the random effect associated with subject i. 

Construction of 95% prediction intervals was based on the normal approximation method. GLMM 

analyses were conducted using the GLIMMIX procedure of SAS software version 9.2. At the end of the 

Simon Game session, subjects completed the NASA TLX to provide a subjective assessment of user 

workload (Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration) 

(Ref. 17). The primary objective of the Simon Game survey analyses was to compare participants’ 

opinions about the mental, physical, and temporal demands; as well as game performance, effort, and 

level of frustration when playing the Simon Game with and without tactile feedback. The response to 

each question was measured on a 20-point Likert-scale with higher points reflecting greater demand, 

greater performance, greater effort, and greater frustration. The integer-scale responses to each survey 

question were analyzed by way of a negative binomial generalized linear model (GLM). The GLM 

dependent variable was the integer-scale response to the question, and the independent variable identified 

whether response was in reference to the tactile feedback game trails, or in reference to the non-tactile 

feedback game trials. The GLM parameters were estimated by way of maximum likelihood and the GLM 

link function was the natural logarithm. 
NASA/TM—2013-216546 10 

For each survey question, the Wald chi-square test was used to test the null hypothesis that the mean 

response was the same regardless of whether the question was in reference to the tactile feedback game 

trials, or in reference to the non-tactile feedback game trials. A P0.05 decision rule was established a 

priori as the null hypothesis rejection criterion. 95% confidence interval construction for the mean 

response, and for the ratio of mean responses, was based on the Wald method. The software of the 

GENMOD procedure of SAS 9.2 was used to conduct the survey questionnaire analyses. 

3.4.2 Text Entry Activity 

In a within-subjects design, each participant performed the Text Entry activity for 5 min in both 

hunt-and-peck and touch-typing modes, both with and without tactile feedback (for a total of 10 min of 

recorded text entry in each feedback condition). Prior to each timed test event, participants first practiced 

in the given combination of text entry and tactile mode for 5 min. The primary objective of the data 

analysis was to determine if the participant’s typing performance was enhanced by tactile feedback. As 

secondary objectives, we examined if typing performance was dependent upon the mode of typing, and if 

typing performance was dependent upon trial order (training effect). The primary performance measure 

was the number of characters typed per minutes (CPM) over a 5-min trial. Secondary error measures 

included keystrokes per correct character entered (KPC) and mean string distance between target phrases 

and entered text (MSD). 

CPM and MSD data were analyzed by way of a negative binomial generalized estimating equation 

model, and the KPC data were analyzed by way of a Poisson generalized estimating equation model. The 

dependent variable for the CPM analysis was the total number of correct characters typed during the 

5 min typing performance trial. The dependent variable for the KPC analysis was the total number of key 

strokes (correct and incorrect) entered, and the dependent variable for the MSD analysis was the total 

string length between entered versus targeted phrases. Each GEEM had three predictor variables. One 

variable distinguished the tactile feedback typing performance trials from the non-tactile feedback typing 

performance trials. One variable distinguished the touch-typing condition from hunt-and-peck, and the 

remaining predictor variable was tactile feedback mode by typing mode interaction. Since a participant’s 

performance from trial to trial could not be assumed to be statistically independent, the Huber “sandwich” 

estimator was utilized to estimate the variance-components of the GEEM variance-covariance matrix. The 

natural logarithmic link function was used for both the negative binomial GEEM and the Poisson GEEM. 

The GEEM offset variable for the CPM analysis was the time duration of the typing performance trial 

(i.e., 5 min). The GEEM offset variable for the KPC analysis was the number of correct characters 

entered, and the GEEM offset variable for MSD was the total number of target characters. 

With regard to hypothesis testing, the GEEM version of the Wald chi-square test was used to test the 

2 primary null hypotheses: (1) that the mean of the distribution of the performance measurements for the 

touch-typing performance trials is the same regardless of whether or not the typing performance trial is 

performed with the aid of tactile feedback, and (2) that the mean of the distribution of the performance 

measurements for the hunt-and-peck performance trials is the same regardless of whether or not the 

typing performance trial is performed with the aid of tactile feedback. A p0.05 decision rule was 

established a priori as the null hypothesis rejection criterion. The following two secondary hypotheses 

were also examined: (1) the mean of the distribution of the performance measurements for the with-tactile 

feedback performance trials is the same regardless of whether or not the trials are performed via touchtyping or hunt-and-peck method, and (2) the mean of the distribution of the performance measurements 

for the without-tactile feedback typing performance trials is the same regardless of whether or not the 

trials are performed via touch-typing or hunt-and-peck method. Hypotheses directed at testing for tactile 

mode and typing mode main effects were also performed when the test for tactile mode by typing-mode 

interaction was not rejected at p0.05. A p0.05 decision rule was established a priori as the null 

hypothesis rejection criterion for the secondary hypothesis tests and for the hypotheses tests related to 

main effect. 95% confidence interval construction for estimating the ratio of mean performance was based 

the GEE version of the Wald normal approximation method. 
NASA/TM—2013-216546 11 

At the end of the Text Entry activities, subjects completed the NASA TLX instrument to provide a 

subjective assessment of user workload (Mental Demand, Physical Demand, Temporal Demand, 

Performance). The Text Entry TLX data were analyzed in a like fashion to the Simon Game TLX data. 

3.5 Results 

3.5.3 Simon Game Results 

Over all subjects and trials, with tactile feedback, correct finger strikes were made 1143 times while 

incorrect finger strikes were made 33 times. Without tactile feedback, the corresponding totals were 1133 

and 40. Mean values and confidence intervals generated using the aforementioned GEM model are shown 

in Figure 8. The finger of motion matched the target finger 97.2% of the time (95% CI: [95.9, 98.1%]) 

with tactile feedback, and 96.6% of the time (95% CI: [95.1, 97.6%]) without (p = 0.466). 

The subject-specific predictions for the mean percentage of target finger matches, generated using the 

GLMM model, are shown in Figure 9. Results are ordered by increasing mean. Subject-specific 

predictions range from 90.1 to 99.3% for the tactile feedback trials and from 88.2 to 99.1% for the nontactile trials. 

Tactile Feedback

Estimated % of Target Finger Matches

Yes No

94

95

96

97

98

99

100

P = 0.466

97.2 96.6

Figure 8.—Target finger matches versus tactile condition. 

Subject

Predicted % of Target Finger Matches

80

82

84

86

88

90

92

94

96

98

100

Non-Tactile Feedback

Tactile Feedback

Figure 9.—Subject specific predictions. 
NASA/TM—2013-216546 12 

The concordance between the target finger and the finger of motion is summarized in Table 1. The 

percentage of mismatches associated with a given target finger are shown in parentheses. For all trials 

(i.e., irrespective of tactile feedback status), 61.9% (95% CI: [55.6, 68.0%]) of finger mismatches 

involved an adjacent finger. For the tactile feedback trials, 66.4% (95% CI: [56.9, 75.0]) of finger 

mismatches involved an adjacent finger, and for the non-tactile feedback trials 58.2% (95% CI: [49.4, 

66.7]) of finger mismatches involved an adjacent finger. 

For the tactile feedback Simon Game trials, when the target finger was either the thumb, index finger, 

or middle finger, the percentage of target finger matches was 97.3% (95% CI: [96.2, 98.1%]), whereas, 

when the target finger was either the ring finger or little finger, the percentage of target finger matches 

was 94.1% (95% CI: [92.0, 95.7%]) (p0.001) (Figure 10). For the non-tactile feedback Simon Game 

trials, the corresponding results are 96.8 (95% CI: [95.7, 97.7]) for thumb-index-middle and 92.9% 

(95% CI: [90.7, 94.6%]) (p0.001) for ring-little finger. The impact of the finger composition on the 

percentage of target finger matches was unrelated to tactile condition. 

TABLE 1.—CONCORDANCE—TARGET AND FINGER OF MOTION 

Fingers

Estimated % of Target Finger Matches

thumb-index-middle ring-little

90

92

94

96

98

100

P<0.001 P<0.001

Tactile Feedback Non-Tactile Feedback

97.3 94.1 96.8 92.9

thumb-index-middle ring-little

Figure 10.—Impact of target finger. 
NASA/TM—2013-216546 13 Target Finger Matches (%)

Tactile Feedback Yes No Yes No Yes No Yes No Yes No

Sequence 1 234 5

80

85

90

95

100

Figure 11.—Match frequencies versus target sequence length. 

Sequence

Estimated % of Target Finger Matches

1-3 4-5 1-3 4-5

92

93

94

95

96

97

98

99

100 Tactile Feedback Non-Tactile Feedback

P = 0.003 P = 0.002

97.2 95.2 96.6 94.3

Figure 12.—Impact of target sequence length. 

Frequencies for the number of target finger matches and mismatches as a function of game sequence 

number and tactile condition are summarized in Figure 11. 

Predictions for the impact of sequence length on predicted performance based on an extended GEEM 

model are shown in Figure 12. For the tactile condition, when the target sequence had a length of 3 or 

fewer finger strikes, the percentage of target finger matches was 97.2% (95% CI: [95.9, 98.1%]), whereas, 

when the sequence had a length of 4 or 5, the percentage of target finger matches was 95.2% (95% CI: 

[93.9, 96.2%]) (p = 0.003). For the Simon Game in the non-tactile condition, when the sequence had a 

length of 3 or fewer, the percentage of target finger matches was 96.6% (95% CI: [95.9, 98.1%]), 

whereas, when the sequence had a length of 4 or 5, the percentage of target finger matches was 94.3% 

(95% CI: [92.9, 95.5%]) (p = 0.002). The impact of the sequence length on the percentage of target finger 

matches was unrelated to tactile condition (p = 0.907). 

As described above, each subject played 20 Simon games in total, ten in each condition, alternating 

between conditions with each game. The relationship between the percentage of target finger matches and 

the sequential game number (i.e., game 1 was played first, game 20 last) is summarized in tabular form in 

Figure 13. No significant association (represented as the slope regression parameter in a generalized 

estimating model) was found between the odds of target match and the game number (p = 0.883, and 

p = 0.204, respectively) in either the tactile feedback or non-tactile feedback trials. 
NASA/TM—2013-216546 14 

Game

Target Finger Match (%)

1-4 5-8 9-12 13-16 17-20

Tactile Feedback

Non-Tactile Feedback

80

85

90

95

100

Figure 13.—Match frequencies versus game number. 

Tactile Feedback

Mean Survey Question Score

No Yes No Yes No Yes No Yes No Yes No Yes

0

2

4

6

8

10

12

14

16

18

20

Mental 

Demand Physical 

Demand Temporal 

Demand Performance Effort Frustration

 P = 0.207 P = 0.580 P = 0.737 P = 0.068 P = 0.081 P = 0.068

8.3 6.5 8.8 8.0 4.6 4.2 12.9 15.3 10.9 8.6 7.1 4.9

Figure 14.—NASA TLX analysis summary. 

The distributions of Likert-scale questionnaire responses from the NASA TLX survey are 

summarized in Figure 14. The mean scores for “mental demand” were 6.5 (95% CI: [4.9, 8.6]) 

and 8.3 (95% CI: [6.3, 10.9]), for the tactile feedback and the non-tactile feedback surveys questions, 

respectively (p = 0.207). The mean scores for “physical demand” were 8.0 (95% CI: [6.2, 10.3]) and 8.8 

(95% CI: [6.8, 11.3]), (p = 0.580), and the mean scores for “temporal demand” were 4.2 (95% CI: [2.9, 

6.1]) and 4.6 (95% CI [3.2, 6.6]), (p = 0.737). 
NASA/TM—2013-216546 15 

The mean scores for “performance” were 15.3 (95% CI [13.5, 17.4]) and 12.9 (95% CI [11.3, 14.7]), 

respectively, for the tactile feedback and the non-tactile feedback survey questions (p = 0.068). The mean 

scores for “effort” were 8.6 (95% CI [7.1, 10.4]) and 10.9 (95% CI [9.1, 13.0]), (p = 0.081); and the mean 

scores for “frustration” were 4.9 (95% CI [3.7, 6.5]) and 7.1 (95% CI [5.4, 9.2]), (p = 0.068). 

The mean for the composite of the objective task demand-related (Ref. 17) responses (the sum of the 

“mental”, “physical”, and “temporal” demand scores) for the tactile feedback and the non-tactile feedback 

survey questions were 17.8 (95% CI: [4.3, 22.1]) and 21.7 (95% CI: [17.7, 26.7]), respectively (p = 

0.192). The mean for the composite of the subjective behavioral-related responses (Ref. 17) of “effort” 

and “frustration” were 13.5 (95% CI: [11.2, 16.3]) with tactile feedback and 17.9 (95% CI: [14.9, 21.3]), 

without, (p = 0.035). 

3.5.4 Text Entry Activity Results 

The empirical distributions of the CPM measurements across all subjects are shown in Figure 15. 

For the virtual touch typing trials, the mean of the CPM measurement distribution was 28.8 (95% CI: 

[26.0, 32.0]) with tactile feedback, and 25.4 (95% CI: [22.5, 28.6]) without tactile feedback (p = 0.005) 

(Figure 16(a)). For the hunt-and-peck typing trials, the mean of the CPM measurement distribution was 

37.5 (95% CI: [35.7, 39.4]) with tactile feedback, and 35.9 (95% CI: [34.2, 37.7]) without tactile feedback 

(p = 0.065). Across both data entry modes, the mean of the CPM distribution was 8% (95% CI: [3, 13%]) 

greater with tactile feedback than without (p = 0.001) (Figure 17). 

For performance trials performed with tactile feedback, the mean of the CPM distribution was 

30% (95% CI: [19, 41%]) greater in hunt-and-peck mode versus touch-typing (p0.001) (Figure 16(b)). 

Without tactile feedback, the mean of the CPM distribution was 41% (95% CI: [29, 56%]) greater in 

hunt-and-peck mode versus touch-typing (p0.001). Across tactile feedback conditions, the mean of the 

CPM distribution was 35% (95% CI: [25, 46%]) greater in hunt-and-peck mode versus touch-typing 

(p0.001) (Figure 17). 

Figure 15.—Characters per minute (CPM) data summary. 
NASA/TM—2013-216546 16 

 

(a) Effect of Tactile Feedback (b) Effect of Typing Mode

Figure 16.—Characters per minute (CPM) analysis. 

Figure 17.—Characters per minute (CPM) marginal (average) effect summary. 
NASA/TM—2013-216546 17 

The empirical distributions of the KPC measurements across all subjects are shown in Figure 18. For 

the touch-typing performance trials (Figure 19(a)), the mean of the KPC measurement distribution was 

1.07 (95% CI: [1.04, 1.10]) with tactile feedback and 1.10 (95% CI: [1.07, 1.14]) without (p = 0.054). For 

the hunt-and-peck trials, the mean of the KPC measurement distribution was 1.02 (95% CI: [1.01, 1.02]) 

with tactile feedback and 1.02 (95% CI: [1.01, 1.03]) without (p = 0.319). Across data entry modes, the 

mean of the KPC distribution was 2% (95% CI: [0.0, 3%]) less for with tactile than without (p = 0.023) 

(Figure 20). 

For performance trials with tactile feedback, the mean of the KPC distribution was 5% (95% CI: 

[2, 7%]) less when the hunt-and-peck typing method was used compared to touch-typing (p0.001) 

(Figure 19(b)). For performance trials without tactile feedback, the mean of the KPC distribution was 7% 

(95% CI: [5, 10%]) less in hunt-and-peck mode versus touch-typing (p = 0.001). Across both tactile 

modes, the mean of the KPC distribution was 6% (95% CI: [4, 8%]) less hunt-and-peck compared to 

touch-typing (p0.001) (Figure 20). 

Figure 18.—Keystrokes per character (KPC) data summary. 

 

(a) Effect of Tactile Feedback (b) Effect of Typing Mode 

Figure 19.—Keystrokes per character (KPC) analysis. 
NASA/TM—2013-216546 18 

Figure 20.—Keystrokes per character (KPC) marginal (average) 

effect summary. 

Figure 21.—Mean string distance (MSD) data summary. 

The empirical distributions of the MSD measurements across all subjects are shown in Figure 21. 

For the touch-typing performance trials, the mean of the MSD measurement distribution was 0.026 

(95% CI: [0.013, 0.051]) with tactile feedback compared to 0.026 (95% CI: [0.013, 0.054]) without 

(p = 0.992) (Figure 22(a)). For the hunt-and-peck trials, the mean of the MSD measurement distribution 

was 0.012 (95% CI: [0.006, 0.027]) with tactile feedback compared to 0.023 (95% CI: [0.010, 0.056]) 

without (p = 0.002). As there was significant tactile condition by typing condition interaction (p = 0.038), it 

is inappropriate to estimate the marginal (average) effect of tactile feedback across data entry conditions. 

For performance trials with tactile feedback, the mean of the MSD distribution was 53% (95% CI: 

[3, 77%]) less for hunt-and-peck versus touch-typing (p = 0.042) (Figure 22(b)). Without tactile feedback, 

the mean of the MSD distribution was 12% (95% CI: [–86, 58%]) less for hunt-and-peck versus touchtyping (p = 0.737). As there was significant tactile condition by typing condition interaction (p = 0.038), it 

is inappropriate to estimate the marginal (average) effect of data entry mode across tactile condition. The 

GEE regression analyses show that trial order (order in which tactile feedback was not associated with the 

primary performance measure CPM (p = 0.528), nor was “trial order” associated with KPC (p = 0.307), or 

MSD (p = 0.177). 
NASA/TM—2013-216546 19 

 

(a) Effect of Tactile Feedback (b) Effect of Typing Mode

Figure 22.—Mean string distance (MSD) analysis. 

The confidence intervals and means values of the NASA TLX categories for the hand-and-peck trials 

are summarized in Figure 23. The mean scores for “mental demand” were 6.3 (95% CI: [4.8, 8.4]) with 

tactile feedback versus 6.7 (95% CI: [5.3, 8.6]) without (p = 0.150). The mean scores for “physical 

demand” were 13.5 (95% CI: [12.0, 15.2]) and 13.7 (95% CI: [12.3, 15.2]), respectively (p = 0.403). The 

mean scores for “temporal demand” were 7.8 (95% CI: [6.1, 10.1]) with tactile feedback and 7.8 (95% CI: 

[6.0, 10.0]) without (p = 0.564). The mean scores for “performance” were 14.6 (95% CI: [13.2, 16.2]) and 

14.6 (95% CI: [13.3, 16.1]), respectively, with versus without tactile feedback (p = 1.000). The mean 

scores for “effort” were 11.1 (95% CI: [9.5, 12.9]) with and 11.8 (95% CI: [10.3, 13.5]) without tactile 

feedback (p = 0.025). The mean scores for “frustration” were 5.6 (95% CI: [4.1, 7.6]) and 5.4 (95% CI: 

[4.3, 6.8]), respectively, with versus without tactile feedback (p = 0.780). 

The confidence intervals and means values of the NASA TLX categories for the touch-typing trials 

are summarized in Figure 24. The mean scores for “mental demand” were 9.2 (95% CI: [6.9, 12.2]) with 

tactile feedback and 9.8 (95% CI: [7.4, 12.9]) without (p = 0.001). The mean scores for “physical 

demand” were 13.1 (95% CI: [11.3, 15.1]) and 13.0 (95% CI: [11.2, 15.1]), respectively, with and without 

tactile feedback (p = 0.561). The mean scores for “temporal demand” were 8.5 (95% CI: [6.6, 10.9]) with 

and 8.7 (95% CI: [6.8, 11.2]) without tactile feedback (p = 0.176). The mean scores for “performance” 

were 12.3 (95% CI: [10.4, 14.7]) with tactile feedback and 11.8 (95% CI: [10.0, 14.0]) without 

(p = 0.125). The mean scores for “effort” were 13.1 (95% CI: [11.4, 15.0]) with and 14.6 (95% CI: [13.3, 

16.0]) without tactile feedback (p0.001). The mean scores for “frustration” were 9.2 (95% CI: [7.0, 

12.1]) and 10.7 (95% CI: [8.5, 13.4]), respectively, with and without tactile feedback (p = 0.015). 
NASA/TM—2013-216546 20 

Figure 23.—Hunt-and-peck TLX summary. 

Figure 24.—Touch-typing TLX summary. 
NASA/TM—2013-216546 21 

4.0 Discussion 

Despite the stiffness and bulk of the pressurized gloves, users were able to employ the GECO system 

to accurately generate desired finger strike sequences, both with and without tactile feedback. In both 

cases the lower bound on a 95% CI (over all subjects and trials) exceeds 95% accuracy. Subjects, with 

only limited practice, were remarkably effective. Individually, none of the 20 participants achieved a 

predicted mean below 88% accuracy. Five of the subjects achieved a predicted mean accuracy of greater 

than 99% for both tactile and non-tactile conditions. It may be reasonably argued that highly skilled 

astronauts might fall into this highest-performing group. 

Over the ensemble of subjects, and for every one of the 20 individual participants, mean performance 

improved with tactile feedback; however the effect cannot be considered statistically significant. The 

unexpectedly high level of performance across both conditions likely resulted in a ceiling effect that 

reduced the power of the test to detect differences due to tactile condition. 

Previous research has found that due to natural hand synergies, normal subjects attempting to move a 

single finger will produce motion in others (Ref. 18). Expected error rates were therefore expected to vary 

as a function of the finger targeted. Additionally, the bulk of the surrogate TMG layer causes some degree 

of binding between fingers that could contribute to a finger-dependency in error rate. Indeed, 61.9% (95% 

CI: [55.6, 68.0%]) of errors involved an erroneous strike by a finger adjacent to the targeted digit. 

Figure 10 shows that there was a significantly greater chance of errors targeting either the ring or little 

finger versus the thumb, index, or middle. Nevertheless, the predicted mean accuracy in these cases was 

94.1% (95% CI: [92.0, 95.7%]) for the tactile case, and 92.9% (95% CI: [90.7, 94.6%]) (p0.001) in the 

non-tactile condition. Considering that some subjects achieved even higher accuracy rates, the ring and 

little fingers can be considered useful for the GECO human-computer interface. 

In investigating the impact of tactile feedback on participants’ accuracy in generating finger strikes, 

only the first three sequences in any game were included in the primary analysis. This decision was 

informed in part by a previous study (Ref. 19), in which 24 elderly hearing-impaired and 12 young 

normal-hearing individuals performed a Simon game-like activity. Both groups’ success rate approached 

100% for sequences of length three or less, after which an accuracy drop-off occurred related to working 

memory and sequence learning ability. To explore this effect, secondary analyses in the GECO study 

included the impact of sequence length. Figure 12 shows that for both the tactile and non-tactile 

conditions, the mean accuracy of finger strikes for target sequences of 3 or fewer was greater than for 

target sequences of 4 or 5. In both cases the effect size was small, approximately 2%, but significant, 

confirming the original conjecture on interference due to memory/learning effects beyond the third 

sequence. A future study is under consideration to specifically explore the potential benefit of tactile 

feedback for memory/learning. 

Shoulder and arm fatigue was noted by some participants in the Phase I study (Ref. 11), motivating 

inquiry into potential degradation in user performance over the course of the 20 Simon games played. The 

same Phase I study detected a significant training effect that resulted in increased performance in a textual 

data entry task over time. Regression analysis of the Simon Game data revealed no significant 

relationship between game number and accuracy, indicating either (1) both the absence of a significant 

training effect (beyond the 8 practice games) as well as absence of a significant fatigue effect on 

performance over the games recorded, or (2) that the two effects were present but cancelled each other 

out. The lack of any observable trend in the summary data in Figure 13 would imply the former. It is 

likely that onset of significant fatigue would appear some number of games beyond the 28 total played in 

the GECO protocol. A future study may be conducted to assess how long users could use the gloves prior 

to fatigue onset. 

The mean responses to the subjective Simon Game Likert-scale NASA TLX questionnaire all trended 

favorably for the tactile case. Taking the six questionnaire response items individually, no claim of 

significance can be made. Categorizing responses into objective task demand-related and subjective 

behavioral-related items (Ref. 17) permits a composite view of potential impacts on workload. The 

composite sum of the three objective task demand items does not show a significant effect due to tactile 
NASA/TM—2013-216546 22 

condition. In contrast, the composite of the two negative subjective behavioral items (effort and 

frustration) reveals a significant benefit for tactile feedback. This finding might indicate that affect 

(feelings or emotional responses) rather than effect (objective performance) is the most important 

contribution of tactile feedback in this application. 

Written narrative comments included statements that tactile feedback provided a more “natural,” 

“intuitive,” and or “pleasurable” interface; and made the task “easier.” A number of participants made 

remarks similar to these samples: “no feedback made me want to push harder to ensure registration,” “it 

was much easier to know when a button was pushed with the tactile feedback,” and “vibration helped 

verify contact.” One respondent took a contrary view, “was almost oblivious to the tactile feedback - 

didn't notice it there or not.” Although numerous participants noted that the gloves were “too stiff” (an 

unavoidable consequence of pressurization), there were surprisingly no complaints about hand fatigue in 

the Simon Game. 

The estimated effect size for tactile feedback (8% improvement) in relation to text entry performance 

was consistent with previous investigations involving smartphones (Ref. 20) and unpressurized gloves 

(Ref. 11). In the GECO evaluation, tactile feedback provided a statistically significant benefit for data 

entry rate (CPM) as well as error rate (KPC) across data entry modes. A reduction in MSD error rate in 

the touch-type mode with active tactile feedback also suggests that unintended key strokes may go 

undetected without the cues. In the subjective workload measures assessed in the TLX Likert instrument, 

tactile feedback showed a benefit for “effort” level in each data entry modality. In touch-typing, tactile 

feedback also resulted in lower “mental demand” and “frustration.” It is interesting to note that while 

participants performed significantly better with tactile feedback in the objective measures, TLX results 

indicate they didn’t overtly perceive this improved performance. This might be evidence of unconscious 

somatosensory processing of tactile information (Ref. 21) in the higher mental workload text entry 

activity. 

The hunt-and-peck mode resulted in clearly superior text entry performance compared to the touchtype approach, (35% greater) and was associated with fewer errors. The similarity of the task to texting on 

a smart phone may have aided users’ rapid adaptation and success. The touch-type mode required 

familiarity with conventional touch typing hand movements, and was generally less intuitive for the 

untrained user. Limitations on ease, range, and linearity of motion imposed by the gloves and arm 

sections within the pressurized glove box appeared to have a greater impact on the touch-type mode 

which, in this implementation, required greater translational movement than hunt-and-peck. 

Multiple participants cited the convenience of having the thumb “hard-wired” to the space bar in 

touch-type mode, and expressed interest in having the same in hunt-and-peck. This has motivated the 

creation of a new “hybrid” MOUSE mode which implements this concept. The hybrid mode also hardwires the middle, ring, and little fingers to the backspace key; in recognition of the synergies observed in 

Figure 10. Based on the Simon Game results, we can expect accuracies of between 96.2 and 98.1% 

(95% confidence) for first-time untrained users in this mode. Text completion keys have also been added 

to the virtual keyboard to accelerate entry rates. 

The emphasis on textual data entry in this study is not meant to imply that it is the only, or even 

primary, use case for the GECO system. Texting capability does directly enable new forms of EVA 

communications, for example text “chat” communications. In the consumer market, despite the pervasive 

availability of voice, texting has proven to be an enduring , and arguable pre-eminent, form of interperson tele-discourse. The time-delays imposed by deep-space missions, limited bandwidth, and cluttered 

audio channels make texting perhaps even more relevant for EVA. Text entry is also relevant for 

scientific note taking, cataloguing, and annotation of samples/images. Beyond text, GECO’s ability to 

emulate the ubiquitous mouse, in the most literal sense, makes its utility equally as broad. Mouse 

emulation enables manipulation of buttons, sliders, drop-down menus, toolbars, and/or any other 

conceivable GUI widget. In the most general sense, the ten fingers of the GECO gloves represent ten 

buttons that can be mapped to any function. The GECO system could thus replace cumbersome on-suit 

elements, many of which date to the Apollo-era, as the primary controls for environmental systems 

(retaining the physical controls as backups). Likewise, GECO could replace difficult-to-access keypads or 
NASA/TM—2013-216546 23 

switches on EVA crew exploration systems. Astronauts donning suits through the suit ports in the Space 

Exploration Vehicle (SEV) could immediately gain access, through the GECO gloves, to any required 

information and control systems. 

On-going efforts include pressurized testing of right- and left-hand GECO gloves at NASA Johnson 

Space Center (JSC) on the Mark III Advanced EVA Space Suit Technology Demonstrator, scheduled for 

late-March 2013. Suited testing will support improved assessment of arm mobility impacts on users’ 

ability to employ the GECO gloves for data entry. Evaluations at JSC will also include use of EVA tools 

in canonical tasks to characterize the performance of GECO gloves for manual manipulation activities. 

Future plans may include operational and analog environment testing in “Rockyard” and/or Desert 

Research and Technology Studies evaluations (Ref. 22). 

Eventual incorporation of GECO technology into a future suit is subject to future decisions on the 

direction of EVA technology. The team will continue to investigate improvements to software, hardware, 

data entry modalities, and suit integration concepts. The final configuration of an operational GECO 

system will be determined by broader decisions involving elements such as suit-integrated graphical 

display, EVA computing and communications architectures, as well as operational concepts of 

employment. These may drive alternative realizations of GECO capabilities such as hand motion 

tracking. IR LED tracking may, for example, be replaced by a visible camera-based vision system, suitintegrated joint position sensing, magnetic marker tracking, structured light sensing, laser direction and 

range finding, and/or intertial sensing. The fundamental concept of employing hand motion tracking, 

finger movement sensing, and tactile feedback persists in these alternate realizations. 

5.0 Conclusions 

Simon Game and Text Entry experiments of GECO system employment in a glove box at 4.3 psid 

demonstrate that users can effectively use an instrumented glove as a human-computer interface under an 

EVA-like pressure differentials. With very little training, volunteers were able to employ a GECO glove, 

achieving high accuracy in generation of volitional finger strikes. Participants were also able to use the 

glove to enter text through a virtual keyboard in modes emulating both a conventional mouse and a 

QWERTY keyboard. Investigation of the impact of vibrotactile feedback showed an improvement for 

both text entry rate and reduction in errors. Subjective responses to a NASA TLX Likert-scale instrument 

also revealed a perceived reduction in workload with tactile feedback. The results strongly support the 

viability of GECO as a potential solution to the yet-unsolved challenge of providing access to suitintegrated computing, communications, and control systems during EVA. Follow-on suited testing will 

investigate the benefits and challenges of this new technology for human-computer interface. 

References 

1. Boucher, M.F., Hodgson, E., Murray, S.K., Lee, P., and Braham, S., “Investigation of EVA 

Information Interface Technology in a Mars Analog Arctic Field Science Setting,” 32nd International 

Conference on Environmental Systems (ICES), Vancouver, B.C., 2002. 

2. Hodgson et al., “Requirements and Potential for Enhanced EVA Information Interfaces,” 33rd 

International Conference on Environmental Systems (ICES), Vancouver, B.C., 2003. 

3. Hurlbert, K., Bagdigian, B., Carroll, C., Jeevarajan, A., Kliss, M., and Singh, B., Space Technology 

Roadmap, Technology Area 06, Human Health, Life Support and Habitation Systems, NASA, 

Washington, D.C., April 2012. 

4. Graziosi, D., Stein, J., Ross, A., and Kosmo, J., “Phase VI Advanced EVA Glove Development and 

Certification for the International Space Station,” 31st International Conference on Environmental 

Systems (ICES), Tokyo, Japan 2001. 

5. Thompson, S., Holden, K., England, S., and Benson, E., “The Effect of Pressurized Space Gloves on 

Operability of Cursor Controls, Mobility, and Strength,” Proceedings of the Human Factors and 

Ergonomics Society 55th Annual Meeting, Las Vegas, NV, 2011. 
NASA/TM—2013-216546 24 

6. Wickens, C.D., “Multiple Resources and Mental Workload,” Human Factors, vol. 50, no. 3, 

June 2008. 

7. Stolen, M.F., Dillow, B., Jacobs, S.E., Akin, D.L., “Interface for EVA Human-Machine Interaction,” 

38th International Conference on Environmental Systems, SAE Paper 2008-01-1986. 

8. Jacobs, S.E., Di Capua, M., Husain, S.A., Mirvis, A., Akin, D.L., “Incorporating Advanced Controls, 

Displays and Other Smart Elements into Space Suit Design,” 39th International Conference on 

Environmental Systems, SAE Paper 2009-01-2472. 

9. Hieronymus, J.L., “Clarissa Spoken Dialogue System for Procedure Reading and Navigation,” 

IAC-04-T.3.07, NASA Ames Research Center, USA, 2004. 

10. Rayner, M., Hockey, B.A., Renders, J.M., Chatzichrisafi, N., and Farrel, K., “Spoken Language 

Processing in the Clarissa Procedure Browser,” Natural Language Engineering, 1, Cambridge 

University Press, U.K., 2005. 

11. Adams, R.J., Olowin, A.B., Hannaford, B., and Sands, O., “Tactile Data Entry for Extravehicular 

Activity,” 2011 IEEE World Haptics Conference (WHC), Istanbul, TK, June 2011. 

12. Lindsay, J., Adams, R., and Hannaford, B., “Improving Tactile Feedback With an Impedance 

Adapter,” 2013 IEEE World Haptics Conference (WHC), Daejeon, Korea, April 2013. 

13. Soukoreff, R.W. and MacKenzie, I.S., “Metrics for Text Entry Research: An Evaluation of MSD and 

KSPC, and a New Unified Error Metric,” CHI 2003, Ft. Lauderdale, FL, 2003. 

14. Humes, L.E. and Floyd, S.S., “Measures of Working Memory, Sequence Learning, and Speech 

Recognition in the Elderly,” Journal of Speech, Language, and Hearing Research, vol. 48, pp. 224-

235, Feb 2005. 

15. Hardin, J. and Hilbe, J., Generalized Estimating Equations, Chapman and Hall/CRC, London, 2003. 

16. Fitzmaurice, G., Laird, N., and Ware, J., Applied Longitudinal Analysis, Wiley-Interscience, 

Hoboken, NJ, 2004. 

17. Hart, S.G. and Staveland, L.E. “Development of NASA-TLX: Results of Empirical and Theoretical 

Research,” In P.A. Hancock & N. Meshkati (Eds.), Human Mental Workload, Amsterdam: NorthHolland, 1988, pp. 139–183. 

18. Hager-Ross, C. and Schieber, M.H., “Quantifying the Independence of Human Finger Movements: 

Comparisons of Digits, Hands, and Movement Frequencies,” Journal of Neuroscience, vol. 20, no. 22, 

pp. 8542–8550, 2000. 

19. Humes, L.E. and Floyd, S.S., “Measures of Working Memory, Sequence Learning, and Speech 

Recognition in the Elderly,” Journal of Speech, Language, and Hearing Research, vol. 48, pp. 224-

235, Feb 2005. 

20. Brewster, S., Chohan, F., and Brown L., “Tactile Feedback for Mobile Interactions,” Proceedings of 

the SIGCHI Conference on Human Factors in Computing Systems, 2007. 

21. Dijkerman, C.H. and de Haan, E.H.F., “Somatosensory Processes Subserving Perception and Action,” 

Behavioral and Brain Sciences, vol. 30, 189-239, 2007. 

22. Ross, A., Kosmo, J., and Janoiko, B., “Historical Synopses of Desert RATS 1997–2010 and a 

Preview of Desert RATS 2011,” Acta Astronautica, May 2012.