General public preference is clear nowadays: visit any retail or online computer store to see that. Nonetheless, mice are still not exclusive on the market. Let's try to evaluate actual data related to the pointing devices comparison. Over the years of wide popularity of computers, many researches tried to compare different devices to find out which one is better for particular task. Many reports are available, often confirming the mouse is better - but question is: how the data were obtained, and who are users involved in testing?
One example is a military-oriented survey conducted in 2003 by German Research Institute for Communication, Information Processing to determine the best device for Naval Anti-Air Warfare Operators. Results published seems to confirm trackball is less productive. Sounds like pure scientific data? At first glance, yes. But there's one thing more. Take some average Joe selected to participate in tests. It's clear he was using a mouse in school, mouse at home, mouse in college, mouse at library, mouse at workplace... Now, he'll have to perform some accuracy and speed testing operations with mouse, trackball, voice input and touchscreen. Guess what?
In my opinion, there are no "the best" device type for all. It's merely matter of personal preference, and if some kind of input tool is perfect for you - that's fine, but not necessarily true for others. Below is a list of various reports I've collected, but I won't recommend to take any single of them as the source of absolute truth...
A Comparison of Input Devices in Elemental Pointing and Dragging Tasks
Scott MacKenzie, Abigail Sellen, and William Buxton
Ontario Institute for Studies in Education
Dynamic Graphics Project, Computer Systems Research Institute
University of Toronto
Seneca College of Applied Arts and Technology
Toronto, Ontario, Canada
https://trackballs.eu/media/library/A c ... evices.pdf
===An experiment is described comparing three devices (a mouse, a trackball, and a stylus with tablet) in the performance of pointing and dragging tasks. During pointing, movement times were shorter and error rates were lower than during dragging. It is shown that Fitts’ law can model both tasks, and that within devices the index of performance is higher when pointing than when dragging. Device differences also appeared. The stylus displayed a higher rate of information processing than the mouse during pointing but not during dragging. The trackball ranked third for both tasks.
Age Differences in Computer Input Device Use: A Comparison of Touchscreen, Trackball, and Mouse
Ho-chuen Ng, Da Tao, and Calvin K.L. Or
Department of Industrial & Manufacturing Systems Engineering,
The University of Hong Kong
https://trackballs.eu/media/library/A C ... Mouse.pdf
===This study examined age-related differences in user performance and preference with three computer input devices (mouse, touchscreen, and trackball) among older, middle-aged, and younger adults. Sixty-six participants were recruited and equally split into the three age groups. The results showed that age and input device had significant effect on task completion time and number of error. There were significant age-related performance differences in task completion time among the input devices. Ratings of users’ preference indicated that the older adults preferred trackball to the mouse and touchscreen. Our findings suggest that the touchscreen could moderate only part of the age-related performance differences and conferred limited benefits to older adults. More research efforts are required to examine user characteristics, user perception on the use of the devices, and task requirements before we can determine the benefits of an input device for users in different ages.
Accuracy Measures for Evaluating Computer Pointing Devices
I. Scott MacKenzie, Tatu Kauppinen, Miika Silfverberg
CHI 2001 • 31 MARCH – 5 APRIL
https://trackballs.eu/media/library/Acc ... evices.PDF
===In view of the difficulties in evaluating computer pointing devices across different tasks within dynamic and complex systems, new performance measures are needed. This paper proposes seven new accuracy measures to elicit (sometimes subtle) differences among devices in precision pointing tasks. The measures are target re-entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement error, and movement offset. Unlike movement time, error rate, and throughput, which are based on a single measurement per trial, the new measures capture aspects of movement behaviour during a trial. The theoretical basis and computational techniques for the measures are described, with examples given. An evaluation with four pointing devices was conducted to validate the measures. A causal relationship to pointing device efficiency (viz. throughput) was found, as was an ability to discriminate among devices in situations where differences did not otherwise appear. Implications for pointing device research are discussed.
Accurate Measurements of Pointing Performance from In Situ Observations
Krzysztof Z. Gajos, Katharina Reinecke and Charles Herrmann
Harvard School of Engineering and Applied Sciences
https://trackballs.eu/media/library/Acc ... rmance.pdf
===We present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for discriminating between deliberate, targeted mouse pointer movements and those movements that were affected by any extraneous factors. To develop and validate these classifiers, we developed logging software to unobtrusively record pointer trajectories as participants naturally interacted with their computers over the course of several weeks. Each participant also performed a set of pointing tasks in a formal study set-up. For each movement, we computed a set of measures capturing nuances of the trajectory and the speed, acceleration, and jerk profiles. Treating the observations from the formal study as positive examples of deliberate, targeted movements and the in situ observations as unlabeled data with an unknown mix of deliberate and distracted interactions, we used a recent advance in machine learning to develop the classifiers. Our results show that, on four distinct metrics, the data collected in-situ and filtered with our classifiers closely matches the results obtained from the formal experiment.
An Evaluation of Input Devices for 3-D Computer Display Workstations
Robert J. Beaton, Richard J. DeHoff, Novia Weiman, and Peter W. Hildebrandt
Imaging Research Laboratory, Tektronix Laboratories, P.O. Box 500, Mail Stop 50 -320
Tektronix, Inc., Beaverton, Oregon
https://trackballs.eu/media/library/An ... or 3-D.pdf
===This paper reports several results from an on-going research program designed to examine the utility of alternate input device technologies for 3-dimensional (3-D) computer display workstations. In this paper, operator performance levels on a 3-D cursor-positioning task were compared using three input devices: (1) a trackball that allowed unrestricted (i.e., free-space) movements within the display space, (2) a mouse that provided selectable two-axis (i.e., plane) movements, and (3) a set of thumbwheels that provided separate controls for orthogonal single-axis (i.e., vector) movements. In addition, the input device evaluation was conducted for two operationally distinct 3-D display techniques: (1) a linear perspective encoding of image depth information and (2) a field -sequential stereoscopic encoding of depth information. Results are discussed in terms of input device selection and general design considerations for the user interface to 3-D computer workstations.
An Indexed Bibliography on Tracking
Salvatore P. Schipani
U.S. Army Human Engineering Laboratory
Aberdeen Proving Ground, Maryland
https://trackballs.eu/media/library/An ... acking.pdf
===This document comprises a bibliography about tracking and related literature, with corresponding documentation (indexed author and subject listings) to enhance its utility as a reference. This information was initially gathered to support the premise that tracking research should be considered when contemplating the development of military systems for teleoperation scenarios.
Comparison of Multiple 3D Rotation Methods
Yao Jun Zhao, Dmitri Shuralyov, Wolfgang Stuerzlinger
Department of Computer Science and Engineering
York University, Toronto, Canada
https://trackballs.eu/media/library/Com ... ethods.pdf
===In this paper, we present an experimental comparison of 3D rotation techniques. In all techniques, the third degree of freedom is controlled by the mouse-wheel. The investigated techniques are Bell’s Trackball, Shoemake’s Arcball and the Two-axis Valuator method. The result from a pilot showed no performance or accuracy difference among these three. However, we did observe minor differences in an experiment with more participants and trials, though these differences were not significant. Also, from questionnaires, we found that most of the users considered the use of mouse wheel helpful for completing the tasks.
Comparison of Six Cursor Control Devices Based on Fitts' Law Models
Brian W. Epps
Human Factors Laboratory, V.P.I. & S.U.
https://trackballs.eu/media/library/Com ... evices.pdf
===Six cursor control devices were compared on a target acquisition task which required subjects to move a cursor into square targets of varying sizes and at various screen distances. The target acquisition performance data were fitted to movement time models proposed by Fitts, Jagacinski, and Kvalseth. Regression analysis results indicated good predictions of target acquisition performance for the six cursor devices. The best fit was obtained with the trackball across the three models.
Empirical Comparison of Five Input Devices for Anti-Air Warfare Operators
Morten Grandt, Claudius Pfendler, Oliver Mooshage
FGAN - Research Institute for Communication, Information Processing, and Ergonomics
https://trackballs.eu/media/library/Emp ... evices.pdf
===Operators appointed in the warfare area Anti Air Warfare (AAW) on battleships have to identify and classify airborne objects using sensor data presented on workstation displays. They feed results into the system so that an adequate evaluation of the situation can be conducted and response can be initiated if necessary. Stress and time load are very high as the decision basis is often limited and wrong decisions may have severe consequences. Aside from computer assistance, adequate input devices could also support operators by allowing faster responses. Therefore, in an experiment a conventional computer mouse, two different trackballs as well as touch and speech input were compared in respect to response time, correctness, and subjective workload in a simplified AAW task. Furthermore popup menus and conventional buttons at the screen’s upper edge were compared. The results demonstrate that in general touch input and mouse show the fastest response times whereas speech input and the trackballs constitute the other extreme. As well, popup menus were inferior to the buttons in response times. Workload was mostly consistent with these results, except for speech input, which was rated low in workload.
Evaluation of Three Wearable Computer Pointing Devices for Selection Tasks
Joanne E. Zucco, Bruce H. Thomas, and Karen Grimmer
Wearable Computer Laboratory
School of Computer and Information Science
Centre of Allied Health
School of Health Sciences
University of South Australia
https://trackballs.eu/media/library/Eva ... evices.pdf
===This paper presents the results of an experiment comparing three commercially available pointing devices (a trackball, gyroscopic mouse and Twiddler2 mouse) performing selection tasks for use with wearable computers. The study involved 30 participants performing selection tasks with the pointing devices while wearing a wearable computer on their back and using a head-mounted display. The error rate and time to complete the selection of the circular targets was measured. When examining the results, the gyroscopic mouse showed the fastest mean time for selecting the targets, while the trackball performed with the lowest error rate.
Gameplay Evaluation of the Trackball Controller
Daniel Natapov I. Scott MacKenzie
Department of Computer Science and Engineering
York University, Toronto, Canada
https://trackballs.eu/media/library/Gam ... roller.pdf
===We present a study of user performance in a First Person Shooter game comparing a prototype trackball controller, a standard game controller, and a keyboard and mouse. The prototype controller replaces the right analog stick of a standard game controller (used for pointing and camera control) with a trackball. To measure the performance of the three input devices, participants played two games. Penguin Hunt measured the number of target hits per minute, which was 28.1 with the keyboard and mouse, 22.9 with the trackball controller, and 21.7 with the standard controller. Modern Warfare measured average completion times, which were 26.8 s with the keyboard and mouse, 31.8 s with the trackball controller and 35.5 s with the standard controller. The trackball controller represents a 5.5% target hits increase over the standard controller in Penguin Hunt, and a 10.4% speed-up in trial completion time in Modern Warfare.
Gaze-supported pointing devices for day-to-day computer interaction
KTH Royal Institute of Technology
School of Information and Communication
https://trackballs.eu/media/library/Gaz ... device.pdf
===In this work we assess four prototypes of gaze-supported input devices that aim to be more ergonomic alternatives than the mouse and touchpads for day to day computer usage. These prototypes (nunchuck, ring, smartphone and trackball)have three properties. First, they enable a distant interaction from the computer, letting the user have a 135-degree body-thigh sitting posture. Second, they allow one-handed interactions, a convenient setting for people that can only use one hand due to ergonomic complications in the other one. And last, they are based on familiar input technologies and conventions, so users can easily learn how to use them. An experiment on pointing and drag & drop tasks was conducted to evaluate quantitative properties of the devices and get qualitative user feedback. The smartphone ranked first on almost all the user feedback entries.The measures of speed and accuracy presented several outliers.According to the measured results, the smartphone was the fastest pointing device and the most accurate for every task; the trackball was the fastest device for drag & drop tasks. The nunchuck ranked last in all the measures. Based on this study, future work should be conducted on improving the design of the companion devices based on multitouch and trackball technologies.
I. Scott MacKenzie
The Wiley Handbook of Human Computer Interaction
© 2018 John Wiley & Sons Ltd.
https://trackballs.eu/media/library/Han ... action.pdf
===Human movement is ubiquitous in computing. Our arms, wrists, and fingers busy themselves on keyboards, desktops, and contact‐sensitive displays. So, matching the movement limits and capabilities of humans with interaction techniques on computing systems is an important area of research in human‐computer interaction (HCI). Considerable HCI research is directed at modeling, predicting, and measuring human performance. In the realm of human movement, Fitts’ law is the preeminent model for this research.
Hardware and Software Resolution For a Pointing Device
© 1993 Microchip Technology Inc.
https://trackballs.eu/media/library/Har ... Device.pdf
===The rated differences in pointing device resolution can be confusing to the user; this application note describes the method for calculating hardware resolution of a pointing device that incorporates Microchip's MTA41XXX Mouse Controller. It also includes an explanation of the software controlled resolution for these same devices. Mice and trackball resolution is rated in terms of DPI (Dots Per Inch). This resolution may be controlled via hardware or software, but specific differences exist in each case
Human Performance Using Computer Input Devices in the Preferred and Non-Preferred Hands
Paul Kabbash, I. Scott MacKenzie, William Buxton
Computer Systems Research Institute, University of Toronto
Dept. of Computing & Information Science, University of Guelph
University of Toronto & Xerox PARC, c/o Computer Systems Research Institute
https://trackballs.eu/media/library/Hum ... evices.pdf
===Subjects’ performance was compared in pointing and dragging tasks using the preferred and non-preferred hands. Tasks were tested using three different input devices: a mouse, a trackball, and a tablet-with-stylus. The trackball had the least degradation across hands in performing the tasks, however it remained inferior to both the mouse and stylus. For small distances and small targets, the preferred hand was superior. However, for larger targets and larger distances, both hands performed about the same. The experiment shows that the non-pteferred hand is more than a poor approximation of the preferred hand. The hands are complementary, each having its own strength and weakness. One design implication is that the non-preferred hand is well suited for tasks that do not require precise action, such as scrolling.
A Journal of Theoretical, Empirical, and Methodological Issues of User Science and of System Design
Volume 7, Number 1, 1992
Thomas P. Moran, Xerox Palo Alto Research Center
Lawrence Erlbaum Associates, Publishers
Hillsdale, New Jersey, Hove and London
https://trackballs.eu/media/library/Hum ... Center.pdf
Temporal Aspects of Tasks in the User Action Notation
H. Rex Hartson, Philip D. Gray
Virginia Polytechnic Institute and State University
Inferring Graphical Procedures: The Compleat MetamouseThe need for communication among a multiplicity of cooperating roles in user interface development translates into the need for a common set of interface design representation techniques. The important difference between design of the interaction part of the interface and design of the interface software calls for representation techniques with a behavioral view-a view that focuses on user interaction rather than on the software. The User Action Notation (UAN) is a user- and task-oriented notation that describes physical (and other) behavior of the user and interface as they perform a task together. The primary abstraction of the UAN is a user task. The work reported here addresses the need to identify temporal relation- ships within user task descriptions and to express explicitly and precisely how designers view temporal relationships among those tasks. Drawing on simple temporal concepts such as events in time and preceding and overlapping of time intervals, we identify basic temporal relationships among tasks: se- quence, waiting, repeated disjunction, order independence, interruptibility, one-way interleavability, mutual interleavability, and concurrency. The UAN temporal relations, through the notion of modal logic, offer an explicit and precise representation of the specific kinds of temporal behavior that can occur in asynchronous user interaction without the need to detail all cases that might result.
David L. Maulsby, Ian H. Witten, Kenneth A. Kittlitz, and Valerio G. Franceschin
University of Calgary
Fitts' Law as a Research and Design Tool in Human-Computer InteractionMetamouse is a demonstrational interface for graphicai editing tasks within a drawing program. The user specifies a procedure by performing an example execution trace and creating graphical tools where necessary to help make constraints explicit. The system generalizes the user's action sequence, identifying key features of individual steps and disregarding coincidental events. It creates a program with loops and conditional branches as appro- priate and predicts upcoming actions, thereby reducing the tedium of repetitive and precise graphical editing. It uses default reasoning about graphical constraints to make initial generalizations and enables the user to correct these hypotheses either by rejecting its predictions or by editing iconic descriptors it displays after each action.
I. Scott MacKenzie
University of Toronto
===According to Fitts' law, human movement can be modeled by analogy to the transmission of information. Fitts' popular model has been widely adopted in numerous research areas, including kinematics, human factors, and (recently) human-computer interaction (HCI). The present study provides a historical and theoretical context for the model, including an analysis of problems that have emerged through the systematic deviation of observations from predictions. Refinements to the model are described, including a formulation for the index of task difficulty that is claimed to be more theoretically sound than Fitts' original formulation. The model's utility in predicting the time to position a cursor and select a target is explored through a review of six Fitts' law studies employing devices such as the mouse, trackball, joystick, touchpad, helmet-mounted sight, and eye tracker. An analysis of the performance measures reveals tremendous inconsistencies, making across-study comparisons difficult. Sources of experimental variation are identified to reconcile these differences.
Input devices for web browsing: age and hand effects
Tiffany Jastrzembski, Neil Charness, Patricia Holley and Jeffrey Feddon
Department of Psychology, Florida State University, Tallahassee, USA
https://trackballs.eu/media/library/Inp ... owsing.pdf
===The work reported in this paper examined performance on a mixed pointing and data entry task using direct and indirect positioning devices for younger, middle-aged, and older adults (n=72) who were experienced mouse users. Participants used both preferred and non-preferred hands to perform an item selection and text entry task simulating a typical web page interaction. Older adults performed more slowly than middle-aged adults who in turn performed more slowly than young adults. Performance efficiency was superior with the mouse for older adults only on the first two trial blocks. Thereafter mouse and light pen yielded equivalent performance. For other age groups, mouse and light pen were equivalent at all points of practice. Contrary to prior research revealing superior performance with a light pen for pure pointing tasks, these results suggest that older adults may initially perform worse with a light pen than a mouse for mixed tasks.
Input Devices: An Illustrated Tour
Human Input to Computer Systems, 25 October, 2016
===The purpose of chapter is to provide a quick introduction to a range of input devices. It will give the reader a sense of how diverse the range of devices is, both across and within device categories. In it, devices are organized primarily by their physical and mechanical properties. This is the way in which they have mostly been discussed in the literature (for example, Newman & Sproull, 1973; Foley & van Dam, 1982; Sherr, 1988; MacKenzie, 1995). While this is a good start, much of the rest of this book is to build on this foundation, and try and balance our discussion of technologies with one that focuses more on the user, intent, and context.
Motor Behaviour Models for Human-Computer Interaction
I. Scott MacKenzie
Department of Computer Science
https://trackballs.eu/media/library/Mot ... action.pdf
===The movement of body and limbs is inescapable in human-computer interaction (HCI). Whether browsing the web or intensively entering and editing text in a document, our arms, wrists, and fingers are at work on the keyboard, mouse, and desktop. Our head, neck, and eyes move about attending to feedback marking our progress. This chapter is motivated by the need to match the movement limits, capabilities, and potential of humans with input devices and interaction techniques on computing systems. Our focus is on models of human movement relevant to human-computer interaction. Some of the models discussed emerged from basic research in experimental psychology, whereas others emerged from, and were motivated by, the specific need in HCI to model the interaction between users and physical devices, such as mice and keyboards.
Performance Differences in the Fingers, Wrist, and Forearm in Computer Input Control
Ravin Balakrishnan, I. Scott MacKenzie
Dept. of Computer Science
Dept. of Computing & Information Science
University of Toronto
https://trackballs.eu/media/library/Per ... orearm.pdf
===Recent work in computer input control has sought to maxi- mize the use of the fingers in the operation of computer pointing devices. The main rationale is the hypothesis that the muscle groups conmolling the fingers have a higher bandwidth than those controlling other segments of the human upper limb. Evidence which supports this, however, is inconclusive. We conducted an experiment to determine the relative bandwidths of the fingers, wrist, and forearm and found that the fingers do not necessarily outperform the other limb segments. Our results indicate that the bandwidth of the unsupported index finger is approximately 3.0 bits/s while the wrist and forearm have bandwidths of about 4.1 bits/s. We also show that the thumb and index finger work- ing together in a pinch grip have an information processing rate of about 4.5 bits/s. Other factors which influence the relative performance of the different limbs in manipulation tasks are considered.
How choice of mouse may affect response timing in psychological studies
Richard R. Plant, Nick Hammond, and Tom Whitehouse
University of York, England
===Mice from the early 1990s seemed to offer a cheap and viable alternative to more expensive response boxes, with fairly consistent results being found between studies. However, has anything changed in the intervening decade? Are newer mice technologies necessarily better? Is USB a better mouse interface than the old-fashioned serial interface? With such questions in mind, we outline a method for bench-testing the timing characteristics of mice outside of a PC, in order to predict their contribution to response timing. A sample set of mice was tested under a visual stimulus–response paradigm, using E-Prime to compare predicted performance with measured response registration. A representative range of mice technologies was tested alongside a standard keyboard and an E-Prime deluxe response box. The implications for using any response device other than a recognized response box are discussed.
Testing Pointing Device Performance and User Assessment with the IS0 9241, Part 9 Standard
Sarah A. Douglas and Arthur E. Kirkpatrick, I. Scott MacKenzie
Computer and Information Science Dept.
University of Oregon
Dept. of Computing and Information Science
University of Guelph
https://trackballs.eu/media/library/Tes ... ssment.pdf
===The IS0 9241, Part 9 Draft International Standard for testing computer pointing devices proposes an evaluation of performance and comfort. In this paper we evaluate the scientific validity and practicality of these dimensions for two pointing devices for laptop computers, a finger-controlled isometric joystick and a touchpad. Using a between-subjects design, evaluation of performance using the measure of throughput was done for one-direction and multi-directional pointing and selecting. Results show a significant difference in throughput for the multi-directional task, with the joystick 27% higher; results fm the one- direction task were non-significant. After the experiment, participants rated the device for comfort, including operation, fatigue, and usability. The questionnaire showed no overall difference in the responses, and a significant statistical dBerence in only the question concerning force required to operate the device-the joystick requiring slightly more force. The paper concludes with a discussion of problems in implementing the IS0 standard and recommendations for improvement.
The Trackball Controller: Improving the Analog Stick
Daniel Natapov, I. Scott MacKenzie
Department of Computer Science and Engineering
York University, Toronto, Canada
https://trackballs.eu/media/library/The ... Stick.pdf
===Two groups of participants (novice and advanced) completed a study comparing a prototype game controller to a standard game controller for point-select tasks. The prototype game controller replaces the right analog stick of a standard game controller (used for pointing and camera control) with a trackball. We used Fitts’ law as per ISO 9241-9 to evaluate the pointing performance of both controllers. In the novice group, the trackball controller’s throughput was 2.69 bps – 60.1% higher than the 1.68 bps observed for the standard controller. In the advanced group the trackball controller’s throughput was 3.19 bps – 58.7% higher than the 2.01 bps observed for the standard controller. Although the trackball controller performed better in terms of throughput, pointer path was more direct with the standard controller.
Age-Related Differences in Performance with Touchscreens Compared to Traditional Mouse Input
Leah Findlater, Jon E. Froehlich, Kays Fattal, Jacob O. Wobbrock, Tanya Dastyar
College of Information Studies
Dept. of Computer Science
University of Maryland
The Information School
University of Washington
https://trackballs.eu/media/library/Tou ... Input.pdf
===Despite the apparent popularity of touchscreens for older adults, little is known about the psychomotor performance of these devices. We compared performance between older adults and younger adults on four desktop and touchscreen tasks: pointing, dragging, crossing and steering. On the touchscreen, we also examined pinch-to-zoom. Our results show that while older adults were significantly slower than younger adults in general, the touchscreen reduced this performance gap relative to the desktop and mouse. Indeed, the touchscreen resulted in a significant movement time reduction of 35% over the mouse for older adults, compared to only 16% for younger adults. Error rates also decreased.
Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts’ law research in HCI
R. William Soukoreff, I. Scott MacKenzie
Department of Computer Science and Engineering, York University
Toronto, Ontario, Canada
© 2004 Published by Elsevier Ltd.
https://trackballs.eu/media/library/Tow ... uation.pdf
===This paper makes seven recommendations to HCI researchers wishing to construct Fitts’ law models for either movement time prediction, or for the comparison of conditions in an experiment. These seven recommendations support (and in some cases supplement) the methods described in the recent ISO 9241-9 standard on the evaluation of pointing devices. In addition to improving the robustness of Fitts’ law models, these recommendations (if widely employed) will improve the comparability and consistency of forthcoming publications. Arguments to support these recommendations are presented, as are concise reviews of 24 published Fitts’ law models of the mouse, and 9 studies that used the new ISO standard.
User performance with trackball-mice
Poika Isokoski, Roope Raisamo, Benoit Martin, Grigori Evreinov
Tampere Unit for Computer-Human Interaction, Department of Computer Sciences,
University of Tampere, Finland
LITA, University Paul Verlaine, Metz, France
© 2006 Elsevier B.V. All rights reserved.
===Trackball-mice are devices that include both a trackball and a mouse. In this paper we discuss our experiences in building and testing trackball-mouse prototypes. We report four experiments on user performance with the prototypes used as trackball-mice, conventional mice, and in two-handed configuration with a separate trackball for the non-dominant hand. The results show that user performance with the two-handed configuration was better than in one-handed operation of a trackball-mouse and in one-handed operation of a mouse. Trackball-mouse use and conventional mouse use were more evenly matched. However, Trackball-mouse operation involves a skill that most users do not have whereas mouse operation is familiar to most. Therefore, widespread introduction of trackball-mice does not appear to be justified on performance grounds alone. However, trackball-mice can be used as regular mice by ignoring the ball. This makes them compatible with traditional graphical user interfaces while offering two extra degrees of freedom in tasks where they are beneficial.
The Effect of Input Device on User Performance With a Menu-Based Natural Language Interface
J J. Hendrickson, R. D. Williams
Naval Ocean Systems Center, San Diego, California
User Systems Engineering Organization at Texas Instruments, Dallas, Texas.
Virginia Polytechnic Institute and State University
https://trackballs.eu/media/library/The ... Device.pdf
===Menu-Based Natural Language (MBNL) provides a form of constrained natural language dialogue for human-computer interaclion where natural language words and phrases are displayed on the screen as menu items. Previous research on cursor devices has provided mixed results concerning "the best" cursor device and no firm recommendations were available for use with MBNL interfaces. This study was developed to determine the best inplt device for MBNL interfaces to Naval command and control databases. Results showed that search keys were slower than cursor keys and trackhall. No-scrolling was faster than scrolling. Trackball was preferred, and was more quickly learned than cursor keys.
Generalized Trackball for Surfing Over Surfaces
Luigi Malomo, Paolo Cignoni, Roberto Scopigno
Visual Computing Lab, ISTI - CNR, Italy
University of Pisa, Italy
https://trackballs.eu/media/library/Gen ... ckball.pdf
===We present an efficient 3D interaction technique: generalizing the well known trackball approach, this technique unifies and blends the two common interaction mechanisms known as panning and orbiting. The approach allows to inspect a virtual object by navigating over its surrounding space, remaining at a chosen distance and performing an automatic panning over its surface. This generalized trackball allows an intuitive navigation of topologically complex shapes, enabling unexperienced users to visit hard-to-reach parts better and faster than with standard GUI components. The approach is based on the construction of multiple smooth approximations of the model under inspection; at rendering time, it constrains the camera to stay at a given distance to these approximations. The approach requires negligible preprocessing and memory overhead and works well for both mouse-based and touch interfaces. An informal user study confirms the impact of the proposed technique.
Evaluating the effect of four different pointing device designs on upper extremity posture and muscle activity during mousing tasks
Michael Y.C. Lin, Justin G. Young, Jack T. Dennerlein
Harvard School of Public Health, Boston, USA
Kettering University, Flint, USA
Bouve College of Health Sciences, Northeastern University, Boston, USA
© 2014 Elsevier Ltd and The Ergonomics Society
https://trackballs.eu/media/library/dif ... evices.pdf
===The goal of this study was to evaluate the effect of different types of computer pointing devices and placements on posture and muscle activity of the hand and arm. A repeated measures laboratory study with 12 adults (6 females, 6 males) was conducted. Participants completed two mouse-intensive tasks while using a conventional mouse, a trackball, a stand-alone touchpad, and a rollermouse. A motion analysis system and an electromyography system monitored right upper extremity postures and muscle activity, respectively. The rollermouse condition was associated with a more neutral hand posture (lower inter-fingertip spread and greater finger flexion) along with significantly lower forearm extensor muscle activity. The touchpad and rollermouse, which were centrally located, were associated with significantly more neutral shoulder postures, reduced ulnar deviation, and lower forearm extensor muscle activities than other types of pointing devices. Users reported the most difficulty using the trackball and touchpad. Rollermouse was not more difficult to use than any other devices. These results show that computer pointing device design and location elicit significantly different postures and forearm muscle activities during use, especially for the hand posture metrics.
Effects of input device and motion type on a cursor-positioning task
Yi-Jan Yau, Sheue-Ling Hwang, Chin-Jung Chao
National Tsing Hua University, Chung-Shan Institute of Science and Technology, Sanchong City, Taipei County, Taiwan
https://trackballs.eu/media/library/Eff ... device.pdf
===Many studies have investigated the performance of using nonkeyboard input devices under static situations, but few of them have considered the effects of motion direction on manipulating these input devices. This study with twelve men compared their performance of using four input devices (three trackballs: currently used, trackman wheel and erectly held trackballs as well as one touch screen) under five motion directions of static, heave, roll, pitch and random movements. The input device and motion direction significantly affected the movement speed and accuracy, and their interaction significantly affected the movement speed. The touch screen was the fastest but the least accurate input device. The erectly held trackball was the slowest, whereas the error rate of the currently used trackball was the lowest. The impairments of the random motion on movement time and error rate were larger than those of other motion directions. Taking the objective and subjective evaluations into account, the trackman wheel and currently used trackball were more efficient for operation than the erectly held trackball and touch screen under the motion environments