For evaluations of 2D target selection using Fitts’ law, ISO 9241-411 recommends using the effective target width (\(W_\text{e}\)) calculated using the univariate standard deviation of selection coordinates. Related research proposed using a bivariate standard deviation; however, the proposal was only tested using a single speed-accuracy bias condition, thus the assessment was limited. We compared the univariate and bivariate techniques in a 2D Fitts’ law experiment using three speed-accuracy biases and 346 crowdworkers. Calculating \(W_\text{e}\) using the univariate standard deviation yielded higher model correlations across all bias conditions and produced more stable throughput among the biases. The findings were also consistent in cases using randomly sampled subsets of the participant data. We recommend that future research should calculate \(W_\text{e}\) using the univariate standard deviation for fair performance evaluations. Also, we found trivial effects when using nominal or effective amplitude and using different perspectives of the task axis.

Gaze is inherent in both head pointing and walking. When a person performs these tasks simultaneously, there may be detrimental effects on performance. This study examined whether differences in the number of gaze fixations explain why walking worsens pointing performance based on selection time. An 18-person user study compared the number of fixations with selection time under stationary and walking conditions. These conditions were tested using a Fitts’ law head pointing task in virtual reality. Both selection time and fixations per selection increased significantly with task difficulty, modulated by movement condition and pointing task parameters, resulting in a correlation between task performance and gaze behavior. Decoupling head pointing from gaze movement suggested that gaze is less involved in the physical aspect of head pointing. However, there is a contextual advantage of gaze-controlled interactions as its relative stability is particularly useful for precise pointing while walking.

Gaze-based interaction is increasingly used in hands-free and accessible interfaces. Although modern eye-trackers provide sufficient spatial and temporal resolution, accurate and efficient target selection remains a challenge. In this work, we compared three selection methods (wink, blink, dwell) and tested whether displaying or hiding the cursor affects performance in a 2D pointing task. Testing was standardized, as per ISO 9241-411, with performance evaluated using throughput, movement time, and error rate. Cursor visibility produced no significant effects. Concerning throughput, blink achieved the highest value (2.11 bps), followed by dwell-time (1.95 bps; ≈ 7.5% lower), whereas wink performed worst (1.56 bps). Although throughput was higher, only 16.7% of participants preferred blink; most favored dwell-time. Error rates further disadvantaged wink (20.4%), while dwell-time showed the lowest errors (1.82%). Overall, considering throughput, error rate, and user preference, wink is not recommended. Dwell-time selection is the best overall choice, offering a better balance for gaze-controlled interaction.

We present EarEOG, a real-time wheelchair control system using around-ear electrooculogram (EOG) signals. Electrodes are placed in standard over-the-ear headphones to improve user comfort. By detecting around-the-ear signals from eye gestures and jaw clenching, EarEOG offers a non-invasive and intuitive approach to low-latency wheelchair control. We describe the methods for signal acquisition, as well as the algorithms used for signal processing and classification. The feasibility, robustness, and low latency of EarEOG were confirmed through two experiments. The algorithm demonstrated a classification accuracy of 94.1% for all motion signals, which further improved to 97.3% when personalized models were applied. To ensure stability, we examined electrode impedance and algorithm accuracy across multiple trials where participants operated simulated wheelchairs while wearing EarEOG. The results indicated that when the electrode impedance was below 1 MΩ, all participants successfully controlled the simulated wheelchair. Furthermore, EarEOG demonstrated low latency, with recognition delays of less than 125 ms.

Monetization is fundamental to “free-to-play” mobile games, typically in the form of advertising placed within gameplay. Monetization within digital games is linked to deceptive design, and other ethically dubious practices such as loot-boxes. However, it is unclear what impact monetization has on the overall player experience. This research measured the experience and player performance in an experimental “Pong”-style game in three conditions: no advertising, static-interstitial advertising, and video-advertising. A between-subjects study was carried out with 95 participants aged 9-11 years playing the game in one of the three conditions, then completing the FunQ questionnaire. Results showed that while the static-interstitial advertising condition had a negative impact on player experience and player performance the video-advertising condition did not. Findings were ‘reported back’ to a group of the original child participants, feedback gathered during this session showed that children understood the findings and were able to contribute both additional insights and ideas for future research.

In a ten-week project with nine school classes across the North West of England we explored ocean health with IT-enabled solutions. We describe the activities carried out under headings of participation, learning, and design. Participation activities, which included recruitment, focused on setting the parameters for children’s inclusion and ensuring they understood how data might be used, and that handing in artefacts to the research team was their choice. Learning happened in an environment of contextual relevance that enabled children to develop data literacy whilst we could explore relevant research questions. Design was a journey from individual to whole-class design, while developing engineering thinking and social cohesion. We reflect on the journey showing that children learned from the activities and acquired a new enthusiasm for their local coastline. We reflect on how our inclusive approach can broaden HCI research to wider communities of children and encourage others to apply our model.

We present LookToFocus, a method to perform real-time manual camera focus based on eye tracking. LookToFocus and two alternative methods for manual focus1 photography tasks were compared in a user study. A novel manual focus camera simulation was used to test the methods. The first two methods were LookToFocus and LookToFocusNB (no bounding box). The third method, TapToFocus, used touch for manual focus and image capture, analogous to typical smartphone interaction. LookToFocusNB had the fastest mean capture time at 1429 ms; the mean capture times were 1431 ms for LookToFocusNB and 2416 ms for TapToFocus. When compared to TapToFocus, LookToFocus and LookToFocusNB had faster capture times because these algorithms start converging to the optimal focus as soon as the user spots the target. LookToFocus and LookToFocusNB also had no significant decrease in sharpness error compared to TapToFocus. Users preferred LookToFocus over both LookToFocusNB and TapToFocus.

Two cross-lingual (Nepalese and English) letter exchanges took place between school children from Nepal and England, using Digipal; an Android chatting application. Digipal uses Google Translate to enable children to read and reply in their native language. In two studies we analysed the errors made and the effect of errors on children’s understanding and on the flow of conversation. We found that errors of input negatively affected translation, although this can be reduced through initial grammar cleaning. We highlight features of children’s text that cause errors in translation whilst showing how children worked with and around these errors. Errors sometimes added humour and contributed to continuing the conversations.

A dry-electrode head-mounted sensor for visually-evoked electroencephalogram (EEG) signals has been introduced to the gamer market, and provides wireless, low-cost tracking of a user’s gaze fixation on target areas in real-time. Unlike traditional EEG sensors, this new device is easy to set up for non-professionals. We conducted a Fitts’ law study (N = 6) and found the mean throughput (TP) to be 0.82 bits/s. The sensor yielded robust performance with error rates below 1%. The overall median activation time (AT) was 2.35 s with a minuscule difference between one or nine concurrent targets. We discuss whether the method might supplement camera-based gaze interaction, for example, in gaze typing or wheelchair control, and note some limitations, such as a slow AT, the difficulty of calibration with thick hair, and the limit of 10 concurrent targets.

This work compares four mid-air target selection methods (Push, Tap, Dwell, Pinch) with two types of ultrasonic haptic feedback (Select, HoverSelect) in a Fitts’ law experiment. Results revealed that Tap is the fastest, the most accurate, and one of the least physically and cognitively demanding selection methods. Pinch is relatively fast but error prone and physically and cognitively demanding. Dwell is slowest by design, yet the most accurate and the least physically and cognitively demanding. Both haptic feedback methods improve selection performance by increasing users’ spatial awareness. Particularly, Push augmented with Hover & Select feedback is comparable to Tap. Besides, participants perceive the selection methods as faster, more accurate, and more physically and cognitively comfortable with the haptic feedback methods.

The conventional dwell-based methods for text entry by gaze are typically slow and uncomfortable. A swipe-based method that maps gaze path into words offers an alternative. However, it requires the user to explicitly indicate the beginning and ending of a word, which is typically achieved by tedious gaze-only selection. This paper introduces TAGSwipe, a bi-modal method that combines the simplicity of touch with the speed of gaze for swiping through a word. The result is an efficient and comfortable dwell-free text entry method. In the lab study TAGSwipe achieved an average text entry rate of 15.46 wpm and significantly outperformed conventional swipe-based and dwell-based methods in efficacy and user satisfaction.

We present the design and evaluation of Talk-and-Gaze (TaG), a method for selecting and correcting errors with voice and gaze. TaG uses eye gaze to overcome the inability of voice-only systems to provide spatial information. The user's point of gaze is used to select an erroneous word either by dwelling on the word for 800 ms (D-TaG) or by uttering a "select" voice command (V-TaG). A user study with 12 participants compared D-TaG, V-TaG, and a voice-only method for selecting and correcting words. Corrections were performed more than 20% faster with D-TaG compared to the V-TaG or voice-only methods. As well, D-TaG was observed to require 24% less selection effort than V-TaG and 11% less selection effort than voice-only error correction. D-TaG was well received in a subjective assessment with 66% of users choosing it as their preferred choice for error correction in voice-based text entry.

We compared four audio-based radar metaphors for providing directional stimuli to users of AR headsets. The metaphors are clock face, compass, white noise, and scale. Each metaphor, or method, signals the movement of a virtual arm in a radar sweep. In a user study, statistically significant differences were observed for accuracy and response time. Beat-based methods (clock face, compass) elicited responses biased to the left of the stimulus location, and non-beat-based methods (white noise, scale) produced responses biased to the right of the stimulus location. The beat methods were more accurate than the non-beat methods. However, the non-beat methods elicited quicker responses. We also discuss how response accuracy varies along the radar sweep between methods. These observations contribute design insights for non-verbal, non-visual directional prompting.

Head-mounted displays offer full control over lighting conditions. When equipped with eye tracking technology, they are well suited for experiments investigating pupil dilation in response to cognitive tasks, emotional stimuli, and motor task complexity, particularly for studies that would otherwise have required the use of a chinrest, since the eye cameras are fixed with respect to the head. This paper analyses pupil dilations for 13 out of 27 participants completing a Fitts' law task using a virtual reality headset with built-in eye tracking. The largest pupil dilation occurred for the condition subjectively rated as requiring the most physical and mental effort. Fitts' index of difficulty had no significant effect on pupil dilation, suggesting differences in motor task complexity may not affect pupil dilation.

This paper presents a Fitts' law experiment and a clinical case study performed with a head-mounted display (HMD). The experiment compared gaze, foot, and head pointing. With the equipment setup we used, gaze was slower than the other pointing methods, especially in the lower visual field. Throughputs for gaze and foot pointing were lower than mouse and head pointing and their effective target widths were also higher. A follow-up case study included seven participants with movement disorders. Only two of the participants were able to calibrate for gaze tracking but all seven could use head pointing, although with throughput less than one-third of the non-clinical participants.

We present TouchGazePath, a multimodal method for entering personal identification numbers (PINs). Using a touch-sensitive display showing a virtual keypad, the user initiates input with a touch at any location, glances with their eye gaze on the keys bearing the PIN numbers, then terminates input by lifting their finger. TouchGazePath is not susceptible to security attacks, such as shoulder surfing, thermal attacks, or smudge attacks. In a user study with 18 participants, TouchGazePath was compared with the traditional Touch-Only method and the multimodal Touch+Gaze method, the latter using eye gaze for targeting and touch for selection. The average time to enter a PIN with TouchGazePath was 3.3 s. This was not as fast as Touch-Only (as expected), but was about twice as fast the Touch+Gaze. TouchGazePath was also more accurate than Touch+Gaze. TouchGazePath had high user ratings as a secure PIN input method and was the preferred PIN input method for 11 of 18 participants.

Touch is the predominant method of text entry on mobile devices. However, these devices also facilitate tilt-based input using accelerometers and gyroscopes. This paper presents the design and evaluation of TiltWriter, a non-touch, tilt-based text entry technique. TiltWriter aims to supplement conventional techniques when precise touch is not convenient or possible (e.g., the user lacks sufficient motor skills). Two keyboard layouts were designed and evaluated: Qwerty, and "Custom", a layout inspired by the telephone keypad. Novice participants in a longitudinal study achieved speeds of 12.1 wpm for Qwerty, 10.7 wpm for Custom. Error rate averaged 0.76% for Qwerty, 0.62% for Custom. A post-study extended session yielded 15.2 wpm with Custom, versus 11.5 wpm with Qwerty. Results and participant feedback suggest that a selection dwell time between 700 - 800 ms benefits accuracy.

Emerging ultra-small wearables like smartwatches pose a design challenge for touch-based text entry. This is due to the "fat-finger problem," wherein users struggle to select elements much smaller than their fingers. To address this challenge, we developed DriftBoard, a panning-based text entry technique where the user types by positioning a movable qwerty keyboard on an interactive area with respect to a fixed cursor point. In this paper, we describe the design and implementation of DriftBoard and report results of a user study on a watch-size touchscreen. The study compared DriftBoard to two ultra-small keyboards, ZoomBoard (tapping-based) and Swipeboard (swiping-based). DriftBoard performed comparably (no significant difference) to ZoomBoard in the major metrics of text entry speed and error rate, and outperformed Swipeboard, which suggests that panning-based typing is a promising input method for text entry on ultra-small touchscreens.

Exergame controllers are intended to add fun to monotonous exercise. However, studies on exergame controllers mostly focus on designing new controllers and exploring specific application domains without analyzing human factors, such as performance, comfort, and effort. In this paper, we examine the characteristics of a speed-based exergame controller that bear on human factors related to body movement and exercise. Users performed tasks such as changing and maintaining exercise speed for avatar control while their performance was measured. The exergame controller follows Fitts' law, but requires longer movement time than a gamepad and Wiimote. As well, resistance force and target speed affect performance. User experience data confirm that the comfort and mental effort are adequate as practical game controllers. The paper concludes with discussion on applying our findings to practical exergame design.

This paper presents an experimental investigation of gaze-based control modes for unmanned aerial vehicles (UAVs or "drones"). Ten participants performed a simple flying task. We gathered empirical measures, including task completion time, and examined the user experience for difficulty, reliability, and fun. Four control modes were tested, with each mode applying a combination of x-y gaze movement and manual (keyboard) input to control speed (pitch), altitude, rotation (yaw), and drafting (roll). Participants had similar task completion times for all four control modes, but one combination was considered significantly more reliable than the others. We discuss design and performance issues for the gaze-plus-manual split of controls when drones are operated using gaze in conjunction with tablets, near-eye displays (glasses), or monitors.

This paper introduces a text entry application for users with physical disabilities who cannot utilize a manual keyboard. The system allows the user to enter text hands-free, with the help of "Non-verbal Vocal Input" (e.g., humming or whistling). To keep the number of input sounds small, an ambiguous keyboard is used. As the user makes a sequence of sounds, each representing a subset of the alphabet, the program searches for matches in a dictionary. As a model for the system, the scanning-based application QANTI was redesigned and adapted to accept the alternative input signals. The usability of the software was investigated in an international longitudinal study done at locations in the Czech Republic, Germany, and the United States. Eight test users were recruited from the target community. The users differed in the level of speech impairment. Three users did not complete the study due to the severity of their impairment. By the end of the experiment, the users were able to enter text at rates between 10 and 15 characters per minute.

We present what we believe is the most efficient and quickest four-key text entry method available. H4-Writer uses Huffman coding to assign minimized key sequences to letters, with full access to error correction, punctuation, digits, modes, etc. The key sequences are learned quickly, and support eyes-free entry. With KSPC = 2.321, the effort to enter text is comparable to multitap on a mobile phone keypad; yet multitap requires nine keys. In a longitudinal study with six participants, an average text entry speed of 20.4 wpm was observed in the 10th session. Error rates were under 1%. To improve external validity, an extended session was included that required input of punctuation and other symbols. Entry speed dropped only by about 3 wpm, suggesting participants quickly leveraged their acquired skill with H4-Writer to access advanced features.

Areas of design improvements for BlinkWrite, an eye blink text entry system, are examined, implemented, and evaluated. The result, BlinkWrite2, is a text entry system for individuals with severe motor impairment. Since the ability to blink is often preserved, even in severe conditions such as locked-in syndrome, BlinkWrite2 allows text entry and correction with blinks as the only input modality. Advantages of BlinkWrite2 over its predecessor include an increase in text entry speed. In a user evaluation, 12 participants achieved an average text entry rate of 5.3 wpm, representing a 16% increase over BlinkWrite and a 657% increase over the next fastest video-based eye blink text entry system reported in the literature.

The human eye, with the assistance of an eye tracking apparatus, may serve as an input controller to a computer system. Much like point-select operations with a mouse, the eye can "look-select", and thereby activate items such as buttons, icons, links, or text. Applications for accessible computing are particularly enticing, since the manual ability of disabled users is often lacking or limited. Whether for the able-bodied or the disabled, computer control systems using the eye as an input "device" present numerous research challenges. These involve accommodating the innate characteristics of the eye, such as movement by saccades, jitter and drift in eye position, the absence of a simple and intuitive selection method, and the inability to determine a precise point of fixation through eye position alone.

The design and evaluation of a scanning ambiguous keyboard (SAK) is presented. SAK combines the most demanding requirement of a scanning keyboard—input using one key or switch—with the most appealing feature of an ambiguous keyboard—one key press per letter. The optimal design requires just 1.713 scan steps per character for English text entry. In a provisional evaluation, 12 able-bodied participants each entered 5 blocks of text with the scanning interval decreasing from 1100 ms initially to 700 ms at the end. The average text entry rate in the 5 th block was 5.11 wpm with 99% accuracy. One participant performed an additional five blocks of trials and reached an average speed of 9.28 wpm on the 10 th block. Afterwards, the usefulness of the approach for persons with severe physical disabilities was shown in a case study with a software implementation of the idea explicitly adapted for that target community.

A new one-key text entry method is presented. SAK, for ambiguous keyboard, combines one-key physical input (including error correction) with three virtual letter keys and a SPACE key. The virtual letter keys are highlighted in sequence (scanned) and selected when the key bearing the desired letter receives focus. There is only one per letter. Selecting SPACE transfers scanning to a word-selection region, which presents a list of candidate words. A novel feature of SAK is multiple-letter-selection in a single scanning interval. In an evaluation with 12 participants, average entry speeds reached 5.11 wpm (all trials, 99% accuracy) or 7.03 wpm (error-free trials). A modification using timer restart on selection allowed for more time and more selections per scanning interval. One participant performed extended trials (5 blocks x 5 phrases/block) with the modification and reached an average entry speed of 9.28 wpm.

This paper argues that the speed-accuracy trade-off arises as a consequence of Shannon's fundamental theorem for a channel with noise, when coupled with two additional postulates: that people are imperfect information processors, and that motivation is a necessary condition of the speed-accuracy tradeoff.

Unistrokes and Graffiti are stylus-based text entry techniques. While Unistrokes is recognized in academia, Graffiti is commercially prevalent in PDAs. Though numerous studies have investigated the usability of Graffiti, none exists to compare its long-term performance with that of Unistrokes. This paper presents a longitudinal study comparing entry speed, correction rate, stroke duration, and preparation (i.e., inter-stroke) time of these two techniques. Over twenty fifteen-phrase sessions, performance increased from 4.0 wpm to 11.4 wpm for Graffiti and from 4.1 wpm to 15.8 wpm for Unistrokes. Correction rates were high for both techniques. However, rates for Graffiti remained relatively consistent at 26%, while those for Unistrokes decreased from 43% to 16%.

We describe an experiment to test the hypothesis that Fitts' throughput is independent of the speed-accuracy tradeoff. Eighteen participants used a mouse in performing a total of 5,400 target selection trials. Comparing nominal, speed-emphasis, and accuracy-emphasis conditions, significant main effects were found on movement time (ms) and error rate (%), but not on throughput (bits/s). In the latter case, failure to reject the null hypothesis of "no significant difference" (i.e., .05 < p < 1) is viewed as evidence supporting the constant-throughput hypothesis.

For decades, Fitts' law (1954) has been used to model pointing time in user interfaces. As with any rapid motor act, faster pointing movements result in increased errors. But although prior work has examined accuracy as the "spread of hits," no work has formulated a predictive model for error rates (0-100%) based on Fitts' law parameters. We show that Fitts' law mathematically implies a predictive error rate model, which we derive. We then describe an experiment in which target size, target distance, and movement time are manipulated. Our results show a strong model fit: a regression analysis of observed vs. predicted error rates yields a correlation of R2=.959 for N=90 points. Furthermore, we show that the effect on error rate of target size (W) is greater than that of target distance (A), indicating a departure from Fitts' law, which maintains that W and A contribute proportionally to index of difficulty (ID). Our error model can be used with Fitts' law to estimate and predict error rates along with speeds, providing a framework for unifying this dichotomy.

Two eye typing techniques and a fixation algorithm are described. Similar to word prediction, letter prediction chooses three highly probable next letters and highlights them on an onscreen keyboard. Letter prediction proved promising, as it was as good as word prediction, and in some cases better. The fixation algorithm chooses which button to select for eye-over highlighting. It often chooses the desired button even if another button is closer to the fixation location. Error rates were reduced when using the fixation algorithm combined with letter prediction; however, the algorithm was sensitive to the correctness of the first several letters in a word.

Eye trackers have been used as pointing devices for a number of years. Due to inherent limitations in the accuracy of eye gaze, however, interaction is limited to objects spanning at least one degree of visual angle. Consequently, targets in gaze-based interfaces have sizes and layouts quite distant from natural settings. To accommodate accuracy constraints, we developed a multimodal pointing technique combining eye gaze and speech inputs. The technique was tested in a user study on pointing at multiple targets. Results suggest that in terms of a footprint-accuracy tradeoff, pointing performance is best (~93%) for targets subtending 0.85 degrees with 0.3-degree gaps between them. User performance is thus shown to approach the limit of practical pointing. Effectively, developing a user interface that supports hands-free interaction and has a design similar to today's common interfaces is feasible.

We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.

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.

A new technique to enter text using a mobile phone keypad is described. For text input, the traditional touchtone phone keypad is ambiguous because each key encodes three or four letters. Instead of using a stored dictionary to guess the intended word, our technique uses probabilities of letter sequences --- "prefixes" --- to guess the intended letter. Compared to dictionary-based methods, this technique, called LetterWise, takes significantly less memory and allows entry of non-dictionary words without switching to a special input mode. We conducted a longitudinal study to compare LetterWise to Multitap, the conventional text entry method for mobile phones. The experiment included 20 participants (10 LetterWise, 10 Multitap), and each entered phrases of text for 20 sessions of about 30 minutes each. Error rates were similar between the techniques; however, by the end of the experiment the mean entry speed was 36% faster with LetterWise than with Multitap.

An experiment with 12 participants tested text entry rates on two sizes of soft keyboards with either a Qwerty layout or a layout presenting a randomized letter arrangement after each tap. The randomized layout simulated the novice experience by requiring users to visually scan the layout for each tap to find the intended letter. Rates for the Qwerty layouts were about 20 wpm with no significant difference between the large and small size. Rates for both sizes of the randomized layouts were very low, about 5.4 wpm. This is the expected walk-up text entry rate with a soft keyboard bearing an unfamiliar layout. This empirical result allows us to reject a previous model of novice interaction that used Fitts' law for stylus movement and the Hick-Hyman law for visual scan time.

We present a model for predicting expert text entry rates for several input methods on a 12-key mobile phone keypad. The model includes a movement component based on Fitts' law and a linguistic component based on digraph, or letter-pair, probabilities. Predictions are provided for one-handed thumb and two-handed index finger input. For the traditional multi-press method or the lesser-used two-key method, predicted expert rates vary from about 21 to 27 words per minute (wpm). The relatively new T9 method works with a disambiguating algorithm and inputs each character with a single key press. Predicted expert rates vary from 41 wpm for one-handed thumb input to 46 wpm for two-handed index finger input. These figures are degraded somewhat depending on the user's strategy in coping with less-than-perfect disambiguation. Analyses of these strategies are presented.

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 for 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 difference 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 design and evaluation of a high performance soft keyboard for mobile systems are described. Using a model to predict the upper-bound text entry rate for soft keyboards, we designed a keyboard layout with a predicted upper-bound entry rate of 58.2 wpm. This is about 35% faster than the predicted rate for a QWERTY layout. We compared our design (OPTI) with a QWERTY layout in a longitudinal evaluation using five participants and 20 45-minute sessions of text entry. Average entry rates for OPT1 increased from 17.0 wpm initially to 44.3 wpm at session 20. The average rates exceeded those for the QWERTY layout after the 10 session (about 4 hours of practice). A regression equation (R = .997) in the form of the power-law of learning predicts that our upper-bound prediction would be reach at about session 50.

Text entry rates are explored for several variations of soft keyboards. We present a model to predict novice and expert entry rates and present an empirical test with 24 subjects. Six keyboards were examined: the Qwerty, ABC, Dvorak, Fitaly, JustType, and telephone. At 8-10 wpm, novice predictions are low for all layouts because the dominant factor is the visual scan time, rather than the movement time. Expert predictions are in the range of 22-56 wpm, although these were not tested empirically. In a quick, novice test with a representative phrase of text, subjects achieved rates of 20.2 wpm (Qwerty), 10.7 wpm (ABC), 8.5 wpm (Dvorak), 8.0 wpm (Fitaly), 7.0 wpm (JustType), and 8.0 wpm (telephone). The Qwerty rate of 20.2 wpm is consistent with observations in other studies. The relatively high rate for Qwerty suggests that there is skill transfer from users' familiarity with desktop computers to the stylus tapping task.

An experiment is described comparing two commercial handwriting recognizers with discrete hand-printed characters. Each recognizer was tested at two levels of constraint, one using lowercase letters (which were the only symbols included in the input text) and the other using both uppercase and lowercase letters. Two factors—recognizer and constraint—with two levels each, resulted in four test conditions. A total of 32 subjects performed text-entry tasks for each condition. Recognition accuracy differed significantly among conditions. Furthermore, the accuracy observed (87%–93%) was below the walk-up accuracy claimed by the developers of the recognizers. Entry speed was affected not only by recognition conditions but by users' adaptation to the idiosyncrasies of the recognizers. In an extensive error analysis, numerous weaknesses of the recognizers are revealed, in that certain characters are error prone and are misrecognized in a predictable way. This analysis, and the procedure for such, is a useful tool for designers of handwriting-recognition systems. User satisfaction results showed that recognition accuracy greatly affects the impression of walk-up users.

Article A comparison of three selection techniques for touchpads Share on Authors: I. Scott MacKenzie Dept. of Computing & Information Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Dept. of Computing & Information Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1View Profile , Aleks Oniszczak Dept. of Computing & Information Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Dept. of Computing & Information Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1View Profile Authors Info & Claims CHI '98: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsJanuary 1998 Pages 336–343https://doi.org/10.1145/274644.274691Online:01 January 1998Publication History 55citation1,859DownloadsMetricsTotal Citations55Total Downloads1,859Last 12 Months197Last 6 weeks29 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Recent work in computer input control has sought to maximize 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 working 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.

"Half-QWERTY" is a new, one-handed typing technique designed to facilitate the transfer of two-handed touch-typing skill to the one-handed condition. It is performed on a standard keyboard with modified software or on a special half-keyboard with full-size keys. In an experiment using touch typists, hunt-and-peck typing speeds were surpassed after 3 to 4 hr of practice. Subjects reached 50% of their two-handed typing speed after about 8 hr. After 10 hr, all subjects typed between 41% and 73% of their two-handed speed, ranging from 23.8 to 42.8 words per minute (wpm). In extended testing, subjects achieved average one-handed speeds as high as 60 wpm and 83% of their two-handed rate. These results are important for providing access to disabled users and for designing compact computers.

A theoretical model is presented to predict upper-and lower-bound text-entry rates using a stylus to tap on a soft QWERTY keyboard. The model is based on the Hick-Hyman law for choice reaction time, Fitts law for rapid aimed movements, and linguistic tables for the relative frequencies of letter-pairs, or digrams, in common English. The model's importance lies not only in the predictions provided, but in its characterization of text-entry tasks using keyboards. Whereas previous studies only use frequency probabilities of the 26 × 26 digrams in the Roman alphabet, our model accommodates the space har—the most common character in typing tasks. Using a very large linguistic table that decomposes digrams by position-within-words, we established start-of-word (space-letter) and end-of-word (letter-space) probabilities and worked from a 27 × 27 digram table. The model predicts a typing rate of 8.9wpm for novices unfamiliar with the QWERTY keyboard, and 30.1wpm for experts. Comparisons are drawn with empirical studies using a stylus and other forms of text entry.

Human performance comparisons on interactive systems were drawn between output displays (CRT and LCD) across settings of control-display gain. Empirical evidence was sought in light of the common feeling in the user community that motor-sensory tasks are more difficult on a system equipped with an LCD display vs. a CRT display. In a routine target acquisition task using a mouse, movement times were 34% longer and motor-sensory bandwidth was 25% less when the output display was an LCD vs. a CRT. No significant difference in error rates was found. Control-display (C-D) gain was tested as a possible confounding factor; however, no interaction effect was found. There was a significant, opposing main effect for C-D gain on movement lime and error rates, illustrating the difficulty in optimizing C-D gain on the basis of movement time alone.

An experiment is described which demonstrates that the point-drag sequence common on interactive systems can be modelled as two separate Fitts law tasks — a point-select task followed by a drag-select task. Strong prediction models were built; however, comparisons with previous models were not as close as the standard error coefficients implied. Caution is therefore warranted in follow-up applications of models built in research settings. Additionally, the previous claim that target height is the appropriate substitute for target width in calculating Fitts' index of difficulty in dragging tasks was not supported. The experiment described varied the dragging target's width and height independently. Models using the horizontal width of the drag target or the smaller of the target's width or height outperformed the target height model.

Fitts' law, a one-dimensional model of human movement, is commonly applied to two-dimensional target acquisition tasks on interactive computing systems. For rectangular targets, such as words, it is demonstrated that the model can break down and yield unrealistically low (even negative!) ratings for a task's index of difficulty (ID). The Shannon formulation is shown to partially correct this problem, since ID is always ≥ 0 bits. As well, two alternative interpretations “target width” are introduced that accommodate the two-dimensional nature of tasks. Results of an experiment are presented that show a significant improvement in the model's performance using the suggested changes.

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.

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 pmeessing than the mouse during pointing but not during dragging.The trackball ranked third for both tasks,