Effects of age and gender on remote pointing performance and their design implications

The purpose of this research is to investigate the effects of age and sex on remote pointing movements. In addition, an attempt was made to incorporate possible age-related or sex differences into the design of a remote pointing user interface. The subjects were recruited from three age groups (elderly, middle-aged, and young) with equal number of both sexes. The participants were required to perform cursor positioning tasks using a remote pointing device. Their static hand stability and remote positioning time were recorded and analyzed. The remote positioning time was further separated into two components: initial submovement duration and adjustment submovement duration. The results reveal that age-related effects reduced the subjects' ability to perform remote pointing tasks and also maintained hand stability. However, sex differences had no significant effect on either performance. Moreover, the results also reveal that remote positioning movements for the young group were mostly completed in their initial submovement phase, while the elderly subjects spent most of their movement time on the fine adjustment phase. In light of the fact that different age groups exhibit different kinds of movement behavior patterns, suggestions for the design of signal sensitivity, target features, and display/control gain in remote pointing user interface were outlined.

Relevance to industry

Investigations on the variation in physical and psychomotor capabilities between the sexes and between different age groups which affect remote pointing performance will facilitate the design of remote pointing user interface. This study investigated the effects of age on remote pointing actions and outlined some suggestions for the design of remote pointing user interface.     

Gaze–mouse coordinated movements and dependency with coordination demands in tracing

Eye movements have been shown to lead hand movements in tracing tasks where subjects have to move their fingers along a predefined trace. The question remained, whether the leading relationship was similar when tracing with a pointing device, such as a mouse; more importantly, whether tasks that required more or less gaze–mouse coordination would introduce variation in this pattern of behaviour, in terms of both spatial and temporal leading of gaze position to mouse movement. A three-level gaze–mouse coordination demand paradigm was developed to address these questions. A substantial dataset of 1350 trials was collected and analysed. The linear correlation of gaze–mouse movements, the statistical distribution of the lead time, as well as the lead distance between gaze and mouse cursor positions were all considered, and we proposed a new method to quantify lead time in gaze–mouse coordination. The results supported and extended previous empirical findings that gaze often led mouse movements. We found that the gaze–mouse coordination demands of the task were positively correlated to the gaze lead, both spatially and temporally. However, the mouse movements were synchronised with or led gaze in the simple straight line condition, which demanded the least gaze–mouse coordination.     

Gender Differences in Mouse and Cursor Movements

Computer cursor and mouse activities such as moving, pointing, selecting, and dragging are essential parts of everyday interactions. Yet it is unknown how men and women differ in the way they move computer cursors. This study examines gender differences in movements of computer cursors. In one experiment, the authors measured trajectories of computer cursors every 20 ms in a simple choice-reaching task and tested the extent to which movement features related to controlling and targeting diverge between male and female participants. Results showed significant gender differences in cursor motions. Female participants deviated from the straight path toward the target location to a larger degree than did male participants, and female participants showed more backward motions (deviating backward from the target location) than did male participants. Implications for sources of these gender differences, user interface and input device design, and musculoskeletal disorders in women are also discussed.     

Effects of computer mouse design and task on carpal tunnel pressure

Computer mouse use has become an integral part of office work in the past decade. Intensive mouse use has been associated with increased risk of upper extremity musculoskeletal disorders, including carpal tunnel syndrome. Sustained, elevated fluid pressure in the carpal tunnel may play a role in the pathophysiology of carpal tunnel syndrome. Carpal tunnel pressure was measured in 14 healthy individuals while they performed tasks using three different computer mice. Participants performed a multidirectional dragging (‘drag and drop’) task starting with the hand resting (static posture) on the mouse. With one mouse, an additional pointing (‘point-and-click’) task was performed. All mice were associated with similar wrist extension postures (p= 0.41) and carpal tunnel pressures (p= 0.48). Pressures were significantly greater during dragging and pointing tasks than when resting the hand (static posture) on the mouse (p= 0.003). The mean pressures during the dragging tasks were 28.8- 33.1 mmHg, ～ 12 mmHg greater than the static postures. Pressures during the dragging task were higher than the pointing task (33.1 versus 28.0 mmHg), although the difference was borderline non-significant (p= 0.06). In many participants the carpal tunnel pressures measured during mouse use were greater than pressures known to alter nerve function and structure, indicating that jobs with long periods of intensive mouse use may be at an increased risk of median mononeuropathy. A recommendation is made to minimize wrist extension, minimize prolonged dragging tasks and frequently perform other tasks with the mousing hand.     

Analysis of cursor movements with a mouse

With a mouse input device, 32 experienced computer users moved a cursor from a starting position to a target, both of which were displayed simultaneously on the computer’s screen. Cursor movements occurred under conditions of variations in the angle of approach to the target, the target size and shape, the distance to the target, and the nature of the task, drag-drop or point-click. In a fully crossed within-subjects design, all variables studied significantly affected movement time. Fitts’ law accounted for 44% or 97% of the variance in movement time, depending on the method of analysis. Fitts’ law was not equally effective under all combinations of the variables studied. An analysis of residuals showed that residuals were smaller for a point-click task in comparison to a drag-drop task, and residuals were lowest for the largest target displayed at the shortest distance from the starting position. The application of Fitts’ law to cursor movements with a mouse should be qualified by noting the conditions under which the movements were observed.     

A kinematic analysis of directional effects on mouse control

The directional effects associated with cursor movement controlled by a computer mouse have long been studied to improve mouse performance during precise tasks. However, those studies have rarely considered the kinematic variables associated with directional effects and have only analysed the projection of trajectories along the main axes of movement, eventually reducing the original dimensions of the data. In addition, as the angle of approach has a limited number of levels, it has been difficult to observe singular behaviour in the horizontal directions. In this study, we investigated the directional effects on kinematic variables when using a mouse to select circular targets. In this experiment, the measured trajectory of 16 different angles of approach was measured after separating the x and y components. The results revealed interesting biomechanical and cognitive features of mouse control and led to the suggestion of two improvements to be made upon the typical mouse design.     

A method for evaluating head-controlled computer input devices using Fitts' law

The discrete movement task employed in this study consisted of moving a cursor from the center of a computer display screen to circular targets located 24.4 and 110.9 mm in eight radial directions. The target diameters were 2.7, 8.1, and 24.2 mm. Performance measures included movement time, cursor path distance, and root-mean-square cursor deviation. Ten subjects with no movement disabilities were studied using a conventional mouse and a lightweight ultrasonic head-controlled computer input pointing device. Average movement time was 306 ms greater (63%) for the head-controlled pointer than for the mouse. The effect of direction on movement time for the mouse was relatively small compared with the head-controlled pointer, which was lowest at 90 and 270 deg, corresponding to head extension and head flexion, respectively. Average path distance and root mean square displacement was lowest at off-diagonal directions (0, 90, 180, and 270 deg). This methodology was also shown to be useful for evaluating performance using an alternative head-controlled input device for two subjects having cerebral palsy, and measured subtle performance improvements after providing a disabled subject with lateral torso support.     

Extended Fitts' law for 3D pointing tasks using 3D target arrangements

This study explored an extended 3D Fitts' model, which was more appropriate than the original Fitts' model for pointing tasks in 3D environment. The inclination angle and azimuth angle for spherical coordinate system were added to Fitts' original model formulation. Experiments were conducted by manipulating the distance to the target, the size of target, and the 3D target arrangement, which were described using the two angles of inclination (θ1) and azimuth (θ2). Given the starting point as the center of the coordinates, θ1 was the angle between the positive y-axis and the target location, while θ2 was the angle between the positive x-axis and the projected target location on the x–z plane. All four variables were found to be significant for the movement time (MT) (p < 0.0001). After incorporating the two variables, θ1 and θ2, into the original Fitts' model, the extended Fitts' model with 3D target arrangements for spherical coordinate system showed better agreement with the empirical data than previous models in terms of the correlation coefficient and the standard error of the residuals for the measured and predicted MTs.Relevance to industryThis study presents an extended Fitts' model with a higher degree of predictability than previous studies for pointing task in three-dimensional space. In many situations, people implement pointing tasks in a three-dimensional environment, so it is important for designers to predict human performance accurately. Instead of using Euclidean coordinate system, spherical coordinate system can be also used for 3D pointing tasks. The extended model with spherical coordinate system can be used during the design and evaluation stage of the development process to help designers and developers.     

Forward/up directional incompatibilities during cursor placement within graphical user interfaces

Within graphical user interfaces, an indirect relationship between display and control may lead to directional incompatibilities when a forward mouse movement codes upward cursor motions. However, this should not occur for left/right movements or direct cursor controllers (e.g. touch sensitive screens). In a four-choice reaction time task, 12 participants performed movements from a central start location to a target situated at one of four cardinal points (top, bottom, left, right). A 2 × 2 × 2 design varied directness of controller (moving cursor on computer screen or pen on graphics tablet), compatibility of orientation of cursor controller with screen (horizontal or vertical) and axis of desired cursor motion (left/right or up/down). Incompatibility between orientation of controller and motion of cursor did not affect response latencies, possibly because both forward and upward movements are away from the midline and go up the visual field. However, directional incompatibilities between display and controller led to slower movement with prolonged accelerative phases. Indirect relationships between display and control led to less efficient movements with prolonged decelerative phases and a tendency to undershoot movements along the bottom/top axis. More direct cursor control devices, such as touch sensitive screens, should enhance the efficiency of aspects of cursor trajectories.     

Forward/up directional incompatibilities during cursor placement within graphical user interfaces

Within graphical user interfaces, an indirect relationship between display and control may lead to directional incompatibilities when a forward mouse movement codes upward cursor motions. However, this should not occur for left/right movements or direct cursor controllers (e.g. touch sensitive screens). In a four-choice reaction time task, 12 participants performed movements from a central start location to a target situated at one of four cardinal points (top, bottom, left, right). A 2 x 2 x 2 design varied directness of controller (moving cursor on computer screen or pen on graphics tablet), compatibility of orientation of cursor controller with screen (horizontal or vertical) and axis of desired cursor motion (left/right or up/down). Incompatibility between orientation of controller and motion of cursor did not affect response latencies, possibly because both forward and upward movements are away from the midline and go up the visual field. However, directional incompatibilities between display and controller led to slower movement with prolonged accelerative phases. Indirect relationships between display and control led to less efficient movements with prolonged decelerative phases and a tendency to undershoot movements along the bottom/top axis. More direct cursor control devices, such as touch sensitive screens, should enhance the efficiency of aspects of cursor trajectories.     

Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning

This paper proposes a learning strategy for robots with flexible joints having multi-degrees of freedom in order to achieve dynamic motion tasks. In spite of there being several potential benefits of flexible-joint robots such as exploitation of intrinsic dynamics and passive adaptation to environmental changes with mechanical compliance, controlling such robots is challenging because of increased complexity of their dynamics. To achieve dynamic movements, we introduce a two-phase learning framework of the body dynamics of the robot using a recurrent neural network motivated by a deep learning strategy. The proposed methodology comprises a pre-training phase with motor babbling and a fine-tuning phase with additional learning of the target tasks. In the pre-training phase, we consider active and passive exploratory motions for efficient acquisition of body dynamics. In the fine-tuning phase, the learned body dynamics are adjusted for specific tasks. We demonstrate the effectiveness of the proposed methodology in achieving dynamic tasks involving constrained movement requiring interactions with the environment on a simulated robot model and an actual PR2 robot both of which have a compliantly actuated seven degree-of-freedom arm. The results illustrate a reduction in the required number of training iterations for task learning and generalization capabilities for untrained situations.     

A two visual systems approach to understanding voice and gestural interaction

It is important to consider the physiological and behavioral mechanisms that allow users to physically interact with virtual environments. Inspired by a neuroanatomical model of perception and action known as the two visual systems hypothesis, we conducted a study with two controlled experiments to compare four different kinds of spatial interaction: (1) voice-based input, (2) pointing with a visual cursor, (3) pointing without a visual cursor, and (4) pointing with a time-lagged visual cursor. Consistent with the two visual systems hypothesis, we found that voice-based input and pointing with a cursor were less robust to a display illusion known as the induced Roelofs effect than pointing without a cursor or even pointing with a lagged cursor. The implications of these findings are discussed, with an emphasis on how the two visual systems model can be used to understand the basis for voice and gestural interactions that support spatial target selection in large screen and immersive environments.     

Examining the costs of multiple trajectory pointing techniques

Pointing techniques that offer users multiple trajectories to a target have the potential to reduce pointing time by allowing a shorter than normal movement distance. However, such techniques potentially introduce additional elements into the pointing task involving identification of the alternative trajectories, assessment of their relative performance, and selection of the one to use. These additional tasks may reduce or negate the benefits of offering shorter paths. To better understand these issues, we developed a methodology for controlling the relative benefits of alternative target trajectories, and used it to evaluate three interfaces—a ‘pointer wrapping’ technique that allows the cursor to traverse from one screen edge to the opposing edge, and a system allowing users to choose between multiple cursors in two configurations (‘Ninja cursors’). We found that performance with these techniques was significantly worse than that predicted by Fitts' law for a single cursor, suggesting that the additional elements in their use are significant. Detailed analysis of behaviour during acquisitions showed that much of this cost is accrued in the mental preparation that precedes motor action, and in additional volatility in the pointing movements. We discuss how the method and findings may assist those developing enhanced pointing techniques.     

The Impact of Control-Display Gain on User Performance in Pointing Tasks

ABSTRACT

We theoretically and empirically examine the impact of control display (CD) gain on mouse pointing performance. Two techniques for modifying CD gain are considered: constant gain (CG) where CD gain is uniformly adjusted by a constant multiplier, and pointer acceleration (PA) where CD gain is adjusted using a nonuniform function depending on movement characteristics. Both CG and PA are evaluated at various levels of relationship between mouse and cursor movement: from low levels, which have a near one-to-one mapping, through to high levels that aggressively amplify mouse movement. We further derive a model predicting the modification in motor-space caused by pointer acceleration. Experiments are then conducted on a standard desktop display and on a very large high-resolution display, allowing us to measure performance in high index of difficulty tasks where the effect of clutching may be pronounced. The evaluation apparatus was designed to minimize device quantization effects and used accurate 3D motion tracking equipment to analyze users' limb movements.

On both displays, and in both gain techniques, we found that low levels of CD gain had a marked negative effect on performance, largely because of increased clutching and maximum limb speeds. High gain levels had relatively little impact on performance, with only a slight increase in time when selecting very small targets at high levels of constant gain. On the standard desktop display, pointer acceleration resulted in 3.3% faster pointing than constant gain and up to 5.6% faster with small targets. This supported the theoretical prediction of motor-space modification but fell short of the theoretical potential, possibly because PA caused an increase in target overshooting. Both techniques were accurately modeled by Fitts' law in all gain settings except for when there was a significant amount of clutching. From our results, we derive a usable range of CD gain settings between thresholds of speed and accuracy given the capabilities of a pointing device, display, and the expected range of target widths and distances.     

Advanced modeling of selection and steering data: beyond Fitts' law

Human performance in selection tasks is frequently described by Fitts' law, which states that the average time needed to move to a target and select it is linearly related to the index of difficulty for the task, which is the logarithm of the task characteristic C=A/W, where A is the target distance and W is the target width. The coefficients in this linear relationship can vary across interaction conditions, such as when using distinct interaction devices, and can for instance be used to define throughput, which is a standardized measure to compare interaction conditions. Although Fitts' law has proven very useful in the field of human–computer interaction (HCI) over the past 50 years, there are several issues with Fitts' law that argue in favor of more advanced statistical modeling of experimental data. More specifically, we propose two generalizations of Fitts' law. The first generalization is not to limit the data analysis to average movement times, but to consider the distributions of the observed times instead. The second generalization is to extend Fitts' law to a more general relationship between task characteristics and index of difficulty and to use this generalized model to come up with additional measures that can be used alongside throughput.We use data from an experiment with both selection and tracing tasks to illustrate the proposed analysis method. The primary goal of the experiment is to compare the task performance in four experimental conditions, corresponding to all combinations of interaction device (mouse or pen) and target orientation (horzizontal/vertical versus oblique movements). First, we show that non-linear transformations on the measured task completion times are indeed advised to resolve problems with the normality and homoscedasticity of the data, especially in case of the tracing task. Second, we show that in case of the selection task the data supports a linear relationship between the logarithm of the task characteristic C and the logarithm of the movement time, which corresponds to a power-law between movement time and task characteristic, an alternative to Fitts' law that has previously been proposed by several authors. In case of the tracing task, the data supports a power-law function in between a linear and a logarithmic function. We conclude by demonstrating how multiple performance measures can be used simultaneously when comparing interaction conditions.     

Gain effects on performance using a head-controlled computer input device.

The purpose of this study was to use a Fitts' task to (1) determine how control-display gain influences performance using a head-controlled computer input device; (2) compare relative sensitivity to gain and optimal gain between head control and hand/arm control; and (3) investigate control-display gain interactions with other task factors including target width, movement amplitude and direction. The task was a discrete target acquisition task using circular targets of 2.9 mm, 8.1 mm, and 23.5 mm, movement amplitudes of 24.3 mm and 61.7 mm, and eight radial directions including 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees, and 315 degrees. Each device was operated at four gain levels. Ten subjects participated. The results indicated that gain had a significant effect on movement time for both types of pointing devices and exhibited local minimums. Discrete target acquisition at all gains was aptly described using Fitts' Law for both input devices. The mouse gain resulting in minimum movement time and RMS cursor deviation was between 1.0 and 2.0. The minimum movement time and RMS cursor deviation for the head-controlled pointer occurred at a gain between 0.3 and 0.6. Average movement time at the optimal head-controlled pointer gain had a slope of 169 ms/bit and was more than 76% greater than at the optimal mouse gain with a slope of 135 ms/bit. In addition, average RMS displacement was more than 27% greater for the head-controlled pointer at its optimal gain setting than for the mouse. Gain had the greatest effect for small target widths and long movement amplitudes using the head-controlled pointer. Average movement time increased 37% when increasing the head-controlled pointer gain from 0.6 to 1.2 for the small target width, but only increased 0.3% when increasing gain for the large target width. Average movement time also increased 12% when decreasing the head-controlled pointer gain from 0.3 to 0.15 for the long movement amplitude, but decreased 0.3% when decreasing gain for the short movement amplitude.     

Information society and information technology

To investigate the characteristics of a computer touchpad as a pointing device, 14 participants used their right hand to manipulate the touchpad on a laptop computer. They were required to move a cursor over different distances (7.5 cm, 15 cm) from a home location to targets of different diameters (8 mm, 16 mm), situated to the upper left, middle, or right of a computer screen. A kinematic analysis of movement onsets and cursor trajectories indicated the nature of inefficiencies of the touchpad compared with other devices, primarily excessive submovements. Upper leftward movements were poorer, which can be explained by asymmetries in the finger-wrist system. This result implies that screen accessibility can vary as a function of users' interaction with cursor controllers and that the default placements of key icons might need to vary as a consequence.     

Effects of Cursor Orientation and Required Precision on Positioning Movements on Computer Screens

Arrowhead cursors used within graphic-user interfaces include implicit directional cues that may not be compatible with desired axis of motion. In addition, arrowhead cursors may not afford the best cues for location, and such effects may be exacerbated when there is a greater need for precise cursor placement. To address the impact of the cursor's orientation on its positioning, 12 participants were required to move cursors (pointing arrows) leftward or rightward to targets (small, medium, or large) on a computer screen. Response latencies were influenced by compatibilities between movement direction and cursor shape but only when moderate levels of precision were required. Rightward movements were slower and less accurate, and movements were slower and less efficient when arrows pointed in compatible directions. Implicit directional cues only elicited compatibility effects with moderate precision requirements. Arrowhead cursors compatible with direction of motion led to slower cursor movements and less efficient cursor trajectories. Where response initiation is important, compatible arrowheads are beneficial, but if speed of cursor placement is an issue, an orientation neutral cursor with area effect might be preferable, or a more direct interface (e.g., touch-sensitive screen) might be more appropriate.     

人机交互中基于时间约束的人体工效模型

针对人机交互领域速度-准确度折中关系的预测中任务完成精确度的预测模型较为欠缺的问题,提出了一种基于时间约束的精确度模型预测方法。该方法采用了人机交互研究中常用的受控实验测试分析法,研究了在计算机用户界面中要求用户在给定的时间内完成任务时,任务完成的精确度与给定的时间约束之间的折中关系,用以衡量完成时间约束任务的人体工效。实验中设计了一系列受时间约束的轨道滑动任务,实验环境中自变量包括轨道长度、轨道宽度以及规定的在轨道中滑动的时间,因变量为任务完成的精确度,采用在轨道中滑动时轨迹的纵向偏差表示。通过对30位被试者实验数据的分析发现,任务完成的精确度与轨道宽度以及滑动速度(表示为轨道长度/规定的滑动时间)之间构成线性的关系,在此基础上采用最小二乘方回归法建立了一个基于时间约束的任务完成精确度的量化模型;该模型与真实实验数据集的拟合优度达到了0.857。     

A framework for development of fuzzy GOMS model for human‐computer interaction

The objective of this study was to examine usefulness of fuzzy methodologies in the analysis and design of human‐computer interaction. A framework for generalization of the Goals‐Operators‐Methods‐Selection Rules (GOMS) model, and its fuzzy version was proposed. An experimental verification of the fuzzy GOMS model was also provided. A total of six subjects participated in two laboratory experiments. These experiments were performed in order to validate the proposed fuzzy GOMS model for the text editing task described in information processing terms. The subjects were not familiar with the text files to be edited, and the task was performed from the subject's own office and desk. All subjects were familiar with and regularly used the VI screen editor. The experiments consisted of the following steps: (1) the subject performed a familiar text editing task using a screen editor (VI); (2) the methods by which the subject achieved his goals (word location) as well as selection rules were elicited; (3) several compatibility functions for fuzzy terms used by the subject were derived; and (4) once all the rules, methods, and corresponding membership functions have been elicited, the theory of possibility was used to model the expert's rule selection process. For this purpose, each of the potential rules was assigned a possibility measure equal to the membership value(s) derived during the elicitation phase of experiment Finally, the selected methods were compared to non‐fuzzy predictions and actual experimental data. It was shown that overall, across all subjects and trials of the main editing task, the fuzzy‐based COMS model predicted significantly more of the subject responses, than did the non‐fuzzy COMS model.