Target Selection Task Research Articles

Trajectory prediction model for crossing-based target selection

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some cases. Most of the research in target selection fields are focused on the analysis of the interaction results. Additionally, as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques, an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole process. Methods In this paper, a trajectory prediction model for crossing-based target selection tasks is proposed by taking the reference of a dynamic model theory. Results Simulation results demonstrate that our model performed well with regard to the prediction of trajectories, endpoints and hitting time for target-selection motion, and the average error of trajectories, endpoints and hitting time values were found to be 17.28%, 2.73mm and 11.50%, respectively.

Prediction of Optimal Facial Electromyographic Sensor Configurations for Human-Machine Interface Control.

Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.

There is no perfect evaluator: An investigation based on prospect theory

AbstractPsychophysical measurement has been widely used in various scientific and engineering fields. In the field of human–computer interaction (HCI), many prominent constructs, such as usability, affective satisfaction, user value, and user experience, have been assessed by psychophysical measurement, including Likert, Borg, and 0–100 scales. In spite of considerable empirical evidence that psychophysical measurement is reliable and valid, these measures also have fundamental limitations, such as distortion at both extremes. This study introduced prospect theory, which compensates for decision‐making behavior in uncertain situations, and applied the theory to the transformation of assessed data. In a typical usability assessment study with target selection tasks, this study revealed that psychophysically assessed data might improve reliability and validity. In addition, this study showed that people tend to regard around 75 as judgment criteria on a 0–100 scale. The results of this study can be used and applied to improve the reliability and validity of subjective measurements. This study suggested a method to improve psychological measurements based on prospect theory. The method is expected to be applied to practical engineering experiments, which has been widely conducted in the field of human factors and HCI.

Fitts’ Law: On Calculating Throughput and Non-ISO Tasks

We used a target-selection task to evaluate head-tracking as an input method for mobile devices. First, the method of calculating Fitts’ throughput is described by means of a raw data detailed example. Then, the method of calculating throughput is discussed for non-ISO tasks, since the procedure targets were randomly positioned from trial to trial. Due to a non-constant amplitude within each sequence of trials, throughput was calculated using two methods of data aggregation: the first one, by sequence of trials using the mean amplitude, and the second one, by common A-W conditions. For each data set, we used four methods for calculating throughput. The grand mean for throughput (calculated through the division of means and the adjustment for accuracy) was of 0.74 bps, which is 45 % lower than the value obtained using an ISO task. We recommend to calculate throughput using the division of means plus the adjustment for accuracy, and to avoid using the reciprocal slope of the regression model. We present various design recommendations for non-ISO tasks, such as: i) to keep amplitude and constant target within each sequence of trials, and ii) to use strategies to avoid or remove reaction time.
 Keywords: Fitts’ law, throughput, ISO 9241-411, mobile HCI, head-tracking.

Idiosyncratic representation of peripersonal space depends on the success of one's own motor actions, but also the successful actions of others!

Peripersonal space is a multisensory representation of the environment around the body in relation to the motor system, underlying the interactions with the physical and social world. Although changing body properties and social context have been shown to alter the functional processing of space, little is known about how changing the value of objects influences the representation of peripersonal space. In two experiments, we tested the effect of modifying the spatial distribution of reward-yielding targets on manual reaching actions and peripersonal space representation. Before and after performing a target-selection task consisting of manually selecting a set of targets on a touch-screen table, participants performed a two-alternative forced-choice reachability-judgment task. In the target-selection task, half of the targets were associated with a reward (change of colour from grey to green, providing 1 point), the other half being associated with no reward (change of colour from grey to red, providing no point). In Experiment 1, the target-selection task was performed individually with the aim of maximizing the point count, and the distribution of the reward-yielding targets was either 50%, 25% or 75% in the proximal and distal spaces. In Experiment 2, the target-selection task was performed in a social context involving cooperation between two participants to maximize the point count, and the distribution of the reward-yielding targets was 50% in the proximal and distal spaces. Results showed that changing the distribution of the reward-yielding targets or introducing the social context modified concurrently the amplitude of self-generated manual reaching actions and the representation of peripersonal space. Moreover, a decrease of the amplitude of manual reaching actions caused a reduction of peripersonal space when resulting from the distribution of reward-yielding targets, while this effect was not observed in a social interaction context. In that case, the decreased amplitude of manual reaching actions was accompanied by an increase of peripersonal space representation, which was not due to the mere presence of a confederate (control experiment). We conclude that reward-dependent modulation of objects values in the environment modifies the representation of peripersonal space, when resulting from either self-generated motor actions or observation of motor actions performed by a confederate.

Virtual reality sickness questionnaire (VRSQ): Motion sickness measurement index in a virtual reality environment

This study aims to develop a motion sickness measurement index in a virtual reality (VR) environment. The VR market is in an early stage of market formation and technological development, and thus, research on the side effects of VR devices such as simulator motion sickness is lacking. In this study, we used the simulator sickness questionnaire (SSQ), which has been traditionally used for simulator motion sickness measurement. To measure the motion sickness in a VR environment, 24 users performed target selection tasks using a VR device. The SSQ was administered immediately after each task, and the order of work was determined using the Latin square design. The existing SSQ was revised to develop a VR sickness questionnaire, which is used as the measurement index in a VR environment. In addition, the target selection method and button size were found to be significant factors that affect motion sickness in a VR environment. The results of this study are expected to be used for measuring and designing simulator sickness using VR devices in future studies.

Dissecting patterns of preparatory activity in the frontal eye fields during pursuit target selection.

We investigated the composition of preparatory activity of frontal eye field (FEF) neurons in monkeys performing a pursuit target selection task. In response to the orthogonal motion of a large and a small reward target, monkeys initiated pursuit biased toward the direction of large reward target motion. FEF neurons exhibited robust preparatory activity preceding movement initiation in this task. Preparatory activity consisted of two components, ramping activity that was constant across target selection conditions, and a flat offset in firing rates that signaled the target selection condition. Ramping activity accounted for 50% of the variance in the preparatory activity and was linked most strongly, on a trial-by-trial basis, to pursuit eye movement latency rather than to its direction or gain. The offset in firing rates that discriminated target selection conditions accounted for 25% of the variance in the preparatory activity and was commensurate with a winner-take-all representation, signaling the direction of large reward target motion rather than a representation that matched the parameters of the upcoming movement. These offer new insights into the role that the frontal eye fields play in target selection and pursuit control. They show that preparatory activity in the FEF signals more strongly when to move rather than where or how to move and suggest that structures outside the FEF augment its contributions to the target selection process.NEW & NOTEWORTHY We used the smooth eye movement pursuit system to link between patterns of preparatory activity in the frontal eye fields and movement during a target selection task. The dominant pattern was a ramping signal that did not discriminate between selection conditions and was linked, on trial-by-trial basis, to movement latency. A weaker pattern was composed of a constant signal that discriminated between selection conditions but was only weakly linked to the movement parameters.

A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection.

Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.

This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

An Empirical Study on the Interaction Capability of Arm Stretching

ABSTRACTBody-based motion gestures have been gaining popularity in designing interactive systems. However, theories and design guidelines on the use of body movement in design have not been fully evaluated. This article investigates human ability to perform discrete target selection tasks using stretching action through two controlled experiments. The experimental results indicate that: (1) the range of the discrete levels of depth that users can easily discriminate with arm stretching is up to 16 with full visual feedback, but is down to 4 without the feedback; (2) dwelling, a gesture with keeping a hand motionless and the cursor within a target area for a certain amount of time, may be the best gesture for confirmation command; (3) full visual feedback can improve the user performance; and (4) arm stretching action can be modeled using Fitts’ law. We also discuss the design potentials for Stretch Widgets based on these results.

Combined Use of Rear Touch Gestures and Facial Feature Detection to Achieve Single-Handed Navigation of Mobile Devices

To improve single-handed operation of mobile devices, rear touch panel operation to control commands and facial feature detection to control cursor position are proposed. Operational control is achieved through finger chord gestures on a rear touch panel, and nose movement is used to control cursor movement. Zooming is achieved by detecting the apparent distance between the left and right eyes in conjunction with a finger chord gesture. We have evaluated movement time, error rates, and the throughputs of these techniques in comparison with the conventional single-handed front touch panel thumb operations using Fitts's law. Experiments have been conducted to evaluate two operation modes, selection and zooming, in the form of reciprocal 1-D pointing tasks with 12 participants. For the target selection task, the proposed technique achieved 12% (0.26 s) shorter movement time and 4.7% smaller error rate than the conventional method on average. Especially, for long distance targets, the performance of the conventional method became remarkably inferior due to the limit of reach of the thumb, whereas the proposed technique achieved much less deterioration and obtained expected performance because the cursor could reach anywhere on the display. For the target size adjustment task, the proposed technique achieved 9% (0.22 s) shorter movement time than the conventional method and obtained a comparable error rate of less than 4%. Consequently, we could demonstrate the techniques that make single-handed select and zoom operations available anywhere on a large-sized tablet device with no blockage of the display.

Adaptive Control-Display Ratios for Smartphones

Smartphones pose new design challenges for precise interactions, prompting the development of indirect interaction techniques that improve performance by reducing the occlusion caused by touch input. Direct touch interaction (e.g., tap to select) is imprecise, due to occlusion and the finger’s surface area. Many cursor-based interaction techniques address this issue; however, these techniques do not dynamically adjust the control-to-display movement ratio ( CDratio ) to improve accuracy and interaction times. This paper analyzes the performance benefits of applying adaptive CDratio enhancements to smartphone interaction for target-selection tasks. Existing desktop computer enhancements and a new enhancement method, Magnetic Targets, are compared. Magnetic Targets resulted in significantly shorter target selection times compared to the existing enhancements. Further, a simple method that combined enhancements to provide a CDratio based on a greater context of the interactions demonstrated performance improvements.

Position stabilisation and lag reduction with Gaussian processes in sensor fusion system for user performance improvement

In this paper we present a novel Gaussian Process (GP) prior model-based sensor fusion approach to dealing with position uncertainty and lag in a system composed of an external position sensing device (Kinect) and inertial sensors embedded in a mobile device for user performance improvement. To test the approach, we conducted two experiments: (1) GPs sensor fusion simulation. Experimental results show that the novel GP sensor fusion helps improve the accuracy of position estimation, and reduce the lag (0.11 s). (2) User study on a trajectory-based target acquisition task in a spatially aware display application. We implemented the real-time sensor fusion system by augmenting the Kinect with a Nokia N9. In the trajectory-based interaction experiment, each user performed target selection tasks following a trajectory in (a) the Kinect system and (b) the sensor fusion system. In comparison with the Kinect time-delay system, our system enables the user to perform the task easier and faster. The MSE of target selection was reduced by 38.3 % and the average task completion time was reduced by 26.7 %.

Touch Behavior Analysis for Large Screen Smartphones

As phone touch screen becomes larger and larger, for single hand holding posture, the user interaction can be extremely hard if icon do not be placed into proper touch area. So it is necessary to know the specific suitable touch zone on large phone screen. In this paper we performed a target selection task on a large screen smartphone and analyzed the task performance under different variable conditions. Response time, hit rate and hit offset differences between gender and target location and the possible reasons for such difference are discussed. Design recommendation for button and icon location are also been discussed based on the experiment result.

Enhanced frontier-based exploration for indoor environment with multiple robots

In this paper, the exploration and map-building of unknown environment by a team of mobile robots is intensively investigated. A new exploration technique is proposed to increase the exploration efficiency. In particular, the new technique has two main objectives: firstly, it aims at reducing the exploration time and the traveled distance by reducing the overlap which takes place when a certain area in the environment is explored by more than one robot. To achieve this, a new procedure to assign the next target location for each individual robot is proposed. And secondly, it aims at reducing computations complexity required by target selection and path planning tasks. More importantly, the proposed technique obviates the need for environment segmentation complex procedures which is adopted in some previous important research works. The new technique is intensively tested with different environments. The results showed the effectiveness of the proposed technique.

Comparison of Vertical and Horizontal Eye Movement Times in the Selection of Visual Targets by an Eye Input Device

Objective: The aim of this study is to investigate how well eye movement times in visual target selection tasks by an eye input device follows the typical Fitts Law and to compare vertical and horizontal eye movement times. Background: Typically manual pointing provides excellent fit to the Fitts Law model. However, when an eye input device is used for the visual target selection tasks, there were some debates on whether the eye movement times in can be described by the Fitts Law. More empirical studies should be added to resolve these debates. This study is an empirical study for resolving this debate. On the other hand, many researchers reported the direction of movement in typical manual pointing has some effects on the movement times. The other question in this study is whether the direction of eye movement also affects the eye movement times. Method: A cursor movement times in visual target selection tasks by both input devices were collected. The layout of visual targets was set up by two types. Cursor starting position for vertical movement times were in the top of the monitor and visual targets were located in the bottom, while cursor starting positions for horizontal movement times were in the right of the monitor and visual targets were located in the left. Results: Although eye movement time was described by the Fitts Law, the error rate was high and correlation was relatively low (R² = 0.80 for horizontal movements and R² = 0.66 for vertical movements), compared to those of manual movement. According to the movement direction, manual movement times were not significantly different, but eye movement times were significantly different. Conclusion: Eye movement times in the selection of visual targets by an eye-gaze input device could be described and predicted by the Fitts Law. Eye movement times were significantly different according to the direction of eye movement. Application: The results of this study might help to understand eye movement times in visual target selection tasks by the eye input devices.

PERFORMANCE EVALUATION FOR TOUCH-BASED INTERACTION OF OLDER ADULTS

Understanding target acquisition tasks is particularly important for older adults aged 65 years and older, as some may find touch-based interfaces more challenging due to age-related declines in motor skills. This work aims to develop novel performance evaluation measures that can assess touch-based target selection difficulties of older adults. In addition, this study intends to explore real life unconstrained target selection tasks trajectories that are collected for a longer period of time to develop such performance evaluation measures.

Effects of Force Feedback and Distractor Location on a CDTI Target Selection Task

New flight deck technologies need to be implemented in order to support the projected rises in traffic levels. Future cockpit displays of traffic information (CDTIs) shall accommodate the altered responsibilities of pilots by facilitating more efficient routes and minimizing conflicts. However, the unstable nature of the cockpit may present challenges when precise inputs are required. The present study investigated the effects of force feedback and distractors on point-and-click movement times in a CDTI environment. Participants performed target selection tasks with multiple levels of force feedback and distractor location. Results implied that force feedback failed to benefit movement times relative to the standard computer mouse. However, substantial interactions between distractor effects, force levels, and other target characteristics are explored.

Tablet Interaction Accuracy and Precision

Smart phone and tablet mobile devices have changed the way individuals interact with computers. Users can complete tasks efficiently “on the go” using a mobile device’s touch screen; therefore, it is important to investigate touch interaction as it pertains to a mobile (i.e., walking) user. A user evaluation was conducted using a tablet to present a target selection task within a map-based interface. Participants interacted with the mobile device while seated or while walking in an uncontrolled indoor environment. As expected, the mobile target selection error is significantly higher for the mobile users; thus requiring the targets’ effective widths to be significantly larger for mobile users. A primary contribution of this paper is a recommended target width model for mobile users. A further contribution finds that target selection offsets with tablets are consistent with those of mobile phones, in spite of differences in form factor and finger used.

A support vector machine to search for metal-poor galaxies

Abstract To develop a fast and reliable method for selecting metal-poor galaxies (MPGs), especially in large surveys and huge data bases, a support vector machine (svm) supervized learning algorithms is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey data release 9 provided by the Max Planck Institute and the Johns Hopkins University (http://www.sdss3.org/dr9/spectro/spectroaccess.php). A two-step approach is adopted: (i) the svm must be trained with a subset of objects that are known to be either MPGs or metal-rich galaxies (MRGs), treating the strong emission line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After training on a sample of star-forming galaxies, the remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs using a 10-fold cross-validation technique. For target selection, we have achieved an acquisition accuracy for MPGs of ∼96 and ∼95 per cent for an MPG threshold of 12 + log(O/H) = 8.00 and 12 + log(O/H) = 8.39, respectively. Running the code takes minutes in most cases under the matlab 2013a software environment. The code in the Letter is available on the web (http://fshi5388.blog.163.com). The svm method can easily be extended to any MPGs target selection task and can be regarded as an efficient classification method particularly suitable for modern large surveys.