International Journal of Human-Computer Studies最新文献

Mid-air interaction has been increasingly introduced for human-computer interaction (HCI) tasks in vibration environments, but it has seldom been assessed from ergonomic aspects, especially in comparison with device-assisted interactions. This study aimed to provide a comprehensive ergonomic assessment of mid-air interaction and device-assisted interactions under vibration environments based on task performance, muscle activity in the upper limb and shoulder, and user perceptions. A within-subjects design was implemented in this study, where participants were required to perform basic pointing and dragging tasks with four interaction modes (i.e., one mid-air interaction and three device-assisted interactions) under static, low and high vibration environments, respectively. Both small and large target sizes were examined. Muscle activity was recorded with surface electromyography for five muscles from participants’ dominant arm. Results showed that mid-air interaction yielded longer task completion time, more errors, higher perceived workload, lower usability ratings, and larger muscle activities in the forearm, upper arm and shoulder compared with device-assisted interactions. There were significant interaction effects between vibration and interaction mode. Specifically, compared with device-assisted interactions, mid-air interaction was associated with greater susceptibility to the detrimental effects of vibration (poorer task performance and larger muscle activities). Target size significantly affected task performance, but the effects varied by tasks. Overall, our results suggest that mid-air interaction presents a higher ergonomic risk compared with device-assisted interactions, especially in vibration environments. These findings provide implications for better use, configuration and ergonomic assessment of interaction tools in vibration environments, and are useful in developing evidence-based interventions to control ergonomic risk in HCI tasks in vibration environments.

Object manipulation is fundamental in virtual and augmented reality, where efficiency and accuracy are crucial. However, repetitive object manipulation tasks using the hands can lead to arm fatigue, and in some scenarios, hands may not be feasible for object manipulation. In this paper, we propose a novel approach for object manipulation based on head movement. Firstly, we introduce the concept of head manipulation space and conduct an experiment to collect head manipulation space data to determine the manipulable space. Then, we propose a new method for object manipulation based on head speed and inter-frame viewpoint quality to enhance the efficiency and accuracy of head manipulation. Finally, we design two user studies to evaluate the performance of our head-based object manipulation method. The results show that our method is feasible in terms of task completion efficiency and accuracy compared to state-of-the-art methods and greatly reduces user fatigue and motion sickness. Moreover, our method significantly improves usability and reduces task load. Our method lays a foundation for head-based object manipulation in virtual and augmented reality and provides a new manipulation method for scenarios where hands are not suitable for object manipulation.

With the era of automated driving approaching, designing an effective and suitable human–machine interface (HMI) to present takeover requests (TORs) is critical to ensure driving safety. The present study conducted a simulated driving experiment to explore the effects of three HMIs (instrument panel, head-up display [HUD], and peripheral HMI) on takeover performance, simultaneously considering the TOR type (informative and generic TORs). Drivers’ eye movement data were also collected to investigate how drivers distribute their attention between the HMI and surrounding environment during the takeover process. The results showed that using the peripheral HMI to present TORs can shorten takeover time, and drivers rated this HMI as more useful and satisfactory than conventional HMIs (instrument panel and HUD). Eye movement analysis revealed that the peripheral HMI encourages drivers to spend more time gazing at the road ahead and less time gazing at the TOR information than the instrument panel and HUD, indicating a better gaze pattern for traffic safety. The HUD seemed to have a risk of capturing drivers’ attention, which resulted in an ‘attention tunnel,’ compared to the instrument panel. In addition, informative TORs were associated with better takeover performance and prompted drivers to spend less time gazing at rear-view mirrors than generic TORs. The findings of the present study can provide insights into the design and implementation of in-vehicle HMIs to improve the driving safety of automated vehicles.

In digital control rooms, operators often need to monitor multiple information sources, which are spatially distributed on multiple screens, to manage multiple tasks simultaneously. Operators’ attention allocation between tasks are likely to be affected by the visual salience and eccentricity of secondary task screens. Their exact impacts and underlying mechanisms need to be investigated to inform system design to support balanced attention allocation strategies for optimal multitasking performance. This study aims to address these issues through a laboratory experiment using a simulated unmanned aerial vehicle monitoring platform. The participants performed a primary task and two secondary tasks concurrently, each shown on a separated screen. The visual salience and eccentricity of the maintenance task (one of the secondary tasks) were manipulated within-groups. Attention allocation behaviors were captured by eye movement data, and performance of both primary and secondary tasks were recorded. The participants were more likely to switch to and spent more time on the maintenance task of lower salience, showing a tendency to override the bottom-up influence of secondary task salience with top-down control. Large eccentricity of the secondary task, however, led to less attention allocation and lower task prioritization in paired event conflicts. The interaction effect of the salience and eccentricity of the maintenance task was significant for the accuracy of the primary task and the other secondary task. These findings offered insights into the joint design of salience and eccentricity of secondary tasks to enhance overall multitasking performance.

There are many design techniques to support the co-design of tangible technologies. However, few of these design methods allow the involvement of users at scale and across diverse geographic locations. While popular in psychology, the story completion method (SCM) has only recently started to be adopted within the HCI community. We explore whether SCM can generate meaningful design insights from large, diverse study populations for the design of Tangible User Interfaces (TUIs). Based on the results of two questionnaire studies using SCM, we conclude that the method can be used to generate meaningful design insights. Drawing on a systematic review of 870 TUI papers, we then contextualise the strengths and weaknesses of SCM against commonly used design methods, before reflecting on our experience of using the method across two distinct domains. We discuss the advantages of the method (particularly in terms of the scale and diversity of participation) and the challenges (particularly around constructing meaningful story stems, and developing the correct level of scaffolding to support creativity). We conclude that SCM is particularly suitable to be used in the early stages of the design process to understand the socio-cultural context of deployment.

The navigation of two-dimensional spaces by rhythmic patterns on two buttons is investigated. It is shown how direction and speed of a moving object can be controlled with discrete commands consisting of duplets or triplets of taps, whose rate is proportional to one of two orthogonal velocity components. The imparted commands generate polyrhythms and polytempi that can be used to monitor the object movement by perceptual streaming. Tacking back and forth must be used to make progress along certain directions, similarly to sailing a boat upwind. The proposed rhythmic velocity-control technique is tested with a target-following task. Users effectively learn the tapping control actions, and they can keep a relatively small distance from a moving target. They can potentially rely on overlapping auditory rhythmic streams to compensate for temporary deprivation of visual position of the controlled object. The interface is minimal and symmetric, and can be adapted to different sensing and display devices, exploiting the symmetry of the human body and the ability to follow two concurrent rhythmic streams.

This study introduces an empirical approach for assessing human scent-related experiences within the field of Human-Computer Interaction (HCI). We labeled 43 fragrances based on grounded collective experience, incorporating semantic and impression-based data. Furthermore, we collected comprehensive psychophysiological data, including electroencephalogram (EEG), electrobulbogram (EBG), electrocardiogram (ECG), and facial dynamics captured by a camera, from participants who experienced the scents. By computing scent-wise similarity and correlating both grounded and psychophysiological scent spaces, we identified associations between them, demonstrating the potential of this approach to enhance our understanding of scent-related experiences. Additionally, we propose an iterative evaluation framework to refine the design of smell-based interactions and we conduct a real-life study to validate this framework.

Fluency with computer applications has assumed a crucial role in work-related and other day-to-day activities. While prior experience is known to predict performance in tasks involving computers, the effects of more stable factors like cognitive abilities remain unclear. Here, we report findings from a controlled study ($N=88$) covering a wide spectrum of commonplace applications, from spreadsheets to video conferencing. Our main result is that cognitive abilities exert a significant, independent, and broad-based effect on computer users’ performance. In particular, users with high working memory, executive control, and perceptual reasoning ability complete tasks more quickly and with greater success while experiencing lower mental load. Remarkably, these effects are similar to or even larger in magnitude than the effects of prior experience in using computers and in completing tasks similar to those encountered in our study. However, the effects are varying and application-specific. We discuss the role that user interface design bears on decreasing ability-related differences, alongside benefits this could yield for functioning in society.

With the expansive growth of AI’s capabilities in recent years, researchers have been tasked with developing and improving human-centered AI collaborations, necessitating the creation of human–AI teams (HATs). However, the differences in communication styles between humans and AI often prevent human teammates from fully understanding the intent and needs of AI teammates. One core difference is that humans naturally leverage a positive emotional tone during communication to convey their confidence or lack thereof to convey doubt in their ability to complete a task. Yet, this communication strategy must be explicitly designed in order for an AI teammate to be human-centered. In this mixed-methods study, 45 participants completed a study examining how human teammates interpret the behaviors of their AI teammates when they express different positive emotions via specific words/phrases. Quantitative results show that, based on corresponding behaviors, AI teammates were able to use displays of emotion to increase trust in AI teammates and the positive mood of the human teammate. Additionally, our qualitative findings indicate that participants preferred their AI teammates to increase the intensity of their displayed emotions to help reduce the perceived risk of their AI teammate’s behavior. When taken in sum, these findings describe the relevance of AI teammates expressing intensities of emotion while performing various behavioral decisions as a continued means to provide social support to the wider team and better task performance.