Mental workload recognition is of great significance in preventing human errors and accidents. This study constructed a multimodal recognition scheme to recognize three mental workload states: underload, moderate load, and overload. Based on driving scenarios, these three states were induced in this study by changing the driving modes and situations. Multimodal recognition of underload, moderate load, and overload was performed using electroencephalography (EEG), electrocardiography (ECG), and pupillometry. In addition, various machine learning methods were used to evaluate the recognition performance of different feature combinations. The results showed that the random forest method, trained using spectral power, pupil diameter, and heart rate variability, achieved the highest recognition accuracy of 83.13% for the three mental workload states. This study provides valuable reference information for multimodal recognition of mental workload states.

Perspective-taking and attentional switching are some of the ergonomic challenges that existing teleoperation human-machine interface designs need to address. This study developed two gaze interaction methods, the Eye Stick and the Eye Click, which were based on the joystick metaphor and the navigation metaphor, respectively, to be used in exocentric perspective teleoperation scenarios. We conducted two user studies to test the task performance and the subjective experience of the gaze interaction methods in a virtual ground vehicle teleoperation task. The results showed that compared with a traditional joystick design, the Eye Stick led to a shorter driving distance and the Eye Click led to less task time, and the gaze interaction methods had performance advantages in more difficult mazes. After multiple task sessions, the participants reported that the gaze interaction methods and the traditional joystick were similar in terms of task workload, perceived learnability, and satisfaction; however, the perceived usability of the Eye Stick was not as good as the Eye Click and the traditional joystick. In conclusion, both the Eye Stick and the Eye Click are feasible and promising gaze interaction methods for teleoperation applications with task performance advantages; however, more research is needed to optimize their user experience design.

While AI has benefited humans, it may also harm humans if not appropriately developed. The priority of current HCI work should focus on transiting from conventional human interaction with non-AI computing systems to interaction with AI systems. We conducted a high-level literature review and a holistic analysis of current work in developing AI systems from an HCI perspective. Our review and analysis highlight the new changes introduced by AI technology and the new challenges that HCI professionals face when applying the human-centered AI (HCAI) approach in the development of AI systems. We also identified seven main issues in human interaction with AI systems, which HCI professionals did not encounter when developing non-AI computing systems. To further enable the implementation of the HCAI approach, we identified new HCI opportunities tied to specific HCAI-driven design goals to guide HCI professionals addressing these new issues. Finally, our assessment of current HCI methods shows the limitations of these methods in support of developing HCAI systems. We propose the alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI approach to the development of AI systems. We also offer strategic recommendation for HCI professionals to effectively influence the development of AI systems with the HCAI approach, eventually developing HCAI systems.

Previous studies have used the Fitts’ Law to predict the performance of pointing task on touch screens. It was found that moving distance, target width, and the direction of motion would affect the task performance. The present research aimed to investigate the impact of the initial and terminal positions (ITPs) on pointing performance in two Exps. ITPs were divided into two categories: center of a screen and outer of a screen (center ↔ outer) pointing, and outer of a screen to outer of a screen (outer→outer) pointing. In Exp 1, 30 participants performed the center ↔ outer pointing tasks with 8 directions. The results showed that the outer → center movement was significantly faster than the center → outer at 45°, 90°, and 180°. In Exp 2, 30 participants performed the outer → outer pointing tasks with eight directions. The current study revealed that the ITPs influenced the performance of the pointing task, possibly due to human biomechanical characteristics associated with different movements.