Influence of Task Combination on EEG Spectrum Modulation for Driver Workload Estimation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Objective: This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver’s mental workload index. Background: The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring. Methods: An experiment combining a lane-change task and n-back task was conducted. The task load levels were manipulated in two dimensions, driving task load and working memory load, with each containing three task load conditions. Results: The frontal theta activity showed significant increases in the working memory load dimension, but differences were not found with the driving task load dimension. However, significant decreases in parietal alpha activity were found when the task load was increased in both dimensions. Task-related differences were also found. The driving task load contributed more to the changes in alpha power, whereas the working memory load contributed more to the changes in theta power. Additionally, these two task load dimensions caused significant interactive effects on both theta and alpha power. Conclusion: These results indicate that EEG technology can provide sensitive information for driver workload detection even if the sensitivities of different EEG parameters tend to be task dependent. Application: One potential future application of this study is to establish a general driver workload estimator that uses EEG signals

Evaluation of Manual Skill Degradation Due to Automation in Apparel Manufacturing

Manual skill degradation is a common problem that production managers face in assembly lines due to frequent changes in batch styles. Since the advancement in automated machinery, reliance on manual machines has been reduced. However, due to the high cost of fully automated machinery, it is still not available on a large scale in apparel manufacturing setups as most of the setups are in developing countries. Few related studies regarding the effects of automation on manual skills have been conducted in aviation and other emerging technological advanced fields; little focus was given on the effects of automation in apparel manufacturing. This exploratory study examines automation-induced performance degradation in the apparel production line. Sixty-seven sewing machine operators were initially trained on manual sewing machines to learn a complex production operation. Then, participants were divided randomly into three groups to experience varied amount of automation exposure. The manual machine group (MMG)kept working on the manual machines after the initial training and skill development. In contrast, the automation group (AG) shifted to automated pocket setting machines after skill development. Finally, the refresher training group (RTG) rotated between manual and automated machines after the skill development. The skill retrieval assessment was carried out after six weeks in the production line. The result of an independent t-test showed no significant differences among performances of the three groups after the initial training stage. A significant increase in the average single cycle time (ASCT) and decrease in the right-first-time percent (RFT %) was found in the AG while the ASCT decreased and the RFT% increased among the MMG after the retention interval. The RTG almost maintained its production output and the ASCT due to refresher training drills. Relevance to industry: Production managers usually maintain a skill set among the operators to run the production line smoothly. Therefore, capacity development drills of sewing operators are essential to maintain an efficient required skill set.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of “Follow Me” robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Calibration-free gaze interfaces based on linear smooth pursuit

Since smooth pursuit eye movements can be used without calibration in spontaneous gaze interaction, the intuitiveness of the gaze interface design has been a topic of great interest in the human-computer interaction field. However, since most related research focuses on curved smooth-pursuit trajectories, the design issues of linear trajectories are poorly understood. Hence, this study evaluated the user performance of gaze interfaces based on linear smooth pursuit eye movements. We conducted an experiment to investigate how the number of objects (6, 8, 10, 12, or 15) and object moving speed (7.73 ˚/s vs. 12.89 ˚/s) affect the user performance in a gaze-based interface. Results show that the number and speed of the displayed objects influence users’ performance with the interface. The number of objects significantly affected the correct and false detection rates when selecting objects in the display. Participants’ performance was highest on interfaces containing 6 and 8 objects and decreased for interfaces with 10, 12, and 15 objects. Detection rates and orientation error were significantly influenced by the moving speed of displayed objects. Faster moving speed (12.89 ˚/s) resulted in higher detection rates and smaller orientation error compared to slower moving speeds (7.73 ˚/s). Our findings can help to enable a calibration-free accessible interaction with gaze interfaces.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli