Physical loads on upper extremity muscles while interacting with virtual objects in an augmented reality context.

Augmented reality (AR) environments are emerging as prominent user interfaces and gathering significant attention. However, the associated physical strain on the users presents a considerable challenge. Within this background, this study explores the impact of movement distance (MD) and target-to-user distance (TTU) on the physical load during drag-and-drop (DND) tasks in an AR environment. To address this objective, a user experiment was conducted utilizing a 5× 5 within-subject design with MD (16, 32, 48, 64, and 80 cm) and TTU (40, 80, 120, 160, and 200 cm) as the variables. Physical load was assessed using normalized electromyography (NEMG) (%MVC) indicators of the upper extremity muscles and the physical item of NASA-Task load index (TLX). The results revealed significant variations in the physical load based on MD and TTU. Specifically, both the NEMG and subjective physical workload values increased with increasing MD. Moreover, NEMG increased with decreasing TTU, whereas the subjective physical workload scores increased with increasing TTU. Interaction effects of MD and TTU on NEMG were also significantly observed. These findings suggest that considering the MD and TTU when developing content for interacting with AR objects in AR environments could potentially alleviate user load.

An ergonomic evaluation using a deep learning approach for assessing postural risks in a virtual reality-based smart manufacturing context.

This study proposes an integrated ergonomic evaluation designed to identify unsafe postures, whereby postural risks during industrial work are assessed in the context of virtual reality-based smart manufacturing. Unsafe postures were recognised by identifying the displacements of the centre of mass (COM) of body keypoints using a computer vision-based deep learning (DL) convolutional neural network approach. The risk levels for the identified unsafe postures were calculated using ergonomic risk assessment tools rapid upper limb assessment and rapid whole-body assessment. An analysis of variance was conducted to determine significant differences between the vertical and horizontal directions of postural movements associated with the most unsafe postures. The findings assess the ergonomic risk levels and identify the most unsafe postures during industrial work in smart manufacturing using DL method. The identified postural risks can help industry managers and researchers acquire a better understanding of unsafe postures.

This study aims to identify unsafe postures and calculate risk levels in a VR-based smart manufacturing context. Deep learning is applied to identify unsafe postures by detecting COM displacements and risk levels are calculated using ergonomic risk assessment tools. Results revealed the most unsafe body postures, crucial for workers' safety.

Development and classification of autonomous vehicle's ambiguous driving scenario.

Human drivers are gradually being replaced by highly automated driving systems, and this trend is expected to persist. The response of autonomous vehicles to Ambiguous Driving Scenarios (ADS) is crucial for legal and safety reasons. Our research focuses on establishing a robust framework for developing ADS in autonomous vehicles and classifying them based on AV user perceptions. To achieve this, we conducted extensive literature reviews, in-depth interviews with industry experts, a comprehensive questionnaire survey, and factor analysis. We created 28 diverse ambiguous driving scenarios and examined 548 AV users' perspectives on moral, ethical, legal, utility, and safety aspects. Based on the results, we grouped ADS, with all of them having the highest user perception of safety. We classified these scenarios where autonomous vehicles yield to others as moral, bottleneck scenarios as ethical, cross-over scenarios as legal, and scenarios where vehicles come to a halt as utility-related. Additionally, this study is expected to make a valuable contribution to the field of self-driving cars by presenting new perspectives on policy and algorithm development, aiming to improve the safety and convenience of autonomous driving.

"Slow down. Rail crossing ahead. Look left and right at the crossing": In-vehicle auditory alerts improve driver behavior at rail crossings.

Even though the rail industry has made great strides in reducing accidents at crossings, train-vehicle collisions at Highway-Rail Grade Crossings (HRGCs) continue to be a major issue in the US and across the world. In this research, we conducted a driving simulator study (N = 35) to evaluate a hybrid in-vehicle auditory alert (IVAA), composed of both speech and non-speech components, that was selected after two rounds of subjective evaluation studies. Participants drove through a simulated scenario and reacted to HRGCs with and without the IVAA present and through different music conditions and crossing devices. Driver simulator testing results showed that the inclusion of the hybrid IVAA significantly improved driving behavior near HRGCs in terms of gaze behavior, braking reaction, and approach speed to the crossing. The driving simulator study also showed the effects of background music and warning device types on driving performance. The study contributes to the large-scale implementation of IVAAs at HRGCs, as well as the development of guidelines toward a more standardized approach for IVAAs at HRGCs.

Modeling takeover time based on non-driving-related task attributes in highly automated driving.

This study aims to investigate the effects of non-driving-related tasks (NDRTs) on the transition of control in highly automated driving (HAD) by investigating the effects of NDRT physical, visual, and cognitive attributes during transition of control. A conceptual model of the takeover process is proposed by dividing this process into motor and mental reactions. A laboratory experiment was conducted to evaluate the effects of each NDRT attribute on the corresponding stage of the process of taking over control. A prediction model was developed using the results of multiple linear regression analysis. Additionally, a validation experiment with nine NDRTs and a baseline condition was conducted to determine the extent to which the developed model explains the takeover time for each NDRT condition. The results showed that the timing aspects of the transition of control in HAD largely consist of participant motor reactions that are affected by the physical attributes of NDRTs.

Effects of visual complexity of in-vehicle information display: Age-related differences in visual search task in the driving context.

We aimed to investigate the effects of the visual complexity of in-vehicle information display and driver's age in a driving context. A driving simulator study was conducted where participants performed visual search tasks at different visual complexity levels while driving. Two groups were included, 20 younger drivers (mean age = 28.75 years) and 14 older drivers (mean age = 54.87 years). Older drivers were found to be more vulnerable to the effects of increased visual complexity when performing a visual search task. The task completion time of the younger group increased by about 20% (from 7.69 s to 9.30 s), while the older group increased by about 47% (from 8.92 s to 13.14 s). Further, the driving performance of the older group deteriorated, unlike the younger group. The subjective workload score supported the results of the objective performance measures. These differences can be explained by glance behavior. The total off-road glance duration of older drivers was longer than that of younger drivers, but the average off-road glance duration of younger drivers was longer. In other words, older drivers have a more conservative strategy when dealing with increased visual complexity in a driving context so as not to affect their driving. The findings of this study show that the visual complexity level has a significant effect on driving behaviors, especially in older drivers, which provides insights for designing in-vehicle information displays.

Smartphone form factors: Effects of width and bottom bezel on touch performance, workload, and physical demand.

This study aimed at investigating the effect of two smartphone form factors (width and bottom bezel) on touch behaviors with one-handed interaction. User experiments on tapping tasks were conducted for four widths (67, 70, 72, and 74 mm) and five bottom bezel levels (2.5, 5, 7.5, 10, and 12.5 mm). Task performance, electromyography, and subjective workload data were collected to examine the touch behavior. The success rate and task completion time were collected as task performance measures. The NASA-TLX method was used to observe the subjective workload. The electromyogram signals of two thumb muscles, namely the first dorsal interosseous and abductor pollicis brevis, were observed. The task performances deteriorated with increasing width level. The subjective workload and electromyography data showed similar patterns with the task performances. The task performances of the bottom bezel devices were analyzed by using three different evaluation criteria. The results from these criteria indicated that tasks became increasingly difficult as the bottom bezel level decreased. The results of this study provide insights into the optimal range of smartphone form factors for one-handed interaction, which could contribute to the design of new smartphones.