This study investigated the potential of using virtual reality (VR) as a platform for early-stage design of upper-limb prostheses as well as evaluation with a focus on performance, cognitive workload and usability. Three prosthetic device control modes (Direct Control, DC; Pattern Recognition, PR; and Continuous Control, CC) were compared across physical device (PD) and VR settings. Results indicated that task performance was generally lower in VR than in PD for DC and CC modes, likely due to the reduction of haptic cues and stricter spatial-alignment requirements in the VR interaction setting. PR mode, however, showed consistent performance across settings, highlighting its resilience to sensory limitations in VR. Cognitive workload differed by mode, with DC showing reduced workload in VR due to visual task performance aids (e.g., automatic counting of successful clothespin relocations or door-handle turns), while the PR and CC modes produced higher perceived workload, likely due to the VR simulation control demands. Usability scores were consistent across settings and control modes, highlighting the reliability of VR as a platform for early-stage prosthetic evaluation. These findings highlight the potential of VR as a cost-effective, accessible platform to refine prosthetic control algorithms and facilitate user adaptation, while also emphasizing the need for enhancements, such as haptic feedback to improve VR applicability for advanced design and development.

Emergency responders face significant human factors and ergonomic (HF/E) challenges related to physical, cognitive, emotional, and training demands during high-stress situations. This study investigates these issues through a survey of 60 emergency responders, identifying key HF/E concerns such as fatigue, cognitive overload, and emotional stress. The research proposes innovative artificial intelligence and smart technology-driven solutions, including personalized protective equipment with exoskeleton, augmented reality tools for situational awareness, and virtual reality-based training simulations, to address these challenges. With statistical analysis results, the study emphasizes integrative approach via enhancing responder safety, efficiency, and mental well-being. The findings provide high priority HF/E solutions for advancing adaptive technologies and improving all-hazard responses, ultimately benefiting both responders and the communities they serve.

Determining when to move upper-limb prosthesis users from risk-free Virtual Reality (VR) drills to real-world or Augmented Reality (AR) tasks is often guesswork. We propose a data-driven rule that identifies the VR practice duration after which further workload reductions become negligible, signaling the optimal switch to AR. Modeling workload as a negatively accelerated learning curve with four interpretable parameters, a 1,000-run Monte Carlo simulation across clinically plausible ranges found optimal VR exposure clustered between 2-6 hours (median ≈ 4.8h). Continuing beyond this point cut workload by <2 NASA-TLX points but extended VR time by up to 50%, while switching earlier left users with ~15% higher workload entering AR. This patient-specific benchmark operationalizes therapists’ "train-until-plateau" intuition, supporting efficient, transparent scheduling and real-time adaptation, though empirical validation under non-ideal learning patterns is still needed.

ObjectiveTo evaluate the feasibility of electromyography (EMG)-based human-machine interfaces (HMIs) for high-demand activities such as driving based on performance, cognitive workload, usability, and safety measures.BackgroundUpper-limb amputees face challenges in performing everyday tasks, including driving. EMG-based HMIs offer potential solutions, particularly for wrist disarticulated and trans-radial amputee, but their effectiveness in complex tasks like driving requires further investigation.MethodNineteen able-bodied participants completed a driving simulation study using an EMG-based HMI, dominant hand, and both hands. Participants performed various driving maneuvers including straight lane driving, overtaking, and 90-degree turns at intersections. Driver performance, cognitive workload (measured by blink rate and subjective measures), usability (USE questionnaire), and safety were assessed.ResultsUsing the EMG-based HMI led to higher lane offset and steering angle compared to conventional methods, but demonstrated lower steering entropy in some situations. Cognitive workload was higher for EMG-based HMI, while usability scores were lower. Safety measures were mixed, with EMG-based HMI showing better performance at intersections but lower lane offset and steering angle safety scores overall.ConclusionThe study highlights both limitations and opportunities presented by EMG-based HMIs in high-demand tasks such as driving. While the system exhibited lower performance in some conditions, it demonstrated potential for controlled driving, particularly during specific maneuvers. The higher cognitive workload and lower usability scores indicate areas for improvement.ApplicationThe findings provide valuable insights for the development of more effective EMG-based HMIs, supporting future research and clinical trials aimed at enhancing mobility and independence for individuals with upper-limb amputations.

Limb amputation can lead to significant functional challenges in daily activities, prompting amputees to use prosthetic devices (PDs). However, the cognitive demands of PDs and usability issues have resulted in user rejections. This study aimed to create a Human Performance Model for Upper-Limb Prosthetic Devices (HPM-UP). The model used formulations of learnability, error rate, memory load, efficiency, and satisfaction to assess usability. The model was validated in an experiment with 30 healthy participants using a bypass prosthetic device. Findings indicated that the HPM-UP successfully predicted the usability of prosthetic devices, aligning with human subject data. This research proposes a quantitative approach to predict upper limb prosthetic device usability by quantifying each dimension and computationally connecting them. The model, available on Github and executable with Rstudio, could enable clinicians to assess and analyze the human performance of various commercial prostheses, aiding in recommending optimal devices for patients.

In this scoping review, the integration of artificial intelligence (AI) and smart technologies within Emergency Medical Services (EMS) is thoroughly examined as a strategy to overcome the inherent challenges faced by EMS personnel. These challenges encompass physical strain, cognitive overload, emotional stress, and issues with trainings. The paper emphasizes the critical role AI can play in resolving physical and cognitive demands, enhancing decision-making processes, optimizing resource allocation, and improving emergency response efficiency. The review identifies and categorizes the primary obstacles within EMS operations. It proposes innovative AI-driven solutions, including the use of exoskeletons for physical support, smart glasses for augmented cognitive assistance, AI systems dedicated to monitoring and supporting emotional health, and the application of virtual reality (VR) and augmented reality (AR) for advanced and realistic training scenarios. The findings suggest that such technological advancements can significantly elevate the operational capacity of EMS, ensuring a higher level of safety and efficiency, while also fostering a supportive environment for EMS personnel and enhancing patient care. This paper contributes a novel perspective to the literature by mapping out a future where AI and smart technologies play a pivotal role in transforming emergency medical services into more effective, resilient, and human-centered operations, ultimately advancing the field towards better preparedness and response capabilities in emergency situations. The potential solutions suggested in this study can be referred to be used for future research topics.

Augmented reality (AR) technology has shown great promise for its ability to seamlessly integrate into the real world and simulated environments. Commercially available headsets offer great functionality for tasks ranging from gaming to productivity. However, the main limitation that these headsets have is that they are bulky, cause visual strain, and being in mixed reality causes some to experience motion sickness-like symptoms. Therefore, we set an objective of this study as to design prototype AR glasses to detect physical workload in real time using a machine learning algorithm. The glasses are made from low-cost, commercially available components that emphasize ergonomics while maintaining functionality. Preliminary results from our pilot study shows that visual strain, bulkiness, and simulator sickness are all decreased when using the prototype AR glasses as compared to the Meta Quest Pro.

The aim of this study was to experimentally test the effects of different electromyographic-based prosthetic control modes on user task performance, cognitive workload, and perceived usability to inform further human-centered design and application of these prosthetic control interfaces. We recruited 30 able-bodied participants for a between-subjects comparison of three control modes: direct control (DC), pattern recognition (PR), and continuous control (CC). Multiple human-centered evaluations were used, including task performance, cognitive workload, and usability assessments. To ensure that the results were not task-dependent, this study used two different test tasks, including the clothespin relocation task and Southampton hand assessment procedure-door handle task. Results revealed performance with each control mode to vary among tasks. When the task had high-angle adjustment accuracy requirements, the PR control outperformed DC. For cognitive workload, the CC mode was superior to DC in reducing user load across tasks. Both CC and PR control appear to be effective alternatives to DC in terms of task performance and cognitive load. Furthermore, we observed that, when comparing control modes, multitask testing and multifaceted evaluations are critical to avoid task-induced or method-induced evaluation bias. Hence, future studies with larger samples and different designs will be needed to expand the understanding of prosthetic device features and workload relationships.

As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.

Cognitive performance models have been used in several human factors domains such as driving and human-computer interaction. However, most models are limited to expert performance with rough adjustments to consider novices despite prior studies suggesting novices' cognitive, perceptual, and motor behaviors are different from experts. The objective of this study was to develop a cognitive performance model for novice law enforcement officers (N-CPM) to model their performance and memory load while interacting with in-vehicle technology. The model was validated based on a ride-along study with 10 novice law enforcement officers (nLEOs). The findings suggested that there were no significant differences between the N-CPM and observation data in most cases, while the results of the benchmark model were different from that of N-CPM. The model can be applied to improve future nLEO's patrol mission performance through redesigning in-vehicle technologies and training methods to reduce their workload and driving distraction.

OBJECTIVE:This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology. BACKGROUND:HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored. METHOD:A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method. RESULTS:The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs. CONCLUSION:The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables. APPLICATION:The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

With over 2 million amputees in the U.S., they have been facing significant employment challenges. However, several physical prostheses still lack usability to be able for them to work in person. Therefore, this study explores an innovative application of digital twin approach, focusing on bidirectional interaction modeling and prototyping using convolutional neural networks (CNNs). We developed a simplified digital twin environment integrating electromyography (EMG) sensors and virtual reality (VR) to enable real-time interaction between the virtual and physical worlds. The CNN model, trained to classify hand movement from EMG data, achieved a test accuracy of 99%, demonstrating its effectiveness for practical applications. Our framework facilitates remote control of physical devices through VR gestures, potentially allowing amputees to perform meaningful work from home, thus overcoming physical limitations and fostering greater independence. This preliminary study underscores the possibility of digital twin technology to redefine workplace accessibility, offering amputees opportunities for potential employment.

Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models. Classification accuracy, the area under the receiver operation characteristic curve (AUC), precision, recall, and F1 scores were calculated to compare the models' performance. The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naive bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. The proposed algorithms can help manufacturers/clinicians predict the cognitive workload of future EMG-based prosthetic devices in early design phases.Practitioner summary: This study investigated the use of machine learning algorithms for classifying the cognitive workload of prosthetic devices. The findings suggested that the models could predict workload with high accuracy and low computational cost and could be used in assessing the usability of prosthetic devices in the early phases of the design process.

The integration of Human Factors and Ergonomics (HFE) into emergency responses is crucial due to frequent human errors and inadequate human-system interactions which impede effective emergency and disaster management. Therefore, this research aims to develop and validate the Smart All-hazards Response Framework (SARF) that systematically incorporates HFE principles across all aspects of disaster management, from planning through post-response analysis. The framework focuses on reducing human errors, enhancing human-system interactions, and boosting system resilience. The research methodology includes a detailed literature review, the development of the HFE-integrated framework, and rigorous testing through experimental studies and simulations across various scenarios. Key questions address the integration of HFE to optimize system performance and the impact of ergonomic interventions on responder safety in high-risk environments. By advancing a holistic, human-centered approach to emergency and disaster management, this study aims to significantly enhance the effectiveness, safety, and adaptability of responses to all kinds of hazards.

Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The objective of this study was to compare cognitive workload and usability of using virtual reality-based simulation of electromyography based prosthetic devices and physical devices. The findings suggested that using virtual reality simulations were helpful in reducing cognitive workload and increasing perceived usability of prosthetic devices.

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs' usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

The objective of this study was to assess the effects of unreliable automation, non-driving related tasks (NDRTs), and takeover time budget (TOTB) on drivers' takeover performance and cognitive workload when faced with critical incidents. Automated vehicles are expected to improve traffic safety. However, there are still some concerns about the effects of automation failures on driver performance and workload. Twenty-eight drivers participated in a driving simulation study. The findings suggested that drivers require at least 8 s of TOTB to safely take over the control of the vehicle. In addition, drivers exhibited safer takeover performance under the conditionally automated driving situation than negotiating the critical incident in the manual driving condition. The results of drivers' cognitive workload were inconclusive, which might be due to the individual and recall biases in subjective measures that could not capture subtle differences in workload during takeover requests.Practitioner Summary: A driving simulation study was conducted to assess the effect of unreliable automation, non-driving related tasks, and different takeover time budgets on drivers' performance and workload. The results can provide guidelines for vehicle manufacturers to improve the design of automated vehicles.

The acknowledgment in the publication is incorrect. The correct acknowledgment is given as follows:This work was supported in part by the National Research Foundation of Korea (Grant No. 2019R1A2C1088375), in part by the Technology Innovation Program funded by the Korean Government (MOTIE) (Grant No. 20008908), and in part by the National Natural Science Foundation of China (Grant No. 62073108).

Limb amputation can cause severe functional disability in performing activities of daily living (ADLs). Using prosthetic devices as aids for such activities requires substantial cognitive resources. Machine Learning (ML) algorithms can be used to predict cognitive workload (CW) of prosthetic device prototypes early in the design process and serve as a tool for improving device usability. The objective of this study was to explore subsets of input features that can be easily captured during early stages of the design cycle to classify CW of electromyography (EMG)-based upper-limb prostheses. An experiment was conducted with 30 participants to collect task performance and pupillometry data, and to provide a basis for generating cognitive performance model (CPM) outcomes. Three ML algorithms, including the random forest (RF), support vector machine (SVM), and naive Bayesian (NB) classifier were developed. The most important subset of features was selected based on classification accuracy and computational and experimental cost. Findings revealed that the CPM outcomes and prosthetic device configuration were the most important features for reasonably classifying CW responses under low cost. Also, the SVM classifier can be used for near-real time classification of CW. Future studies should include additional data and improve hyperparameter tuning parameters, as well as advanced CPM techniques to improve the performance of algorithms.

There is a large amount of variation between novices and experts in their cognitive workload when performing tasks. A naturalistic pilot study was conducted with nine novice law enforcement officers (nLEOs) to determine how their use of in-vehicle technology affected their cognitive workload during their normal patrols. Physiological data were collected using a novel synchronization process for naturalistic driving studies, allowing heart rate variability and eye tracking measurements to be synchronized together and directly compared to subjective workload levels. It was found that nLEOs have average or higher workload compared to experienced officers and the general population when they are on duty. Future studies can utilize the approaches and findings of this pilot study for conducting naturalistic driving studies and developing cognitive performance models for novice users.