Interactive effects of users' openness and robot reliability on trust: evidence from psychological intentions, task performance, visual behaviours, and cerebral activations.

Although trust plays a vital role in human-robot interaction, there is currently a dearth of literature examining the effect of users' openness personality on trust in actual interaction. This study aims to investigate the interaction effects of users' openness and robot reliability on trust. We designed a voice-based walking task and collected subjective trust ratings, task metrics, eye-tracking data, and fNIRS signals from users with different openness to unravel the psychological intentions, task performance, visual behaviours, and cerebral activations underlying trust. The results showed significant interaction effects. Users with low openness exhibited lower subjective trust, more fixations, and higher activation of rTPJ in the highly reliable condition than those with high openness. The results suggested that users with low openness might be more cautious and suspicious about the highly reliable robot and allocate more visual attention and neural processing to monitor and infer robot status than users with high openness.

The study could deepen practitioners' understanding of the effect of openness on trust in robots by examining the psychological intention, task performance, visual behaviours, and physiological activations. Moreover, the interaction effect could provide guidelines for designing robots adaptive to users' personalities, and the multimodal method would be practical for measuring trust in interaction.

Exploring the influence of anthropomorphic appearance on usage intention on online medical service robots (OMSRs): A neurophysiological study.

Online medical service robots (OMSRs) are becoming increasingly important in the medical industry, and their design has become a highly focused issue. This study investigated the neuroeconomics underlying the formation of usage intention, specifically evaluating the impact of anthropomorphic appearance and age on users' intentions to use OMSRs. Event-related potentials were used to analyze electroencephalography signals recorded from participants. This study found that OMSRs with a low anthropomorphic appearance induced larger P200 and P300 amplitudes, resulting in increased attentional resources compared to OMSRs with a moderate or high anthropomorphic appearance. OMSRs with moderate anthropomorphic appearances captured more attention and elicited larger P200 and P300 than those with high anthropomorphic appearances. Regarding age characteristics, OMSRs with senior features attracted more attention and induced larger P200 and P300 amplitudes. In terms of usage intention, compared to the others, users demonstrate a stronger usage intention towards the low anthropomorphism of OMSRs. Additionally, compared to the senior ones, users also exhibit a stronger usage intention toward a young appearance of OMSRs. These findings provide valuable insights for robot designers and practitioners to improve the appearance of OMSRs.

Recognition and evaluation of mental workload in different stages of perceptual and cognitive information processing using a multimodal approach.

This study explores the effects of different perceptual and cognitive information processing stages on mental workload by assessing multimodal indicators of mental workload such as the NASA-TLX, task performance, ERPs and eye movements. Repeated measures ANOVA of the data showed that among ERP indicators, P1, N1 and N2 amplitudes were sensitive to perceptual load (P-load), P3 amplitude was sensitive to P-load only in the prefrontal region during high cognitive load (C-load) states, and P3 amplitude in the occipital and parietal regions was sensitive to C-load. Among the eye movement indicators, blink frequency was sensitive to P-load in all C-load states, but to C-load in only low P-load states; pupil diameter and blink duration were sensitive to both P-load and C-load. Based on the above indicators, the k-nearest neighbours (KNN) algorithm was used to propose a classification method for the four different mental workload states with an accuracy of 97.89%.Practitioner summary: Based on the results of this study, it is possible to implement the monitoring of mental workload states and optimise brain task allocation in operations involving high mental workload, such as human-computer interaction.

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

BACKGROUND: Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. OBJECTIVE: To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. METHODS: Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. RESULTS: After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. CONCLUSION: The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.

A bibliometric analysis of occupational low back pain studies from 2000 to 2020.

This study aimed to explore the evolution tracks of occupational low back pain (OLBP) studies during 2000 ∼ 2020 through strict and systematic bibliometric analysis. The analysis began with data retrieval in Web of Science using defined search terms associated with OLBP. Bibliometric tools, including BibExcel and CiteSpace, were employed to conduct performance analysis and co-citation network analysis. Totally, 4,127 documents were identified from 2000 to 2020. The United States contributed the most publications. The leading journals mainly focused on medicine and ergonomics. The co-citation analysis illuminated the development of OLBP studies and the top three published keywords were musculoskeletal disorder, risk factors, and fear avoidance. This paper can help researchers have a broader and deeper understanding of OLBP studies, provide a general insight into aggregate performance in the OLBP field, and find further research directions.

Is an anthropomorphic app icon more attractive? Evidence from neuroergonomomics.

Exploring what types of app icons are attractive has been a topic of great interest in recent years. The main purpose of this study was to explore the neural mechanism of attention capturing of the anthropomorphic app icons based on neuroergonomics. Participants' perception of different app icons was investigated by using event-related potentials (ERPs) and attractiveness evaluation. The results showed that anthropomorphic app icons were evaluated more attractive and elicted larger P2, P3 and LPP amplitude than non-anthropomorphic app icons, which indicated an attention bias to attractive anthropomprphic app icons. The time course of the attention towards anthropomorphic app icons includes three main processes: an early stimulus-driven perceptual detection of app icon features (P2 during 160-200 ms), an involuntary allocation of attention to evaluate and categorize app icons (P3 during 300-500 ms), and experiencing different emotions to anthropomorphic versus non-anthropomorphic app icons (LPP during 500-800 ms). That is, the process of users' perception and attention toward app icons combines "bottom-up" and "top-down" processes. Our findings suggest a new perspective to use ERP components (P2, P3, and LPP) to deep understanding of app icon design. A practical implication is that app icons could be designed using anthropomorphic elements to attract users.

Applying Intelligent Algorithms to Automate the Identification of Error Factors.

OBJECTIVES: Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. METHODS: In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. RESULTS: Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. CONCLUSIONS: The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.

It is Time to Have Rest: How do Break Types Affect Muscular Activity and Perceived Discomfort During Prolonged Sitting Work.

BACKGROUND: Prolonged sitting at work can lead to adverse health outcomes. The health risk of office workers is an increasing concern for the society and industry, with prolonged sitting work becoming more prevalent. OBJECTIVE: This study aimed to explore the variation in muscle activities during prolonged sitting work and found out when and how to take a break to mitigate the risk of muscle symptoms. METHODS: A preliminary survey was conducted to find out the prevalence of muscle discomfort in sedentary work. Firstly, a 2-h sedentary computer work was designed based on the preliminary study to investigate the variation in muscle activities. Twenty-four participants took part in the electromyography (EMG) measurement study. The EMG variations in the trapezius muscle and latissimus dorsi were investigated. Then the intervention time was determined based on the EMG measurement study. Secondly, 48 participants were divided into six groups to compare the effectiveness of every break type (passive break, active break of changing their posture, and stand and stretch their body with 5 or 10 mins). Finally, data consisting of EMG amplitudes and spectra and subjective assessment of discomfort were analyzed. RESULTS: In the EMG experiment, results from the joint analysis of the spectral and amplitude method showed muscle fatigue after about 40 mins of sedentary work. In the intervention experiment, the results showed that standing and stretching for 5 mins was the most effective break type, and this type of break could keep the muscles' state at a recovery level for about 30-45 mins. CONCLUSIONS: This study offers the possibility of being applied to office workers and provides preliminary data support and theoretical exploration for a follow-up early muscle fatigue detection system.

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.

Healthcare Professionals Risk Assessments for Alert Overrides in High-Risk IV Infusions Using Simulated Scenarios.

This study aimed to use healthcare professionals' assessments to calculate expected risk of intravenous (IV) infusion harm for simulated high-risk medications that exceed soft limits and to investigate the impact of relevant risk factors. We designed 30 infusion scenarios for four high-risk medications, propofol, morphine, insulin, and heparin, infused in adult intensive care unit (AICU) and adult medical and surgical care unit (AMSU). A total of 20 pharmacists and 5 nurses provided their assessed expected risk of harm in each scenario. Descriptive statistics, analysis of variance with least square mean, and post hoc test were conducted to test the effects of field limit type, soft (SoftMax), and hard maximum drug limit types (HardMax), and care area-medication combination on risk of harm. The results showed that overdosing scenarios with continuous and bolus dose limit types were assessed with significantly higher risks than those of bolus dose rate type. An overdose infusion in AICU over a large SoftMax was assessed to be of higher risk than over a small one, but not in AMSU. For overdose infusions with three levels of drug amount, greater drug amount in AICU and AMSU was assessed to have higher risk, except insignificant risk difference between the infusions with higher and moderate drug amount in AMSU. This study obtained expected risk for simulated high-risk IV infusions and found that different field limit and SoftMax types can affect expected risk based on healthcare professionals' perspectives. The findings will be regarded as benchmarks for validating risk quantification models in future research.

Constructing and measuring domain-specific emotions for affective design: a descriptive approach to deal with individual differences.

Assessing design solutions via domain-specific emotions has been widely concerned and explored in the field of affective design. However, the examination and accommodation of individual differences have not been settled sufficiently in the literature. To address this research gap, this paper proposes a descriptive approach to draw calibrated collective emotion patterns in survey-based affective design assessment. A 'Repertory Grid Interview linked with Rate-All-That-Apply' (RGI/RATA) procedure is firstly conducted to elicit and code the individual's personal emotional descriptions into mid-level Emotion Words (EWs) and to gather emotion data grids with each grid quantified by an individual's own EWs. The obtained individualised emotion data grids are then subjected to Multiple Factor Analysis (MFA) to extract collective emotional space, thus to enable conceptualising collective emotional dimensions and measuring calibrated collective responses. A case study demonstrating the implementation process for a simple project of appearance design assessment is also presented. Practitioner Summary: The proposed methodology may help a design team to investigate the shared patterns of domain-specific emotions through a single assessment survey. With the provided post hoc analysis tools, designers may also evaluate multi-level individual differences (e.g. regarding user groups or even intra-individual) quantitatively and at a low cost. Abbreviations: EWs: emotion words; HF/E: human factors and ergonomics; IEA: International Ergonomics Association; MFA: multiple factor analysis; PCT: personal construct theory; PCA: principal component analysis; RGI/RATA: repertory grid interview linked with rate-all-that-apply; RGI: repertory grid interview; RATA: rate-all-that-apply; SD: standard deviation; USB: universal serial bus.

Bibliometric analysis of simulated driving research from 1997 to 2016.

OBJECTIVE: The objective of this study was to explore the evolution footprints of simulated driving research in the past 20 years through rigorous and systematic bibliometric analysis, to provide insights regarding when and where the research was performed and by whom and how the mainstream content evolved over the years. METHODS: The analysis began with data retrieval in Web of Science with defined search terms related to simulated driving. BibExcel and CiteSpace were employed to conduct the performance analysis and co-citation network analysis; that is, probe of the performance of institutes, journals, authors, and research hotspots. RESULTS: A total of 3,766 documents were filtered out and presented an exponential growth from 1997 to 2016. The United States contributed the most publications as well as international collaborations followed by Germany and China. In addition, several universities in The Netherlands and the United States dominated the list of contributing institutes. The leading journals were in transportation and ergonomics. The leading researchers were also recognized among the 8,721 contributing authors, such as J. D. Lee, D. L. Fisher, J. H. Kim, and K. A. Brookhuis. Finally, the co-citation analysis illuminated the evolution of simulated driving research that covered the following topics roughly in chronological order: task-induced stress, drivers with neurological disorders, alertness and sleepiness while driving, trust toward driving assistance systems, driver distraction, the effect of drug use, the validity of simulators, and automated driving. CONCLUSIONS: This article employed bibliometric tools to probe the contributing countries, institutes, journals, authors, and mainstream hotspots of simulated driving research in the past 20 years. A systematic bibliometric analysis of this field will help researchers realize the panorama of global simulated driving and establish future research directions.

Evaluation of lifestyle risk factors and job status associated with back injuries among employees at a mid-western university.

For decades the literature has shown an association between work-related risk factors and back injuries among employees. However, only recently, there is a growing body of literature that suggests lifestyle risk factors may also be associated with back injuries. The purpose of this research was to determine if selected lifestyle risk factors are associated with a greater risk of back injuries. Further, there may be an association between job status and incident reporting, lost workdays cases and workers' compensation (WC) paid for back injuries among university employees. Aggregate data from a Health Risk Assessment (HRA) questionnaire were used to analyze 6053 university employees for lifestyle risk factors associated with back injuries. Of the total sample, 57% (n=3471) were female; 46% (n=2778) worked as clerical or service staff; and the mean age was 45years. Pearson chi-square (chi(2)) analyses indicate that job status (chi(2)=307.07, df=4, p<.001) and gender (chi(2)=40.14, df=2, p<.001) were associated with high risk back score. An ordinal regression analysis predicted that participants who exercised vigorously for at least 20min, 3 or more days per week, or 3 or more days per week of combined vigorous exercise and moderate-intensity physical activity are almost 30 times less likely to have a high back risk score compared to participants who do not exercise vigorously or participate in less than 3days per week of moderate-intensity physical activity (OR=29.68, 95% CI=25-35.25, p<.001). Participants who have a low risk score for BMI are three times less likely (OR=3.20, 95% CI=2.74-3.75, p<.001) to have a high back risk score when compared to participants who have a high risk score for BMI. A regression tree predicted high back risk scores were participants who: (1) receive an adequate amount of physical activity or vigorous exercise and is a male service or clerical staff; (2) do not receive an adequate amount of physical activity or vigorous exercise, and is not overweight; or (3) who do not receive an adequate amount of physical activity or vigorous exercise, and is overweight. Six years of Occupational Safety and Health Administration (OSHA) 300 logs and WC claims data paid for back injuries supported the finding that clerical or service staff had the greatest risk of back injuries. Based on the results of this study, there appears to be an association between lifestyle risk factors, job status and back injuries among university employees. We believe our evaluation approach may be used to study other work populations to verify the outcomes observed in this study.

Toward development of a computer-based methodology for evaluating and reducing medication administration errors.

The number of people aged 65 years and older in the United States is increasing. This age group consumes 30% of all prescription drugs. Many elderly people require assistance from caregivers in taking their medication. Medication administration errors can result if caregivers cannot remember to give the medications or do not understand how to give them. This study aims to determine a more effective method of presenting prescription instructions and to determine if the multiple resource hypothesis holds in the context of prescription instructions by evaluating the effect a voice prescription label (which gives audio instructions) has on comprehension and memory of a drug regimen under varying training level, task condition and instruction format. In performing a multivariate ANOVA on data collected among 48 formal and 48 informal caregivers, training level, task condition, sound condition and instruction format were found to significantly affect caregivers' memory and comprehension. There is evidence that audio instructions and the matrix format reduce errors. These results could lead to the development of a medication scheduling management SYSTEM that effectively provides prescription instructions, organizes medicines according to administration time and incorporates decision rules to determine what action should be taken if a dose is missed.

Effects of training and experience on perception of hazard and risk.

This study was designed to show how those proficient at a machining task, where proficiency was gained through both training and past experience, influence the perceived hazard and risk when observing Computer Numerical Control (CNC) machining. The study was also designed to determine whether the impact of the visual and auditory cues can be isolated during diagnosis of the hazard. In a study that included 40 participants, results show that trained observers can perceive more correctly a hazardous condition which is different from what one would predict based on psychophysics. The results also show that trained participants relied strongly on the auditory cues to diagnose the potential hazard and risk correctly whereas the untrained participants who had access to both auditory and visual senses perceived the potential hazard more incorrectly than those trained and more similarly to a traditional Stevens' psychophysical curve. Untrained subjects who had only auditory input had the most difficulty in distinguishing differences in the perception of hazard. This is important considering recent dialogue about whether there is a need to train for tasks that will be automated. The idea of training, especially for automated tasks, is important in allowing better recognition of hazard and risk in unusual circumstances. Additional research may help to improve the diagnosis of hazard and risk and may enable generalization of the results to other training scenarios in the manufacturing and services industries.