Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey.

Within the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and collaborative assembly applications by focusing on the main features that have positively influenced workers' cognitive responses. A methodology for structured development has been proposed. The draft guidelines have been created starting from the outcomes of a systematic and extended screening of the scientific literature. Preliminary validation has been carried out with the help of researchers working in the field. Inputs on comprehensibility and relevance have been gathered to enhance the guidelines. Lastly, a survey was used to examine in depth how international experts in different branches can interpret such guidelines. In total, 108 responders were asked to qualitatively and quantitatively evaluate the guideline's comprehensibility and provide general comments or suggestions for each guideline. Based on the survey's results, the guidelines have been validated and some have been reviewed and re-written in their final form. The present work highlights that integrating human factors into the design of collaborative applications can significantly bolster manufacturing operations' resilience through inclusivity and system adaptability by enhancing worker safety, ergonomics, and wellbeing.

Occupational safety in homecare organizations: the design and implementation of a train-the-trainer program.

Homecare workers face significant occupational risks, necessitating effective safety training programs. This paper presents a comprehensive Train-the-Trainer (TTT) program developed to enhance occupational safety in homecare organizations. Through an analysis of 229 reported safety events, the frequency and type of incidents, such as injuries during handling, road crashes, slips, trips, and falls, were identified and primarily attributed to human errors and violations. Based on the results, a TTT program was designed and implemented. The TTT successfully engaged Health, Safety, and Environment managers, fostering collaborative activities, knowledge sharing, and resource discussions. The program modules address critical areas, including distractions and inattentions, fatigue, time pressure, frustration and aggressiveness, and safety behaviors. This innovative approach provides valuable insights for organizations seeking to improve homecare workers' safety. The findings add to the broader comprehension of occupational safety in the homecare sector, proposing a pragmatic framework for future interventions.

Development and evaluation of design guidelines for cognitive ergonomics in human-robot collaborative assembly systems.

Industry 4.0 is the concept used to summarize the ongoing fourth industrial revolution, which is profoundly changing the manufacturing systems and business models all over the world. Collaborative robotics is one of the most promising technologies of Industry 4.0. Human-robot interaction and human-robot collaboration will be crucial for enhancing the operator's work conditions and production performance. In this regard, this enabling technology opens new possibilities but also new challenges. There is no doubt that safety is of primary importance when humans and robots interact in industrial settings. Nevertheless, human factors and cognitive ergonomics (i.e. cognitive workload, usability, trust, acceptance, stress, frustration, perceived enjoyment) are crucial, even if they are often underestimated or ignored. Therefore, this work refers to cognitive ergonomics in the design of human-robot collaborative assembly systems. A set of design guidelines has been developed according to the analysis of the scientific literature. Their effectiveness has been evaluated through multiple experiments based on a laboratory case study where different participants interacted with a low-payload collaborative robotic system for the joint assembly of a manufacturing product. The main assumption to be tested is that it is possible to improve the operator's experience and efficiency by manipulating the system features and interaction patterns according to the proposed design guidelines. Results confirmed that participants improved their cognitive response to human-robot interaction as well as the assembly performance with the enhancement of workstation features and interaction conditions by implementing an increasing number of guidelines.

Integrating Human Barriers in Human Reliability Analysis: A New Model for the Energy Sector.

Human reliability analysis (HRA) is a major concern for organizations. While various tools, methods, and instruments have been developed by the scientific community to assess human error probability, few of them actually consider human factors impact in their analysis. The active role that workers have in shaping their own performance should be taken into account in order to understand the causal factors that may lead to errors while performing a task and identifying which human factors may prevent errors from occurring. In line with this purpose, the aim of this study is to present a new methodology for the assessment of human reliability. The proposed model relies on well-known HRA methodologies (such as SPAR-H and HEART) and integrates them in a unified framework in which human factors assume the role of safety barriers against human error. A test case of the new method was carried out in a logistics hub of an energy company. Our results indicate that human factors play a significant role in preventing workers from making errors while performing tasks by reducing human error probability. The limits and implications of the study are discussed.

Visual Scanning Techniques and Mental Workload of Helicopter Pilots During Simulated Flight.

INTRODUCTION: The visual scanning techniques used by helicopter pilots are a critical skill to accomplish safe and correct landing. According to the human information processing theory, visual scanning techniques can be analyzed as a function of fixation location, number, and duration of fixations.METHODS: This study assessed these techniques in expert and novice pilots during an open sea flight simulation in a low-workload condition, consisting of a daylight and good weather simulation, and in a high-workload condition of night-time, low visibility, and adverse weather conditions. Taking part in the study were 12 helicopter pilots. Mental workload was assessed through psychological measures (NASA-TLX). The pilots performance was assessed and eye movements were recorded using an eye-tracker during four phases of the flight simulations.RESULTS: Overall, pilots made more fixations out of the window (OTW; 22.54) than inside the cockpit (ITC; 11.08), Fixations were longer OTW (830.17 ms) than ITC (647.97 ms) and they were shorter in the low-demand condition (626.27 ms). Further, pilots reported higher mental workload (NASA-TLX) in the high-demand condition compared to the low-demand condition, regardless of their expertise, and expert pilots reported a lower mental workload compared to novice pilots.DISCUSSION: Pilots performance and perceived mental workload varied as a function of expertise and flight conditions. Pilots rely on instrument support during the cruise phase and external visual cues during the landing phase. The implications for a new visual landing system design are discussed.Rainieri G, Fraboni F, Russo G, Tul M, Pingitore A, Tessari A, Pietrantoni L. Visual scanning techniques and mental workload of helicopter pilots during simulated flight. Aerosp Med Hum Perform. 2021; 92(1):1119.

Gender differences in cyclists' crashes: an analysis of routinely recorded crash data.

Previous research on gender differences in road crashes has focussed uniquely on car drivers and there has been little research examining such differences among cyclists. In this study, we investigated gender differences in bicycle crashes, using routinely recorded crash data. The present paper focussed on characteristics related to the type of crashes (type of collision and opponent vehicle), the infrastructure (road type and type of road segment), the environmental (season, road surface condition and weather) and time period (time of the day and day of the week). Results revealed that, compared to women cyclists, men cyclists were more likely to be involved in a crash regardless the cyclists' age. Moreover, we found gender differences in terms of type of road segment, type of opponent vehicle, type of manoeuvre of the opponent vehicle and of the cyclists, type of collision, time of the day, day of the week and season.

Green wave for cyclists: Users' perception and preferences.

Several innovative measures in traffic control applied in Europe have successfully improved the comfort and safety of cycling, among which is the green waves for cyclists. Consecutive traffic lights are synchronised to create a green wave, increasing comfort and decreasing waiting times and related deliberate red-light running. This study focused on exploring the user acceptance of green wave systems and the user evaluation of six distinct interface designs (i.e. numeric-based countdown, dot-based vertical countdown, dot-based clockwise countdown, LED line, LED road surface, on-bike speed indicator). Results indicate a preference for three systems: numeric-based countdown, LED line and LED road surface. Results also show a significant influence of nationality on the evaluation of the interfaces. Based on our findings, we argue that the numeric-based countdown represents the most promising option for future adaptive green wave implementations. The outcomes of the present study represent a useful evidence and guidance for researchers, designers and decision makers in the field of green waves, mobility and traffic safety.

Unsafe cycling behaviours and near crashes among Italian cyclists.

This study investigates the direct and indirect effect of three types of unsafe behaviours (i.e. errors, generic violations and smartphone-specific violations) on the likelihood of near crashes and actual crashes among Italian cyclists. We considered smartphone-specific violations as a different unsafe behaviour subtype that enhances the probability of committing errors, thus increasing the likelihood of being involved in near crashes. Furthermore, we hypothesized that near crashes will predict actual crashes. Results revealed that errors predicted near crashes, whereas generic and smartphone-specific violations did not. Near crashes mediated the effect of errors on crashes. Moreover, smartphone-specific violations predicted crashes throughout its consecutive effects on errors and near crashes. These findings contribute to deepen our understanding of the relationship between cyclists' unsafe behaviours, near crashes and actual crashes. To our knowledge, the present study is the first that links errors to near crashes among cyclists.

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining.

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist's maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types.

Using data mining techniques to predict the severity of bicycle crashes.

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes.

Cyclists' Anger As Determinant of Near Misses Involving Different Road Users.

Road anger constitutes one of the determinant factors related to safety outcomes (e.g., accidents, near misses). Although cyclists are considered vulnerable road users due to their relatively high rate of fatalities in traffic, previous research has solely focused on car drivers, and no study has yet investigated the effect of anger on cyclists' safety outcomes. The present research aims to investigate, for the first time, the effects of cycling anger toward different types of road users on near misses involving such road users and near misses in general. Using a daily diary web-based questionnaire, we collected data about daily trips, bicycle use, near misses experienced, cyclist's anger and demographic information from 254 Spanish cyclists. Poisson regression was used to assess the association of cycling anger with near misses, which is a count variable. No relationship was found between general cycling anger and near misses occurrence. Anger toward specific road users had different effects on the probability of near misses with different road users. Anger toward the interaction with car drivers increased the probability of near misses involving cyclists and pedestrians. Anger toward interaction with pedestrians was associated with higher probability of near misses with pedestrians. Anger toward cyclists exerted no effect on the probability of near misses with any road user (i.e., car drivers, cyclists or pedestrians), whereas anger toward the interactions with the police had a diminishing effect on the occurrence of near misses' involving all types of road users. The present study demonstrated that the effect of road anger on safety outcomes among cyclists is different from that of motorists. Moreover, the target of anger played an important role on safety both for the cyclist and the specific road users. Possible explanations for these differences are based on the difference in status and power with motorists, as well as on the potential displaced aggression produced by the fear of retaliation by motorized vehicle users.

Social Influence and Different Types of Red-Light Behaviors among Cyclists.

Accident analysis and studies on traffic revealed that cyclists' violation of red-light regulation is one typical infringement committed by cyclists. Furthermore, an association between cyclists' crash involvement and red-light violations has been found across different countries. The literature on cyclists' psychosocial determinants of red-light violation is still scarce. The present study, based on the classification of cyclists' red-light behavior in risk-taking (ignoring the red-light and traveling through the junction without stopping), opportunistic (waiting at red-lights but being too impatient to wait for green signal and subsequently crossing the junction), and law-obeying (stopping to obey the red-light), adopted an eye-observational methodology to investigate differences in cyclists' crossing behavior at intersections, in relation to traffic light violations and the presence of other cyclists. Based on the social influence explanatory framework, which states that people tend to behave differently in a given situation taking into consideration similar people's behaviors, and that the effect of social influence is related to the group size, we hypothesized that the number of cyclists at the intersection will have an influence on the cyclists' behavior. Furthermore, cyclists will be more likely to violate in an opportunistic way when other cyclists are already committing a violation. Two researchers at a time registered unobtrusively at four different intersections during morning and late afternoon peak hour traffic, 1381 cyclists approaching the traffic light during the red phase. The 62.9% violated the traffic control. Results showed that a higher number of cyclists waiting at the intersection is associated with fewer risk-taking violations. Nevertheless, the percentage of opportunistic violation remained high. For the condition of no cyclist present, risk-taking behaviors were significantly higher, whereas, they were significantly lower for conditions of two to four and five or more cyclists present. The percentage of cyclists committing a red-light violation without following any other was higher for those committing a risk-taking violation, whereas those following tended to commit opportunistic violations more often.