In Europe, large numbers of people with disabilities are willing to work but have problems finding a job. One of the barriers to this is job complexity, particularly for those with low education, low IQ, or cognitive impairments. Digital technologies might help. Specifically, cognitive support technology (CST) has the potential to make jobs less complex and thus more accessible. CST may concern step-by-step digital instructions presented with monitors, tablets, smart phones, beamer projections, or near-eye displays. Based on cross-case evaluations, we aimed to define the success factors in the process of technology selection, development, and implementation. Four cases, situated at public social firms which offer jobs to people with disabilities, were selected. In each case, the optimal form of CST was selected. A qualitative analysis of subjective experiences of work accessibility, performance, usability, and acceptance was applied. The results were positive for most participants in most cases. Once installed, the CST was successful in simplifying jobs. A proportion of the workforce for which a specific job had been considered too complex was able to perform that job when supported by CST. Moreover, a majority of people judged the usability of the technology positively. For the consecutive steps of selection, development, and implementation, we ended up with eleven factors of success; these included, among others, shared and transparent decision making (in technology selection), the iterative and active involvement of workers to optimally adjust work instructions (in technology development), and explicit attention for psychosocial barriers (in technology implementation).

Technology for vulnerable people on the labour market. A human-centric approach New ict and robots in the workplace may raise productivity but can have negative effects on people. A human-centered approach and human-centered technology may prevent negative outcomes. In the present article, this is outlined for the diverse and vulnerable group of ‘people with distance to the labour market’.These people face difficulties in getting a job and holding a job for a long period of time. For getting a job, they may need support in getting motivated and acquiring the necessary skills. For holding a job, support may be needed for adequate task performance, for staying healthy and vital, for personal development and for being able to switch from one job to another.Various technologies may provide one or more of these types of support. In particular, cognitive support technology has a great potential to make difficult work easier and therefore more accessible to more people. In three case descriptions we illustrate the application of a human-centered approach and the potential of operator support technology for cognitively impaired people.The case description shows the surplus value of the human centered approach, analyzing first the barriers and needs of the people involved, before iteratively testing and developing the technology with an active involvement of end-users. The described cases as well as other small-scale pilots contribute to our insight in the potential of operator support technology in increasing the accessibility of jobs for cognitively impaired people. The quantification of effects on work participation requires application of this type of technology on a much larger scale.

Spatial Augmented Reality (sAR) as an assistive technology is a promising tool for experienced and novice industrial workers. Five industrial case studies in Dutch manufacturing companies are described to study the effects of sAR assistance on task completion time, learning speed, product quality, work load, technology acceptance and employability in manual assembly guidance and training. Although case study outcomes were rather positive and user acceptance was high, 2 out of 5 use case companies decided not to invest in this technology after the initial pilot project. The main barriers for implementation were concerns about the relatively high system costs, the initial instruction programming time and the required expertise to do so. Future system developments should improve the system's usability from a business process engineering perspective and thereby support zero programming of sAR systems and adaptive work instructions.

Automated truck platooning is getting an increasing interest for its potentially beneficial effects on fuel consumption, driver workload, traffic flow efficiency, and safety. Nevertheless, one major challenge lies in the safe and comfortable transitions of control from the automated system back to the human drivers, especially when they have been inattentive during highly automated driving. In this study, we investigated truck drivers' take-over response times after a system initiated request to take back control in non-critical truck platooning scenarios. 22 professional truck drivers participated in the truck driving simulator experiment and everyone was instructed to drive under three task conditions during highly automated driving: Driver monitoring condition (drivers were instructed to monitor the surroundings), Driver not-monitoring condition (drivers were provided with a hand-held tablet and were asked to use this), and Eyes-closed condition (drivers were not allowed to open their eyes). The total take-over response time was divided into the perception response time and the movement response time by manual video annotation. Results showed significantly longer total take-over times with high variability in both Driver not-monitoring and Eyes-closed conditions compared to the Driver monitoring condition. Hand movement response time was found to be the dominant component of the total take-over time, being influenced by the motoric manoeuvres to resume physical readiness before taking over control (e.g., putting away the hand-held tablet, or adjusting seating position). These results suggest the importance of a personalized driver readiness predictor as an input parameter for a safe and comfortable transition of control. (C) 2019 Elsevier Ltd. All rights reserved.

Augmented Reality (AR) as an assistive technology is a promising tool for novice operators to learn assembly processes. This experiment compared an AR instruction method to display based electronic working instructions (EWI) for product assembly, to assess learning during the first repetitions of the products. In addition, two types of work instructions were used, i.e., standard and chunk instructions. In this experiment a chunk instruction consists of six assembly steps. Effects of the instruction method and type on the learning phase were evaluated with 24 novice operators building two products i.e.. Operators were then asked to build the same products without instructions in order to assess learned skills and establish effects on the recall phase, also as a result of instruction method and type. Task completion time (TCT), product quality, operator workload and learning curve were measured. The learning curve, as indicated by the TCT, took place during the first three repetitions of product assembly. Instruction method and instruction type had no effect on the learning curve. Product quality was high and no differences were found between learning conditions. Operator workload revealed that chunking of the instruction increased workload during the learning phase. During the recall phase, the AR group's TCT increased 19.2%, but only for the first product's repetition without instruction. Product quality remained the same during the recall phase, however operator workload was reduced for chunk learned products. This study indicates that chunking of instructions should be avoided for novice workers. Both EWI and AR can be used for teaching new assembly procedures. While AR and EWI are useful during the learning phase, there are indications that these methods might hinder the operator once they required the necessary skills and knowledge to assemble the product. A possible solution is making instructions more adaptive to fit the skill proficiency of the operator.

There is a growing interest in the application of psychophysiological signals in more applied settings. Unidirectional sensory motor rhythm-training (SMR) has demonstrated consistent effects on sleep. In this study the main aim was to analyze to what extent participants could gain voluntary control over sleep-related parameters and secondarily to assess possible influences of this training on sleep metrics. Bidirectional training of SMR as well as heart rate variability (HRV) was used to assess the feasibility of training these parameters as possible brain computer interfaces (BCI) signals, and assess effects normally associated with unidirectional SMR training such as the influence on objective and subjective sleep parameters. Participants (n = 26) received between 11 and 21 training sessions during 7 weeks in which they received feedback on their personalized threshold for either SMR or HRV activity, for both up- and down regulation. During a pre- and post-test a sleep log was kept and participants used a wrist actigraph. Participants were asked to take an afternoon nap on the first day at the testing facility. During napping, sleep spindles were assessed as well as self-reported sleep measures of the nap. Although the training demonstrated successful learning to increase and decrease SMR and HRV activity, no effects were found of bidirectional training on sleep spindles, actigraphy, sleep diaries, and self-reported sleep quality. As such it is concluded that bidirectional SMR and HRV training can be safely used as a BCI and participants were able to improve their control over physiological signals with bidirectional training, whereas the application of bidirectional SMR and HRV training did not lead to significant changes of sleep quality in this healthy population.

Automated platooning of trucks is getting increasing interest for its potentially beneficial effects on fuel consumption, driver workload, traffic flow efficiency and safety. Nevertheless, one major challenge lies in the safe and comfortable transitions of control from the automated system back to the human drivers, especially when they have been inattentive during highly automated driving. In this study, we investigated drivers’ take-over response times after a system initiated request to take back control in a non-critical scenario. 22 professional truck drivers participated in the truck driving simulator experiment and everyone was instructed to drive under three types of experimental conditions before the presentation of the take-over request: Driver monitoring (drivers were instructed to monitor the surroundings), Driver not-monitoring (drivers were provided with a tablet and were asked to use this) and Eyes-closed condition (they were not allowed to open their eyes). Driver take-over time components in terms of perception response times and movement response times were manually annotated from video recordings. Results showed significantly longer total takeover time and larger individual differences in both Driver not-monitoring and Eyes-closed conditions compared to the driver monitoring conditions. Movement time is found to be the one dominant factor, influenced by driver activities to resume physical readiness before taking over control. The large variability in take-over times between and within conditions suggests the importance of a personalized driver readiness predictor as input parameter for a safe and comfortable transition.

Automated platooning of trucks has its beneficial effects on energy saving and traffic flow efficiency. The vehicles in a platoon, however, need to maintain an extremely short headway to achieve these goals, which will result in a heavily blocked front view for the driver in a following truck. Monitoring surrounding traffic environment and foreseeing upcoming hazardous situations becomes a difficult, yet safety-critical task. This exploratory study aims to investigate whether providing platoon drivers with additional visual information of the traffic environment can influence their monitoring pattern and increase awareness of the upcoming situation. 22 professional truck drivers participated in the driving simulator experiment, either following a see-through lead truck (i.e., with projection of forward scene attached to the rear of the lead truck), or a normal lead truck until the automation system failed unexpectedly in a critical situation. Results showed that when provided with front view projection, the participants spent 10% more time monitoring the road, and responded less severely to a critical situation, suggesting a positive effect of the "see-through" technology.

Background : Truck platooning, with trucks being virtually connected, is getting more and more attention. Truck platooning offers the potential for substantial fuel savings while allowing the truck driver in the platoon to take a rest. However, at the current state of technology, truck drivers are still required to be alert and ready to intervene if required. How safe this situation of platooning is depends partly on whether the driver is able to take over control when requested. Under normal conditions, a transition back to the driver is requested by the system and the truck driver is provided with sufficient time and the situation is not too time critical. Under other conditions, a driver may need to respond to other traffic soon after taking over control, for instance by means of braking. Quite some studies have been performed about transitions of control from automated to manual driving, however studies with professional truck drivers in various platooning situations are scarce. Objective : The aim of this study was to investigate how long it takes before truck drivers take back manual control after a system warning if they can choose their own moment of getting back control. Methods : We studied various scenarios, in which drivers either had to monitor the surroundings, work with a tablet or keep their eyes closed. Besides the response times, we also studied the quality of the driving behaviour right after taking back control in order to investigate whether drivers were actually ready to take back control in normal conditions and also in conditions in which they needed to brake as a response to a braking lead vehicle. 22 professional truck drivers took part in a truck driving simulator experiment. An automated motorway truck platooning system was simulated that allowed the participant to hook on to a lead truck and follow automatically at close distance with hands off the steering wheel and feet off the pedals. All participants made 9 drives, including a manual truck driving condition to get a baseline condition. After hooking on to the platoon, they drove in automated platooning mode until the system requested them to take back control. After getting a warning, they could press a button on the steering wheel to indicate that were ready after which they got back control. Response times were recorded, as well as the quality of driving behaviour after the transition and responses to a lead vehicle braking action. Also, situational awareness and acceptance were studied, as well as eye movements, feet, hand and body position as well as input from wearables measuring heart beat and arm movements. Results : In case of voluntary take-overs, with truck drivers choosing their own moment to take back control after being requested by the system, response times vary quite substantially per condition and per driver. When drivers were asked to monitor the surroundings while platooning, mean response times were around 2.5 seconds, with not so much variance between drivers. For the condition in which drivers were working with a tablet, these response times doubled to around 5.5 seconds, with increasing variance. For the condition in which drivers had their eyes closed, mean response times were a little over 6 seconds, with a large variance and the slowest response times being over 16 seconds. When drivers had been platooning with shorter headways, they took somewhat more time to take back control. After having taken back control, the driving performance was measured and compared to manual truck driving behaviour. Under normal driving conditions, we found that overall performance was comparable to their normal driving behaviour. However for the condition in which drivers had been working with a tablet, there seems to be some negative after-effect of platooning on lateral performance, although effects are relatively small and seem to disappear over time. In the conditions in which drivers have to respond to a braking lead truck after taking back control, we see adequate responses to the lead truck although the minimum time to collision is sometimes quite low. The wearables did not deliver reliable results since there was a very low correlation between the two wrist bands that were used. Automatic analyses of eye movement data, hand, feet and body position showed to be quite complex due to the fact that drivers sometimes obstructed the video images with body or arms, and that sometimes short glances were done to the road that were not automatically detected by the Smart Eye camera. In general, participants were rather positive about the system, with a score of 7 out of 10. The majority of drivers would like to have this system in their truck, even though the trust of the system in the simulator was higher than their trust if they imagined using this on the real road. Conclusion : Drivers that were instructed to monitor the surroundings while truck platooning have short take-over times and lower variability in response times than drivers using a tablet or having their eyes closed during platooning. Remember that in this study, drivers could indicate themselves whether they were ready to take back control, so they could take more or less time. Drivers take more time to get back control when they have been platooning at shorter headways. Apparently truck drivers are aware that they had been out of the loop for a while and are aware of the involved risk in short following distances. There were large individual differences in response times between drivers, with large differences within one condition between drivers, but also with large differences between the different conditions. The large variability in response times is probably also due to the individual differences in body, hand and feet position, and in the fact that people were holding a tablet, were sometimes wearing reading glasses or changed the seating position of their chair. This is behaviour we could see on the video images, and more detailed analyses of the video images will be done in order to find further explanatory variables for differences in response times.

INTRODUCTION:Dutch North Sea helicopter operations are characterized by multiple sector flights to offshore platforms under difficult environmental conditions. In the context of a Ministry of Transport program to improve safety levels of helicopter operations, we assessed effects of pre-duty sleep, pre-duty travel time, and workload factors on the alertness and vigilance of pilots.METHOD:Data of 24 pilots comprising 224 duty days were analyzed. Pilots performed 10-min test sessions after wake up, pre-duty, halfway-duty, end-duty, and at bedtime during normal duty rosters. Test sessions included completion of a vigilance task, vigor and sleepiness ratings, and questions on sleep and operational characteristics. Pilots wore an actometer to objectify sleep data.RESULTS:Vigor scores were high and sleepiness levels were low during the entire flight duty periods (FDPs), while vigilance was impaired only 6.8% in the course of the FDPs. Pre-duty sleep before morning duties was 1.5 h shorter than sleep before duties starting after midday. Longer pre-duty travel time was correlated with shorter pre-duty sleep and lower vigilance levels during duty.CONCLUSION:During the FDPs, pilots maintained alertness and vigilance levels that may be considered safe in terms of alertness-related flight safety. This favorable outcome may be attributed to reasonable length of FDPs, favorable circadian start and end times of duties, sufficient opportunities for restorative pre-duty sleep, and relatively good weather conditions. Appropriate FDP scheduling is an important measure to optimize alertness of helicopter pilots who have to cope with adverse environmental conditions and limited landing and air traffic control facilities.

Connected Cruise Control (CCC) is a system, that is currently under development within a HTAS project. CCC aims to improve throughput in dense motorway traffic by advising drivers how to drive. The advice will integrate a lane advice, a headway advice and speed advice. The CCC advice will be provided via a nomadic device, using input from traffic flow predictions, loop data, floating car data and data of the direct surroundings from a camera inside the vehicle. The advantage of CCC lies in the potentially rapid implementation, in contrast to Cooperative Adaptive Cruise Control (CACC) systems. An additional advantage is the support in terms of lateral driver behaviour advice, However, since it is an advisory system, the actual effectiveness of the system totally depends on the driver response: Is a driver capable and willing to adhere to the advice? If not, the CCC system will not have any beneficial effect on throughput. A way to include the driver capabilities and willingness to accept the advice is to focus on use cases. Also, it is important to include the driver in the actual design by early driving simulator experiments and including Human Factors knowledge in the design of the HMI (Human Machine lnterface). A large user survey with over 230 respondents has been done, looking into driver frustratIons in driving in dense motorway traffic. lf the system can reduce some of the frustrations, acceptance of the rsystem will increase. This paper will discuss the possibilities and need of ADAS to solve specific driver problems in congestion, and discusses the outcomes of a first driving simulator experiment related to headway advice. The possibilities for driver feedback are discussed and the next set of experiments for the CCC system from a human factors point of view are discussed.

This report provides an overview of methods to prevent drowsy driving of drivers (Wilschut et al., 2009). Several preventive approaches were discussed such as the use of questionnaires, campaigns and fatigue management plans. A search for law enforcement instruments to prevent sleepiness at the wheel did not provide any instruments that could detect sleepiness of drivers objectively and reliably at present. There were four different driver groups defined which among the awareness of fatigue and drowsiness should be promoted professional drivers in the domain of freight traffic, professional drivers in the domain of passenger traffic, private drivers and drivers with sleeping disorders. The main part of the report focused on the state-of–the-art regarding drowsiness detection technology.

Connected Cruise Control (CCC) is a new approach to driver assistance that avoids some of the problems associated with autonomous driver assistance by advising optimal driver behaviour via a human-machine interface. Yet the characteristics of this advice can have a negative impact on driver distraction and additional task demand. While the system is still under development, an evaluation of system effects is needed to identify potential safety issues at an early stage in the design process. For this reason an introduction to CCC is provided, followed by a discussion of how advice attributes can generate distraction and add task demand. Finally, two human factors constructs are provided that can indicate the effects of systems characteristics on driver performance.

This paper describes the Human-Machine-Interface (HMI) design for two cooperative systems the Intelligent Cooperative Intersection Safety System (IRIS) and Speed Limit that were developed in the EU-project SAFESPOT. The tactile and haptic warnings of the cooperative systems were compared to the acoustic warnings both combined with a visual icon. Preferences for modalities of displaying information to the diver were mixed. Discussed is to use individual rather than general composed interfaces. Such an individual, or adaptive interface, could increase the acceptance of cooperative systems.