Introduction: Visually impaired people experience trouble with navigation and orientation due to their weakened ability to rely on eyesight to monitor the environment [1][2]. Smartphones such as the iPhone are already popular devices among the visually impaired for navigating [3]. We explored if an iPhone application that responds to Bluetooth beacons to inform the user about their environment could aid the visually impaired in navigation in an urban environment.Method: We tested the implementation in an urban environment with visually impaired people using the route from the Amsterdam Bijlmer train station to the Royal Dutch Visio office. Bluetooth beacons were attached at two meters high to lampposts and traffic signs along a specified route to give the user instructions via a custom made iPhone app. Three different obstacle types were identified and implemented in the app: a crossover with traffic signs, a car parking entrance and objects blocking the pathway like stairs. Based on the work of Atkin et al.[5] and Havik et al. [6] at each obstacle the beacon will trigger the app to present important information about the surroundings like potential hazards nearby, how to navigate around or through obstacles and information about the next obstacle. The information is presented using pictures of the environment and instructions in text and voice based on Giudice et al. [4]. The application uses Apple’s accessibility features to communicate the instructions with VoiceOver screenreader. The app allows the user to preview the route, to prepare for upcoming obstacles and landmarks. Last, users can customize the app by specifying the amount of detail in images and information the app presents.To determine if the app is more useful for the participants than their current navigational method, participants walked the route both with and without the application. When walking with the app, participants were guided by the app. When walking without the app they used their own navigational method. During both walks a supervisor ensured the safety of the participant.During both walks, after each obstacle, participants were asked how safe they felt. We used a five point Likert scale where one stood for “feeling very safe” and five for “feeling very unsafe”.Qualitative feedback on the usability of the app was collected using the speak-a-lout method during walking and by interview afster walking.Results: Five visually impaired participated, one female and five males, age range from 30 to 78 and with varying levels of visual limitations. Three participants were familiar with the route and two walked the route for the first time.After each obstacle participants rated how safe they felt on a five point Likert scale. We normalized the results by deducting the scores of the walk without the app from the scores of the walk with the app. The average of all participants is shown in figure 2. When passing the traffic light halfway during the route we see that the participants feel safer with than without the app.Summarizing the qualitative feedback, we noticed that all participants indicated feeling supported by the app. They found the type of instructions ideal for walking and learning new routes. Of the five participants, three found the length of the instructions appropriate and two found them too long. They would like to split the detailed instructions in a short instruction and the option for more detailed instructions. They felt that a detailed instruction gave too much information in a hazardous environment like a crossover. Two participants found the information focused on orientation not necessary, while three participants liked knowing their surroundings.Conclusion and discussion: Regarding the safety questions we see that participants felt safer with the app, especially when crossing the road with traffic lights. We believe this big difference in comparison to the other obstacles is due to the crossover being considered more dangerous than the other obstacles. This is reflected by their feedback in requesting less direct information at these locations.All participants indicated feeling supported and at ease with our application, stating they would use the application when walking new routes.Because of the small sample size we consider our results an indication that the app can be of help and a good start for further research on guiding people through an urban environment using beacons.
To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
