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An app for multidisciplinary sport science

For creating sports innovations we need tools to collect data on physical activity behaviour. Collecting data is time-consuming and expensive. Therefore, sharing data and using it to answer multiple research questions is desirable. Especially in larger multidisciplinary research projects, it is beneficial if a diverse data set can be collected effortlessly. For this, we created a smartphone app that can be used for scientific data collection on physical activity behavior in sport research.The requirement for the app was that the different research partners could contribute, use the app independently and that the individual contributions could be easily combined into a larger app. We created a base app which provides a framework for easy implementation of custom modules. Each functionality in the app is represented in a module and can be created by different parties. The app uses the EventBus framework [1] to allow easy communication between independent modules including an independent GUI. In our presentation we will further explain this independance using the beacon physical activity app BAMBEA [4] .Over the span of three years we made a sensor collection-, emotion input-, beacon scanner-, beacon handler-, goal-, reward- and message/notification-module. We combined different modules into different versions. A first version initially focused on running behaviour and used a movement- and a heart rate-module that were originally created for research [2] and different modules of partners (like gait step frequency and emotion detection). The app was extended with a GUI to test the user experience of the modules [3]. Finally, the framework was updated and combined different modules and a new GUI for a bootcamp app [4]. In conclusion we experienced the modular framework as a good solution for reuse and collaboration within multidisciplinary research projects. By allowing for the independent development of modules only intense communication between partners was needed when conflicts occurred between modules. Another advantage was that partners could develop their module in their desired programming language and later integrate their module in the app by providing a small wrapper. We consider the framework approach as a success for projects where close collaboration is desired with a multidisciplinary team. However, we would not advise this approach when it can be expected that multiple heavy performance solutions are required and resources like data collection, storage and backup cannot be shared. In that case a tight integration and continuous collaborative implementation is likely a preferred approach.This research is made possible by the Amsterdam University of Applied Sciences research programs Urban Vitality, Amsterdam Creative Industries Network, the Amsterdam Institute of Sport Sciences (AISS) and by the Dutch National program COMMIT/.

Het effect van beacons in de ByAmfi Store gemeten, geanalyseerd en ontmaskerd

Volgens meerdere marketeers zijn beacons ‘the next big thing’; en grote retailers als de Bijenkorf zetten beacons al in. Klanten kopen namelijk steeds meer via internet, en winkels zoeken daarom naar manieren om een beleving te bieden, zodat klanten naar de winkel blijven komen.

Guiding the visually impaired through the environment with beacons

Urban environments are full of noise and obstacles, and therefore potentially dangerous and difficult to navigate for the visually impaired. Using Bluetooth beacons and a smartphone app we guide them through these environments by providing the information needed for that specific location. We present the preliminary results concerning the usability of our approach.

Guiding the Visually Impaired through the Environment with Beacons

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.

Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots

The estimation of the pose of a differential drive mobile robot from noisy odometer, compass and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment. https://doi.org/10.4018/IJAIML.2020010101 https://www.linkedin.com/in/john-bolte-0856134/