During amateur cycling training, analyzing sensor data in real-time would allow riders to receive immediate feedback on how they are performing, and adapt their training accordingly. In this paper, a solution with Semantic Web technologies is presented that gives such real-time personalized feedback, by integrating the data streams with domain knowledge, rider profiles & other context data. This solution consists of a stream reasoning engine running on a low-end Raspberry Pi device, and a tablet app showing feedback based on the continuous query results. To demonstrate this in a static environment, a virtual training app is presented, allowing a user to simulate an amateur cycling training.
CITATION STYLE
De Brouwer, M., Ongenae, F., & De Turck, F. (2019). Demonstration of a Stream Reasoning Platform on Low-End Devices to Enable Personalized Real-Time Cycling Feedback. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11762 LNCS, pp. 28–32). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32327-1_6
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