Demonstration of a Stream Reasoning Platform on Low-End Devices to Enable Personalized Real-Time Cycling Feedback
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.
KeywordsStream reasoning Low-end devices Real-time feedback Personalization Cycling
F. Ongenae is funded by a UGent BOF postdoc grant. Part of this research was funded by the FWO SBO S004017N IDEAL-IoT and the imec.icon CONAMO, funded by imec, VLAIO, Rombit, Energy Lab & VRT.
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