[SECSI 2018] CONAMO - Continuous Athlete Monitoring through a Real-Time Sensor Data Analytics Platform
- 1. Continuous Athlete Monitoring through a Real-Time Sensor Data Analytics Platform SECSI 2018
- 3. SECSI 2018
- 5. SECSI 2018 ? ? 1. Analysis only happens offline 2. Mostly just data representation → no insights or recommendations
- 6. SECSI 2018
- 7. SECSI 2018
- 8. ICON Project – Collaboration between: SECSI 2018 Academics Industry
- 9. Data Dissemination in Challenging Environments SECSI 2018
- 10. SECSI 2018
- 11. SECSI 2018 Multi-hop (disruption-tolerant) communication
- 12. SECSI 2018 Multi-hop (disruption-tolerant) communication
- 13. SECSI 2018 TDF ‘15: Alpe D’Huez Multi-hop (disruption-tolerant) communication
- 14. SECSI 2018 Enhanced Low Latency Scheduling Function (eLLSF) channel + time
- 15. Real-Time Data Analytics Layer SECSI 2018
- 16. SECSI 2018 Registered Continuous SPARQL Queries Stream of resultsQuery contains: Stream + defined window Static data sources Query conditions Sliding window Visualized as real time feedback in web application Static context data: Profile information (a.o. training zones) Sensor information C-SPARQL engine A dynamic stream processing framework
- 17. SECSI 2018 Reasoning + data querying SensorA; heart rate; 162; 16/09/2017 15:56:07 SensorB; altitude; 293; 16/09/2017 15:56:07 SensorC; speed; 26; 16/09/2017 15:56:07
- 18. Training Scheme Compliance SECSI 2018
- 19. SECSI 2018
- 20. SECSI 2018 Context Workout history Basic user info Predict compliance next workout Next workout Manipulate input variables to get good compliance
- 21. SECSI 2018 Context Workout history Basic user info Predict compliance next workout Next workout Manipulate input variables to get good compliance
- 22. BIS: Analyzing in-app navigation through Markov Chain analysis SECSI 2018
- 23. SECSI 2018 Construct a Markov Chain based on all historical transitions
- 24. SECSI 2018 Clustering sequences with Expectation-Maximization