Cycling Training System Integrating Real-Time Sensor Data
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Solution Overview
Problem
Current cycling training systems lack integration of multiple information sources to provide effective training suggestions for cyclists, resulting in suboptimal cycling efficiency.
Innovation Solution
A cycling sport training suggestion system comprising a sport real-time analysis module with a time series neural network architecture, connected to a cyclist information module, route feature module, and sport role data base, which integrates real-time and historical data to generate personalized training suggestions, and adjusts pedaling difficulty based on route features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple information sources are integrated for cycling training analysis, then training suggestion accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple information sources including real-time sensor data (heart rate, speed, power), route features (elevation, distance, terrain), historical performance data, and weather conditions into a unified analysis system. This merging of diverse data streams enables comprehensive training suggestions while managing complexity through integrated architecture.
Solution Approach 2:
The system is designed to handle multiple types of data and provide various training suggestions through a single multi-functional platform. The analysis module can process different sensor inputs, route types, and performance metrics universally, generating personalized training advice without requiring separate specialized systems for each function.
2Measurement precision
If real-time sensor data is collected and analyzed, then cycling performance monitoring is improved, but energy consumption increases
Solution Approach 1:
The system implements continuous real-time monitoring of cycling performance through sensors that continuously collect data on heart rate, speed, power output, and other metrics. This continuous data stream enables dynamic training adjustments and real-time feedback without interruption, maintaining high monitoring accuracy throughout the entire cycling session.
Solution Approach 2:
The patent replaces manual performance monitoring and analysis with automated electronic sensor systems and computational algorithms. Instead of cyclists manually tracking their performance or coaches physically analyzing data, the system uses electronic sensors, wireless communication, and automated analysis modules to continuously monitor and evaluate performance, significantly reducing the energy and time required for manual intervention.
3Adaptability or versatility
If historical and perfect cyclist data are integrated, then training personalization is improved, but data processing complexity increases
Solution Approach 1:
The system pre-processes and stores historical performance data and ideal cycling benchmarks (perfect cyclist data) in structured formats before actual training sessions. This preliminary organization of reference data enables rapid comparison with real-time performance during cycling, allowing personalized training suggestions to be generated quickly without complex real-time processing of raw historical data.
Data Source
AI summary
A cycling sport training suggestion system (10) includes a sport real-time analysis module (102), a cyclist information module (104), a route feature module (106) and a sport role data base (108). The cyclist information module (104) transmits a cyclist real-time sense information (110) to the sport real-time analysis module (102). The route feature module (106) transmits a route feature information (112) to the sport real-time analysis module (102). The sport role data base (108) transmits a cyclist historical sport information (114) and a perfect cyclist sport information (116) to the sport real-time analysis module (102). The sport real-time analysis module (102) integrates and analyzes the cyclist real-time sense information (110), the route feature information (112), the cyclist historical sport information (114) and the perfect cyclist sport information (116) to generate a cycling sport training suggestion signal (118).