Can cycling power be estimated from speed, cadence, altitude change, and heart rate?
Estimating cycling power output is crucial for performance analysis and training optimization. Recent advancements in wearable technology have enabled the collection of rich cycling data, including speed, cadence, altitude change, and heart rate. This study investigates whether these parameters can be used to estimate cycling power accurately.
A comprehensive dataset was collected from 20 experienced cyclists performing a range of cycling activities. Power output was measured using a calibrated power meter, while speed, cadence, altitude change, and heart rate were recorded using a GPS cycling computer. Machine learning algorithms were employed to develop estimation models using various combinations of the input parameters.
The results demonstrated that speed, cadence, altitude change, and heart rate can be effectively utilized to estimate cycling power. The best-performing model, which incorporated all four parameters, achieved a mean absolute error of less than 5%. This suggests that it is possible to estimate cycling power accurately using these readily available measurements.
Related Questions:
- What is the least important parameter for estimating cycling power? Heart rate.
- Can altitude change be used to estimate power output when cycling uphill? Yes.
- Is cadence more important than speed for estimating power? Cadence is slightly more important.
- Can power be estimated using only speed and heart rate? Yes, but with reduced accuracy.
- How accurate is the best-performing model for power estimation? <5% mean absolute error.
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- Garmin Edge 1040 Solar GPS Cycling Computer
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