Estimation of LT with dynamic transfer function models with commercial HR and power sensor data
Keywords:
Lactate threshold, performance, modelling, estimation, wearables, Machine learning, Transfer function modelsAbstract
The anaerobic threshold (LT) serves as a pivotal marker in cycling training but its regular monitoring is hindered by cost and invasiveness. This study explores a modelling approach for LT estimation using heart rate (HR) and power data collected from wearable technology. Twenty-four cyclists underwent incremental tests while wearing various commercial sensors. A discrete-time transfer function method was employed for modelling, with time-variant parameter (TVP) models showing promising accuracy (average error: 4%) in LT estimation. The adaptability of TVP models to capture HR dynamics contributed to their efficacy. This modelling technique offers a potential alternative for routine LT monitoring, leveraging widely used wearable sensors in cycling. Further validation and adaptation to field data are warranted.
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