Algorithm-Based Real-Time Analysis of Training Phases in Competitive Canoeing: An Automated Approach for Performance Monitoring

The increasing demands in high-performance sports have led to the integration of technological solutions for training optimization. This study aimed to develop and validate an algorithm-based system for analyzing three critical phases in canoe training: initial acceleration, steady-state cruising, and final sprint. Using inertial measurement units (WIMU PRO™) sampling at 10 Hz, we collected performance data from 12 young canoeists at the Mar Menor High-Performance Sports Center. The custom-developed algorithm processed velocity–time data through polynomial fitting and phase detection methods. Results showed distinctive patterns in the acceleration phase, with initial rapid acceleration (5 s to stabilization) deteriorating in subsequent trials (9–10 s). Athletes maintained consistent stabilized speeds (14.62–14.98 km/h) but required increasing space for stabilization (13.49 to 31.70 m), with slope values decreasing from 2.58% to 0.74% across trials. Performance deterioration was evident through decreasing maximum speeds (18.58 to 17.30 km/h) and minimum speeds (11.17 to 10.17 km/h) across series. The algorithm successfully identified phase transitions and provided real-time feedback on key performance indicators. This technological approach enables automated detection of training phases and provides quantitative metrics for technique assessment, offering coaches and athletes an objective tool for performance optimization in canoeing. Our aim is to automate the analysis task that is currently performed manually by providing an algorithm that the coaches can understand, using very basic mathematical tools, and that saves time for them.