What is it about?
This study presents the development and validation of an algorithm that automatically detects three essential phases in canoe sprint training—initial acceleration, steady-state cruising, and final sprint—using data from wearable inertial measurement units (WIMU PRO™) sampling at 10 Hz. Data were collected from 12 young canoeists and processed with polynomial fitting and signal-detection methods to identify transition points between phases. The algorithm successfully distinguished phase transitions in real time and provided clear performance metrics for each stage
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Why is it important?
Traditional training in canoeing often depends on subjective observation to assess phase transitions. This algorithmic method offers coaches and athletes objective, quantitative metrics such as time to stabilization, distance covered, speed consistency, and fatigue effects. It reduces manual analysis time and enhances precision in training feedback
Perspectives
Future work should expand sample sizes, include athletes of varied ages and competitive levels, and integrate additional sensor inputs for robust real-world application. Adapting the algorithm for live, on-device analysis and combining it with machine learning could further optimize training adjustments and performance prediction in competitive canoeing
Manuel Gómez-López
University of Murcia
Read the Original
This page is a summary of: Algorithm-Based Real-Time Analysis of Training Phases in Competitive Canoeing: An Automated Approach for Performance Monitoring, Algorithms, April 2025, MDPI AG,
DOI: 10.3390/a18050242.
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