Design and Evaluation of a Wearable-Sensor Intelligent Training System for Aerobic Gymnastics: Machine-Learning–Based Movement Assessment and Neuromuscular Effects in a Randomized Controlled Trial
Abstract
Background: Aerobic gymnastics demands precise coordination of explosive jumps, rapid transitions and high-intensity aerobic movements. Conventional coach-centered training relies mainly on visual observation and therefore provides limited, non-quantitative feedback on force-related movement quality. Recent advances in wearable sensing and machine learning create opportunities for real-time, data-driven intelligent training systems, but rigorous evaluations combining both algorithmic performance and training outcomes remain scarce. Objective: This study designed a wearable-sensor intelligent training system integrating inertial measurement units (IMUs), a force plate and a machine-learning classifier for real-time movement assessment, and evaluated (1) the computational performance of the system and (2) its effects on force-related neuromuscular performance in competitive aerobic gymnastics athletes compared with three commonly used training approaches. Methods: Thirty-six competitive aerobic gymnastics athletes were randomly allocated to four groups (n = 9 each): conventional coach-centered technical training (CON), structured progressive technical training (P-I), high-intensity conditioning-oriented training (P-II) and a digitalized intelligent training program (DITP) implemented via the proposed system. All groups trained four times per week for 8 weeks (90 min per session) using the same RAMP-based warm-up. The intelligent system acquired synchronized IMU and force-plate…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 9 participants or records.
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