관절 움직임 기반 운동 해석을 위한 디지털 케이블 운동기구 활용

초록

This study presents a digital healthcare–oriented exercise analysis framework that uses sensor data from a digital cable machine to support objective assessment in everyday fitness environments. The system acquires force, position, and velocity signals through built-in load cell and encoder sensors, enabling data-driven evaluation without external devices. A biomechanically grounded joint load-distribution model and a Δpos-based segmentation algorithm were applied to automatically detect concentric/eccentric phases and repetition counts. Four adults performed five cable exercises at approximately 75% of their estimated one-repetition maximum. The system demonstrated high accuracy in phase recognition and repetition detection, and the generated load profiles aligned with hypertrophy-appropriate training intensities. The proposed approach establishes a lightweight, sensor-based digital exercise analysis pipeline that integrates measurement, algorithmic interpretation, and personalized load prescription. These findings highlight its potential for application in smart fitness systems and future extensions involving adaptive control algorithms and AI-driven personalized exercise guidance.

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