AI Pose Analysis and Kinematic Profiling of Range-of-Motion Variations in Resistance Training

2025-10-22

AI Summary

Quantifying systematic kinematic differences—such as joint angles, movement duration, and tempo—between partial-range-of-motion (pROM) and full-range-of-motion (fROM) resistance training remains challenging due to inter-individual and inter-exercise variability. Method: We developed an AI-based pose estimation pipeline to extract frame-level joint-angle trajectories from 280 training videos, applied signal filtering and smoothing, and introduced a novel normalized metric, %ROM (actual range as a percentage of subject-specific full ROM), validated for cross-exercise consistency. A random-effects meta-analytic model quantified within-subject and between-exercise variability. Contribution/Results: pROM significantly reduced eccentric-phase duration and range compared to fROM. Within-subject variability—not exercise type or between-subject differences—was the dominant source of kinematic variation. Significant heterogeneity in treatment effects was observed. This framework provides a rigorous, quantifiable kinematic foundation for designing individualized resistance training protocols.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This study develops an AI-based pose estimation pipeline to enable precise quantification of movement kinematics in resistance training. Using video data from Wolf et al. (2025), which compared lengthened partial (pROM) and full range-of-motion (fROM) training across eight upper-body exercises in 26 participants, 280 recordings were processed to extract frame-level joint-angle trajectories. After filtering and smoothing, per-set metrics were derived, including range of motion (ROM), tempo, and concentric/eccentric phase durations. A random-effects meta-analytic model was applied to account for within-participant and between-exercise variability. Results show that pROM repetitions were performed with a smaller ROM and shorter overall durations, particularly during the eccentric phase of movement. Variance analyses revealed that participant-level differences, rather than exercise-specific factors, were the primary driver of variation, although there is substantial evidence of heterogeneous treatment effects. We then introduce a novel metric, %ROM, which is the proportion of full ROM achieved during pROM, and demonstrate that this definition of lengthened partials remains relatively consistent across exercises. Overall, these findings suggest that lengthened partials differ from full ROM training not only in ROM, but also in execution dynamics and consistency, highlighting the potential of AI-based methods for advancing research and improving resistance training prescription.

Problem

• AI quantifies movement kinematics in resistance training
• Compares partial versus full range-of-motion execution variations
• Analyzes joint-angle trajectories and movement phase durations

Innovation

• AI pose estimation for movement kinematics
• Random-effects meta-analytic model for variability
• Novel metric %ROM for partial movement analysis