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

TL;DR

This paper presents an AI-driven method for analyzing movement kinematics in resistance training, revealing how partial range-of-motion affects variability and consistency across exercises and participants.

Contribution

It introduces a novel AI pose estimation pipeline combined with statistical modeling to quantify and compare ROM in resistance exercises under different conditions.

Findings

pROM reduces range of motion without affecting repetition duration.

pROM increases variability in movement execution.

Lengthened partials achieve about 56% of full ROM, with variability across exercises.

Abstract

This study develops an AI-based pose estimation pipeline for quantifying movement kinematics in resistance training. Using videos from Wolf et al. (2025), comprising 303 recordings of 26 participants performing eight upper-body exercises under full (fROM) and lengthened partial (pROM) conditions, we extract joint-angle trajectories using five distinct deep-learning pose estimation models and a unified signal-processing framework. From these trajectories, we derive repetition-level metrics including range of motion (ROM) and repetition duration. We use these outputs as dependent variables in a crossed random-effects model that accounts for participant-, exercise-, and model-level variability to assess systematic differences between ROM conditions. Results indicate that pROM reduces range of motion without significantly affecting repetition duration. Variance decomposition shows that pROM increases both between-participant and between-exercise variability, suggesting reduced consistency in execution. To enable cross-exercise comparison, we model ROM on a logarithmic scale and define %ROM as the proportion of fROM achieved under pROM. While the estimated mean is approximately 56\%, significant heterogeneity across exercises indicates that lengthened partials are not characterized by a fixed proportion of full ROM. The results demonstrate that AI-based motion analysis can provide reliable kinematic insights to inform evidence-based training recommendations.