A Field Workflow for Aligning Video-Derived Movement Descriptors with Internal-Load Measures in Functional Training Classes

Background and Objectives: This study evaluated a field workflow for obtaining video-derived movement descriptors during functional training (FT) classes and examining their relationships with independently measured indicators of internal load. Methods: In a cross-sectional observational study, 25 practitioners were recorded during one class each across six training days. A fixed smartphone camera and the previously developed Movement Description Language (MDL) pipeline were used to derive set-level repetition counts, timing measures, image-plane joint-angle proxies, and angular-speed descriptors. Video-derived descriptors were paired with Polar H10 heart-rate (HR) measurements at the set level; post-session perceived-exertion and fatigue ratings were analysed at the participant level. Pooled correlations were treated as descriptive, and linear mixed-effects models accounted for repeated sets within participants. Results: Video-derived data were available for 551 exercise sets; 532 sets from 24 participants could be linked to HR. Some pooled set-level descriptors correlated with HR, including peak shoulder angle with mean HR (Spearman’s ρ=0.38) and peak HR (ρ=0.43; 78 sets for which a shoulder angle was defined). Associations involving post-session ratings were exploratory and were assessed at the participant level. No video-derived descriptor met the false-discovery-rate criterion in the participant-clustered mixed-effects models (all q≥0.31). In separately fitted models, HR increased with round index (q<0.001), a session-progression variable that was not derived from the video descriptors. Conclusions: This study documents the field application of an end-to-end workflow linking smartphone video acquisition, pose estimation, MDL-based segmentation, extraction of set-level movement descriptors, and alignment with independently measured heart rate and post-session ratings during scheduled functional-training classes. The present evidence defines the current boundary of the extracted variables and does not support claims of predictive validity or individual internal-load estimation. The documented workflow, field dataset, and identified measurement limitations provide a methodological basis for criterion validation, exercise-balanced repeated observations, and future individual-level evaluation.