A markerless motion capture system can reliably determine peak trunk flexion while squatting with and without a weighted vest.

Markerless motion capture has improved physical screening efficiency in sport and occupational settings; however, reliability of kinematic measurements from commercial systems must be established. Further, the impact of torso-borne equipment on these measurements is unclear. The purpose of this study was to evaluate the reliability of HumanTrak, a markerless motion capture system, for estimating peak trunk flexion in squat movements with and without a weighted vest. Eighteen participants completed body weight squats (BWSQ) and overhead squats (OHSQ) to their maximum depth (unrestricted-range) and to a plyometric box (fixed-range) while wearing no body armour (NBA) or 9 kg body armour (BA9). Peak trunk flexion was measured using HumanTrak. Testing was performed in two sessions on one day (intra-day) and one session on a separate day (inter-day) to assess reliability. HumanTrak had a standard error of measurement < 3.74° across all movements and conditions. Reliability was good to excellent (ICC = 0.82-0.96) with very large to nearly perfect Pearson correlations (r > 0.80) for all comparisons except unrestricted-range BWSQ with BA9 (ICC = 0.60-0.71, r = 0.71). HumanTrak was more reliable for intra- than inter-day, but reliability was still excellent for almost all inter-day comparisons (ICC > 0.82). HumanTrak is reliable for detecting differences in peak trunk flexion > 8.5° when body armour is not worn and > 10.5° when body armour is worn. Practitioners can assess meaningful changes in sagittal plane trunk motion when screening squat movements regardless of whether body armour is worn.

Machine learning methods to support personalized neuromusculoskeletal modelling.

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.