Estimation of sagittal-plane whole-body angular momentum during perturbed and unperturbed gait using simplified body models

J. Zhang*, M. Van mierlo, P.H. Veltink, E.H.F. van Asseldonk

*Corresponding author for this work

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Human whole-body angular momentum (WBAM) during walking typically follows a consistent pattern, making it a valuable indicator of the state of balance. However, calculating WBAM is labor-intensive, where the kinematic data for all body segments is needed, that is, based on a full-body model. In this study, we focused on selecting appropriate segments for estimating sagittal-plane WBAM during both unperturbed and perturbed gaits, which were segments with significant angular momentum contributions. Those major segments were constructed as a simplified model, and the sagittal-plane WBAM based on a simplified model was calculated by combining the angular momenta of the selected segments. We found that the WBAM estimated by seven-segment models, incorporating the head & torso (HT) and all lower limb segments, provided an average correlation coefficient of 0.99 and relative angular momentum percentage of 96.8% and exhibited the most similar sensitivity to external perturbations compared to the full-body model-based WBAM. Additionally, our findings revealed that the rotational angular momenta (RAM) of lower limb segments were much smaller than their translational angular momenta (TAM). The pair-wise comparisons between simplified models with and without RAMs of lower body segments were observed with no significant difference, indicating that RAMs of lower body segments are neglectable. This may further simplify the WBAM estimation based on the seven-segment model, eliminating the need to estimate the angular velocities of lower limb segments. These findings have practical implications for future studies of using inertial measurement units (IMUs) for estimating WBAM, as our results can help reduce the number of required sensors and simplify kinematics measurement.
Original languageEnglish
Article number103179
JournalHuman movement science
Early online date21 Jan 2024
Publication statusPublished - Feb 2024


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