Influence Of Sex On Marker To Marker-less Motion Capture Evaluations Of Gait Biomechanics
Bibliographic record
Abstract
Marker-based (MB) motion capture is affected by pelvic shape and soft tissue distribution that differ between sexes. Marker-less (ML) motion capture may provide an alternative. PURPOSE: To compare the effect of sex and method on knee and hip kinematics during gait. METHODS: Twenty participants (10 females: Age = 20.3 ± 0.95 years; Body Mass Index = 21 ± 1.83 kg/m2; 10 males: Age = 21.7 ± 1.34 years; Body Mass Index = 23.37 ± 1.37 kg/m2) participated. Participants completed two over-ground gait sessions at a self-selected pace with MB and ML recording a full right limb gait cycle simultaneously. Hip and knee kinematics in the sagittal and frontal plane were extracted and time-normalized from 0-100% of the gait cycle. Statistical parametric mapping using 2 (sex) by 2 (method) ANOVA with repeated measures compared kinematic waveforms. RESULTS: Interaction effects were found for sagittal plane hip angle from 0-7% (p = 0.009) and 71-100% (p = 0.004) of gait (F = 9.013). ML hip flexion angle was lower for 100% of gait compared to MB (p > 0.001, Z = 2.69), but sex comparisons were influenced by method. Females had greater hip flexion than males in MB (p < 0.001, Z = 3.29) from 0-33% and 64-100% of gait but no difference between sexes were found in ML. There was a main effect of method for frontal plane hip angle from 57-79% of gait (p = 0.007, F = 9.728). ML had less hip abduction (p = 0.005, Z = 2.92) from 57-79% of gait compared to MB. A main effect of method was found for sagittal plane knee angle from 3-23% and 39-100% of gait (p < 0.001, F = 11.965) and frontal plane knee angle from 34-40% and 84-92% of the gait cycle (p = 0.033, p = 0.019, F = 11.261). Knee flexion from 1-25% and 38-100% (p < 0.001, Z = 3.11) and knee adduction from 27-41% and 83-93% of gait were less for ML compared with MB, respectively (p = 0.01, p = 0.003, Z = 3.12). A main effect of sex was found for frontal plane hip angle from 0-40% and 63-100% (p = 0.001, F = 9.013). Females had a larger hip adduction angle than males (p = 0.007, Z = 2.86) between 72-90% of gait. CONCLUSION: Motion capture method influences gait comparisons between sexes, particularly at the hip joint. Studies are needed to develop sex specific ML models that identify differences in hip kinematics between males and females. Lastly, consistent main effects were observed of method that indicate some offset between MB and ML methods for other outcomes. Canadian Foundation for Innovation John R. Evans Leader Fund (Project 42110; PI: Pamukoff) Natural Science and Engineering Research Council of Canada (RGPIN-2022-04804; PI: Pamukoff)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".