Artificial intelligence in lower limb joint moment prediction during typically developed gait: A systematic review and multilevel random-effects meta-analysis
Bibliographic record
Abstract
Objective Artificial intelligence (AI) methods have been widely applied in gait analysis, yet quantitative comparisons across models and their input–output specifications remain limited. This study aims to systematically review and synthesize the existing literature to evaluate the effectiveness of AI methods in predicting lower limb joint moments during typically developed (TD) gait. Methods Relevant studies published before July 1, 2025, were retrieved from five databases (PubMed, Scopus, IEEE Xplore, ScienceDirect, and Web of Science) using Boolean logic operations and were screened according to predefined criteria. Risk of bias and applicability were assessed with PROBAST. Meta-analyses were performed in R using a multilevel random-effects model to examine differences in predictive performance across AI model group, signal input type, and output joints. Results Eleven studies involving 371 TD participants met the inclusion criteria. Deep neural networks (DNN) showed the best performance for R 2 (0.88, 95%CI 0.52-1.24), while traditional machine learning (ML) models demonstrated relative superiority for nRMSE (0.11, 95%CI − 0.06-0.29). Among input types, surface EMG (sEMG) achieved the highest R 2 (0.96, 95%CI 0.04-1.89), whereas all inputs except “kinematic and speed and anthropometrics” performed well in the nRMSE analysis. For output joints, the ankle was significantly superior to both the knee ( p < 0.001) and the hip ( p < 0.001) in terms of R 2 and nRMSE. Conclusion AI methods can effectively predict lower limb joint moments during TD gait, but differences exist across model group, input type, and output joints. DNN show advantages in fitting complex data, while traditional ML demonstrates greater robustness in small-sample settings. The sEMG, as a process-related input, exhibits high potential, and predictions for the ankle joint are generally superior. Future studies should expand sample size, explore multimodal inputs and advanced modeling strategies, and further validate the applicability of AI methods in pathological gait.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".