AI-ASSISTED GAIT ANALYSIS IN PHYSICAL THERAPY: A SYSTEMATIC REVIEW OF TOOLS AND REHABILITATION OUTCOMES
Bibliographic record
Abstract
Background: Artificial intelligence (AI) is increasingly being integrated into rehabilitation medicine, particularly in gait analysis for individuals with mobility impairments. Conventional gait assessments often rely on subjective evaluation or limited sensor technologies, which may lack precision and adaptability. Although various AI-based tools have emerged, the clinical relevance and effectiveness of these technologies in improving rehabilitation outcomes remain inadequately consolidated in existing literature. Objective: This systematic review aims to evaluate the effectiveness of AI-assisted gait analysis tools in physical therapy, focusing on their impact on treatment planning, functional recovery, and overall rehabilitation outcomes. Methods: A systematic review was conducted according to PRISMA guidelines. Four electronic databases—PubMed, Scopus, Web of Science, and Cochrane Library—were searched for studies published between 2020 and 2024. Eligible studies included randomized controlled trials, cohort studies, and observational designs involving patients undergoing physical therapy for gait dysfunction, utilizing AI-based gait analysis tools. Data extraction and risk of bias assessment were performed independently by two reviewers using standardized forms and validated tools such as the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. Results: Eight studies met inclusion criteria, encompassing 644 participants with conditions including stroke, Parkinson’s disease, and orthopedic impairments. AI technologies included wearable sensors, robotic trainers, and vision-based tracking systems. Most studies reported significant improvements in gait parameters such as cadence, stride length, and walking distance (p < 0.05), as well as better adherence and therapy personalization. Risk of bias was generally low to moderate, with some concerns related to performance blinding. Conclusion: AI-assisted gait analysis tools show promising clinical value in enhancing rehabilitation outcomes and supporting individualized therapy planning. While current evidence is encouraging, further large-scale and methodologically rigorous studies are needed to validate these findings and guide broader implementation.
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 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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".