ReviewFrontiers in rehabilitation sciences2024
Optimizing lower limb rehabilitation: the intersection of machine learning and rehabilitative robotics.
Review in Frontiers in rehabilitation sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Optimal training strategy for body weight support treadmill training to enhance lower limb motor function and activity of daily living in persons with stroke: a systematic review and meta-analysis of randomized controlled trials.Frontiers in neurology · 2025Pooled it
- Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy.Sensors (Basel, Switzerland) · 2024Pooled it
- Review
- Advances in sciatic nerve regeneration: A review of contemporary techniques.Regenerative therapy · 2025Review
- Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions.Journal of neuroengineering and rehabilitation · 2025Article
- Validating Questionnaires for Lower Limb Rehabilitation Systems and Devices: A Scoping Review.Sports (Basel, Switzerland) · 2025Review
- Motion control and singular perturbation algorithms for lower limb rehabilitation robots.Frontiers in neurorobotics · 2025Article
- Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lower limb rehabilitation is essential for recovery post-injury, stroke, or surgery, improving functional mobility and quality of life. Traditional therapy, dependent on therapists' expertise, faces challenges that are addressed by rehabilitation robotics. In the domain of lower limb rehabilitation, machine learning is progressively manifesting its capabilities in high personalization and data-driven approaches, gradually transforming methods of optimizing treatment protocols and predicting rehabilitation outcomes. However, this evolution faces obstacles, including model interpretability, economic hurdles, and regulatory constraints. This review explores the synergy between machine learning and robotic-assisted lower limb rehabilitation, summarizing scientific literature and highlighting various models, data, and domains. Challenges are critically addressed, and future directions proposed for more effective clinical integration. Emphasis is placed on upcoming applications such as Virtual Reality and the potential of deep learning in refining rehabilitation training. This examination aims to provide insights into the evolving landscape, spotlighting the potential of machine learning in rehabilitation robotics and encouraging balanced exploration of current challenges and future opportunities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.