Evidence map›Paper›PMID 40819657›Full record

ArticleAnnals of rehabilitation medicine2025

Wearable Robots for Rehabilitation and Assistance of Gait: A Narrative Review.

Jun Min Cha, Juntaek Hong, Jehyun Yoo, Dong-Wook Rha

Abstract read
In one paragraph

Article in Annals of rehabilitation medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. [Personalized lower-limb gait assessment method based on musculoskeletal modeling and machine learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jun Min ChaDepartment and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Korea.
Juntaek HongDepartment and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Korea.
Jehyun YooDepartment of Rehabilitation Medicine, Gachon University Gil Medical Center, Incheon, Korea.
Dong-Wook RhaDepartment and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable robotic exoskeletons have emerged as promising technologies for enhancing gait rehabilitation and providing mobility assistance in individuals with neurological and musculoskeletal disorders. This narrative review summarizes recent advances in wearable robots-including both rigid exoskeletons and soft exosuits-and evaluates their clinical application across diverse conditions such as stroke, spinal cord injury, cerebral palsy, and Parkinson's disease. For rehabilitation purposes, these devices enable repetitive, task-specific gait training that promotes motor learning, reduces therapist burden, and facilitates improvements in walking speed, balance, and endurance. Rigid exoskeletons provide substantial joint support and are particularly effective for patients with severe gait impairments, whereas soft exosuits offer lightweight assistance suited to individuals with milder deficits or fatigue, albeit with limited capacity to deliver high-torque support. Beyond rehabilitation, wearable robots are increasingly used as assistive devices to compensate for permanent gait limitations and restore mobility in daily life. However, widespread clinical adoption remains constrained by several challenges, including a lack of standardized protocols; limited evidence from large-scale, multicenter studies; and practical issues such as device weight, comfort, and ease of use in community settings. Recent developments-such as adaptive control algorithms, volition-adaptive assistance, and artificial intelligence integration-are addressing these barriers by enabling more personalized and responsive support. With continued research investment, user-centered design, and supportive policies, wearable exoskeletons hold considerable potential to improve independence, participation, and quality of life for individuals across a broad spectrum of mobility impairments.

Indexed as

Assistive devicesMobility limitationRehabilitationRobotic exoskeletonWearable devices

Identifiers

PMID40819657
PMCPMC12411867

What OpenQuestion holds

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Registered trials

None linked

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.