Evidence map›Paper›PMID 42548713›Full record

SynthesisFrontiers in rehabilitation sciences2026

Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.

Ping Ye, Yu Li, Mengjian Qu, Jing Liu, Xiangqing Lu, Wenhui Cao, Rong Wang, Yaping Wan, Tao Zhu, Jun Zhou

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in rehabilitation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

10 authors.

Ping YeSchool of Computer Science, University of South China, Hengyang, China.
Yu LiDepartment of Rehabilitation, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Mengjian QuDepartment of Rehabilitation, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Jing LiuDepartment of Rehabilitation, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Xiangqing LuSchool of Computer Science, University of South China, Hengyang, China.
Wenhui CaoSchool of Computer Science, University of South China, Hengyang, China.
Rong WangSchool of Nursing, University of South China, Hengyang, China.
Yaping WanSchool of Computer Science, University of South China, Hengyang, China.
Tao ZhuSchool of Computer Science, University of South China, Hengyang, China.
Jun ZhouDepartment of Rehabilitation, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The digital transformation of rehabilitation training has become a public health imperative driven by a global demand that outstrips professional medical resources and is compounded by a deficit in public rehabilitation literacy. As traditional hospital-centric models reach their scalability limits, there is a critical necessity for accessible home-based care solutions to ensure patients do not miss optimal recovery windows. To evaluate computer vision as a potential solution, this paper conducts a systematic review following the PRISMA 2020 reporting framework. We identify that its clinical migration faces a profound "Paradigm Gap" across three critical domains which this study aims to address: (1) the

Indexed as

action quality assessmentbiomechanical digital twincomputer visiongenerative AIphysical rehabilitationtele-rehabilitation

Identifiers

PMID42548713
PMCPMC13429716

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.