Evidence map›Paper›PMID 42817401›Full record

ArticleSensors (Basel, Switzerland)2026

Application of Deep Learning-Based Markerless Pose Estimation in a 3D Exergame System for Knee Osteoarthritis Exercise Management: Development and Preliminary Evaluation.

Xingye Cheng, Xi Gao, Wenqi Liang, Rebecca M Meiring, Yanxin Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Xingye ChengSchool of Exercise, Sport and Rehabilitation Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.
Xi GaoSchool of Exercise, Sport and Rehabilitation Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.
Wenqi LiangSchool of Exercise, Sport and Rehabilitation Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.
Rebecca M MeiringSchool of Exercise, Sport and Rehabilitation Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.ORCID 0000-0002-8472-4940
Yanxin ZhangSchool of Exercise, Sport and Rehabilitation Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.ORCID 0000-0002-7638-1669

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Home-based exercise management for people with knee osteoarthritis (KOA) is often limited by insufficient movement supervision, individualization, and progression support. This study described the development and preliminary evaluation of a KOA-specific 3D exergame exercise management system. The system combined markerless motion analysis using a conventional RGB camera with biomechanical analysis, concurrent avatar-based guidance, task-specific feedback, adaptive difficulty adjustment, pain monitoring, therapist override, and training records. Exercise tasks addressed lower-limb strength, balance, range of motion, and toe-in or toe-out gait retraining. Five physiotherapists conducted a preliminary evaluation of the prototype through technology-acceptance and usability questionnaires and semi-structured interviews. They reported generally favorable perceptions and identified clinically relevant exercise content, low sensing burden, comprehensive feedback, and training-progress monitoring as potential strengths. Concerns included fall risk during demanding tasks, limited individualization of some parameters, and insufficient safeguards for unsupervised use. Selected TDPT-derived kinematic measures were also compared with synchronized marker-based motion capture using a public dataset comprising 18 squat cycles from six participants. Agreement varied by measure: waveform associations were strong for functional hip flexion, knee flexion, trunk inclination, and frontal-plane hip abduction/adduction, whereas absolute errors ranged from 6.59° to 20.00° and the foot-orientation proxy showed negligible correlation. These findings provide preliminary clinician-informed and task-specific technical evidence for the prototype but do not establish patient usability or clinical effectiveness. Further technical validation, safety refinement, and supervised evaluation with people with KOA are required before routine or independent home use can be considered.

Indexed as

Deep LearningExercise TherapyOsteoarthritis, KneeVideo GamesBiomechanical PhenomenaFemaleGaitHumansMaleMiddle AgedMotion CaptureRange of Motion, Articular3D exergameknee osteoarthritispose estimation algorithmrehabilitation

Identifiers

PMID42817401
PMCPMC13611215

What OpenQuestion holds

Textmetadata
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.