Evidence map›Paper›PMID 41977932›Full record

ArticleSensors (Basel, Switzerland)2026

Estimation of the Knee Joint with Single-Camera Smartphone.

Michela Russo, Carlo Ricciardi, Maria Romano, Vittorio Santoriello, Alfonso Maria Ponsiglione, Francesco Amato, Maria Francesca Spadea

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
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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

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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

7 authors.

Michela RussoDepartment of Chemical, Material and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0002-5874-6441
Carlo RicciardiDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0001-7290-6432
Maria RomanoDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0003-1133-1115
Vittorio SantorielloDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0002-3168-4682
Alfonso Maria PonsiglioneDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0003-1346-515X
Francesco AmatoDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0002-9053-3139
Maria Francesca SpadeaInstitute of Biomedical Engineering, Karlsruhe Institute of Technology, 76131 Baden-Württemberg, Germany.ORCID 0000-0002-5339-9583

Funding

European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on "Telecommunications of the Future" PE00000001 - program "RESTART"
6 · The paper itself

Abstract

(1) Background: Gait analysis provides quantitative information on walking patterns and has proven invaluable for assessing motor function in rehabilitation programmes. A markerless motion capture system combining computer vision techniques provides low-cost, real-time, portable gait analysis. (2) Methods: The kinematics of the knee and ankle of twenty-seven healthy volunteers were assessed using a single smartphone camera combined with the MediaPipe human pose estimation framework. The system was validated using the OPAL wearable sensor system by APDM Wearable Technologies. (3) Results: Findings showed close correspondence between the two systems for knee kinematics showing a mean absolute error of 4.10° ± 2.32° and 3.15° ± 3.10° for right and left knee flexion, respectively, and a mean absolute error of 2.30° ± 2.01° and 3.12° ± 2.63° for right and left knee extension, respectively. The mean absolute error for right and left knee range of motion was found to be 4.55° ± 3.12° and 4.15° ± 3.01°, respectively. Moreover, Bland-Altman plots indicated minimal bias (average 0.6 for flexion, average 0.47 for the extension, and 0.30 for the range of motion) and excellent correlation for knee flexion bilaterally (0.916 and 0.845 for the right and left side, respectively), with slightly lower but still satisfactory agreement for knee extension (0.862 and 0.845 for the right and left side, respectively). Conversely, ankle measurements revealed poor concordance: dorsiflexion and range of motion presented significant differences and systematic errors, while plantarflexion showed no statistical difference but weak correlation. (4) Conclusions: This study demonstrated that combining a smartphone camera with a human pose estimation framework allows for low-cost, real-time, portable gait analysis, particularly of the knee joint.

Indexed as

Gait AnalysisKnee JointSmartphoneAdultBiomechanical PhenomenaFemaleGaitHumansMaleMotion CaptureRange of Motion, ArticularWearable Electronic Devicesgait analysismarkerless motion capturesignal processingsmartphone single-cameravideo-based analysiswearable sensors

Identifiers

PMID41977932
PMCPMC13075125

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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.