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Evaluation of Smartphone Camera Positioning on Artificial Intelligence Pose Estimation Accuracy for Exercise Detection

2026-03-05Artigo científico Acesso aberto
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dc.contributor.authorOliosi, Eduarda
dc.contributor.authorFerreira, Soraia
dc.contributor.authorGiordano, Ana Paula
dc.contributor.authorViveiros, Guilherme
dc.contributor.authorParraca, José
dc.contributor.authorPereira, Paulo
dc.contributor.authorGuede-Fernández, Federico
dc.contributor.authorAzevedo, Salomé
dc.contributor.institutionDF – Departamento de Física
dc.contributor.institutionLIBPhys-UNL
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.pblJMIR Publications
dc.date.accessioned2026-05-15T15:37:01Z
dc.date.available2026-05-15T15:37:01Z
dc.date.issued2026-03-05
dc.descriptionPublisher Copyright: © Eduarda Oliosi, Soraia Ferreira, Ana Paula Giordano, Guilherme Viveiros, José Parraca, Paulo Pereira, Federico Guede-Fernández, Salomé Azevedo.
dc.description.abstractBackground: Artificial intelligence (AI)–driven pose estimation (PE) offers a scalable and cost-effective solution to track exercises in mobile health apps. However, occlusion, influenced by camera angle and distance, can reduce detection accuracy and repetition counting precision. The influence of smartphone positioning on these performance metrics remains underexplored in controlled studies. Objective: The study aimed to examine how smartphone camera angle (front, side, and diagonal) and distance (90 cm, 180 cm, 200 cm, and 360 cm) affect detection performance and repetition counting accuracy during push-ups and squats using AI-based PE. Methods: In this cross-sectional, within-subject study, 44 healthy university students (9 [20.5%] female participants; mean age 20.3 y, SD 0.4 y; mean BMI 23.2, SD 0.6 kg/m2) were assigned to perform either squats or push-ups. Each participant completed their assigned exercise across 12 predefined smartphone camera configurations, yielding approximately 264 squat trials (n=22) and 264 push-up trials (n=22). Each trial consisted of an average of 5 repetitions, totaling approximately 1320 repetitions per exercise. PE performance was assessed using binary classification accuracy, detection rate, and mean absolute error (MAE) for repetition counting. Generalized linear mixed-effects models evaluated classification odds, linear mixed-effects models analyzed MAE, and Tukey-adjusted post hoc tests followed significant effects. Results: The mean detection rate was 61.1% (SD 48.8%) for push-ups and 61.5% (SD 48.7%) for squats, with MAEs of 1.08 (SD 1.78) and 1.11 (SD 1.82) repetitions, respectively. Push-ups were most accurately detected from diagonal views at 90 to 180 cm (up to 85.7% detection; MAE=0.28) and least accurately from the front at 360 cm (20%; MAE=2.70). Squats performed best from a diagonal view at 200 cm (95.5%; MAE=0.05) and worst from the side at 90 cm (0%; MAE=5). Generalized linear mixed models showed that for push-ups, the front 90 cm and diagonal 360 cm views significantly reduced classification odds compared to the side 90 cm view (P=.03 and P=.04, respectively), whereas for squats, diagonal and front views significantly outperformed side views across all distances (P<.001). Post hoc tests confirmed that for push-ups, diagonal close or mid-range views had significantly lower MAEs than far front views, and for squats, diagonal and front views at 180 to 200 cm achieved the highest accuracy and lowest MAEs (P<.05). Conclusions: AI-based PE effectiveness for exercise tracking is significantly affected by smartphone positioning. Diagonal and frontal views at mid-range distances (180‐200 cm) provided the highest detection accuracy and counting precision. These findings offer actionable guidance for developers, clinicians, coaches, and users optimizing mobile health exercise monitoring.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent13
dc.format.extent395034
dc.identifier.doi10.2196/82412
dc.identifier.issn2291-5222
dc.identifier.otherPURE: 162748214
dc.identifier.otherPURE UUID: fc8063b6-3d26-4365-a86d-6e04e778d216
dc.identifier.otherScopus: 105032550506
dc.identifier.otherPubMed: 41813421
dc.identifier.otherWOS: 001709768400002
dc.identifier.otherPubMedCentral: PMC12978916
dc.identifier.urihttp://hdl.handle.net/10362/203151
dc.identifier.urlhttps://www.scopus.com/pages/publications/105032550506
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001709768400002
dc.language.isoeng
dc.peerreviewedyes
dc.subjectComputer vision
dc.subjectDigital health
dc.subjectHuman activity recognition
dc.subjectHuman pose estimation
dc.subjectMhealth
dc.subjectMobile apps
dc.subjectMobile health
dc.subjectPhysical activity
dc.subjectHealth Informatics
dc.subjectSDG 3 - Good Health and Well-being
dc.titleEvaluation of Smartphone Camera Positioning on Artificial Intelligence Pose Estimation Accuracy for Exercise Detectionen
dc.title.subtitleObservational Studyen
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage13
degois.publication.titleJMIR mHealth and uHealth
degois.publication.volume14
dspace.entity.typePublication
rcaap.rightsopenAccess

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