Estimation of joint angles in the upper limbs using a smartphone.
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13777Keywords:
Biomedical signal measurement and processing, Biomedical and medical imaging, image processing, visualization, Rehabilitation engineering and healthcare delivery, Robot perception and sensing, Machine and deep learning for system identificationAbstract
The clinical assessment of spasticity in stroke patients relies on the subjective judgement of specialists. With the aim of objectively quantifying joint mobility, this study validates the BlazePose model—an AI-based model integrated into the MediaPipe framework—to estimate four joint angles of the upper limb. The accuracy of this model is compared with the OptiTrack motion capture system. To this end, an experimental protocol was designed and shoulder abduction and flexion, elbow flexion and wrist flexion were assessed. The results show that the first three angles achieve a correlation coefficient r>= 0.94 with respect to the reference system. Shoulder abduction yields the best result, with a mean absolute error of 8.3º, whilst the elbow shows a systematic bias of +18.9º. The main conclusion of the study is that the clinical viability of this solution is contingent upon specific calibration protocols.
References
Béseler Soto, M. R., Montes García, J., Máñez Añón, I., 2020. Espasticidad tras ictus: ¿la edad es un factor de riesgo? Estudio observacional de la espasticidad en pacientes neurovasculares en una serie retrospectiva de dos centros. Revista Española de Geriatría y Gerontología 55 (5), 258–265. DOI: 10.1016/j.regg.2020.04.004
di Biase, L., Pecoraro, P. M., Bugamelli, F., 2025. Ai video analysis in parkinson’s disease: A systematic review of the most accurate computer visión tools for diagnosis, symptom monitoring, and therapy management. Sensors 25 (20).
Doussoulin, A., Rivas, C., Bacco, J., Sepúlveda, P., Carvallo, G., Gajardo, C., Soto, A., Rivas, R., nov 2020. Prevalence of spasticity and postural patterns in the upper extremity post stroke. Journal of Stroke and Cerebrovascular Diseases 29 (11), 105253. DOI: 10.1016/j.jstrokecerebrovasdis.2020.105253
Jeyasingh-Jacob, J., Crook-Rumsey, M., Shah, H., Joseph, T., Abulikemu, S., Daniels, S., Sharp, D. J., Haar, S., Aug 2024. Markerless motion capture to quantify functional performance in neurodegeneration: Systematic review. JMIR Aging 7, e52582. DOI: 10.2196/52582
Knippenberg, E., Verbrugghe, J., Lamers, I., Palmaers, S., Timmermans, A., Spooren, A., jun 2017. Markerless motion capture systems as training device in neurological rehabilitation: a systematic review of their use, application, target population and efficacy. Journal of NeuroEngineering and Rehabilitation 14 (1), 61. DOI: 10.1186/s12984-017-0270-x
Latreche, A., Kelaiaia, R., Chemori, A., Kerboua, A., 2023. Reliability and validity analysis of mediapipe-based measurement system for some human rehabilitation motions. Measurement 214, 112826. DOI: https://doi.org/10.1016/j.measurement.2023.112826
Ministerio de Sanidad, 2024. Estrategia en ictus del sistema nacional de salud. actualización 2024. Tech. rep., Ministerio de Sanidad, Gobierno de España, actualización aprobada por el Consejo Interterritorial del Sistema Nacional de Salud el 5 de abril de 2024. NIPO en línea: 133-24-118-7. URL: https://cpage.mpr.gob.es
Nagymate, G., Kiss, R., 07 2018. Application of optitrack motion capture systems in human movement analysis a systematic literature review. Recent Innovations in Mechatronics 5. DOI: 10.17667/riim.2018.1/13
Opiña, P., Fajardo, A., 04 2024. Real-time pose estimation for mobile devices: A review. International Journal of Multidisciplinary Research and Analysis 07. DOI: 10.47191/ijmra/v7-i04-44
Sauerzopf, L., Panduro, C., Luft, A., Kühnis, B., Gavagnin, E., Unger, T., Awai, C., Sch¨onhammer, J., Degenfellner, J., Spiess, M., 12 2024. Evaluating inter- and intra-rater reliability in assessing upper limb compensatory movements post-stroke: creating a ground truth through video analysis? Journal of NeuroEngineering and Rehabilitation 21. DOI: 10.1186/s12984-024-01506-7
Scott, B., Seyres, M., Philp, F., Chadwick, E. K., Blana, D., may 2022. Healthcare applications of single camera markerless motion capture: a scoping review. PeerJ 10, e13517. DOI: 10.7717/peerj.13517
Unger, T., Kühnis, B., Sauerzopf, L., Spiess, M. R., de Spindler, A., Luft, A. R., Easthope Awai, C., Sch¨onhammer, J. G., Gavagnin, E., 2025. Using deep learning to detect upper limb compensation in individuals poststroke using consumer-grade webcams—a feasibility study. Frontiers in Medicine Volume 12 - 2025.
Urriola, G. J. M., Trejos, C. I. P., Ferre, M., 2023. Comparison of intrusive and non-intrusive motion capture technologies for upper body kinematic analysis. Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development”. URL: https://api.semanticscholar.org/CorpusID:260985344
Wagh, V., Scott, M.W., Andrushko, J.W., Jones, C. B., Larssen, B. C., Boyd, L. A., Kraeutner, S. N., nov 2025. Using MediaPipe to track upper-limb reaching movements after stroke: a proof-of-principle study. Journal of NeuroEngineering and Rehabilitation 22 (1), 268. DOI: 10.1186/s12984-025-01808-4
Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., Kehtarnavaz, N., Shah, M., 2023. Deep learning-based human pose estimation: A survey 56 (1).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Carlos Montero Pascual, Eva Portillo, Asier Zubizarreta, Gil De Sousa, Itziar Cabanes
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
