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Portilla , J., Rangel, E., Guaycán, L., & Martínez, F. (2024). Una arquitectura volumétrica profunda para discriminar patrones parkinsonianos desde representaciones de poses intermedias. International Journal of Psychological Research, 17(2), 84–90. https://doi.org/10.21500/20112084.7405
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The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
Resumen
La enfermedad de Parkinson (EP) es un trastorno neurodegenerativo común a nivel mundial, con más de 6.2 millones de casos registrados. El análisis de la marcha desempeña un papel fundamental en la evaluación de las anomalías motoras asociadas con esta enfermedad. Sin embargo, los métodos actuales, como sistemas basados en marcadores, son intrusivos y dependientes de expertos. Se han propuesto alternativas sin marcadores, como el análisis de secuencias de video, que tienden a proporcionar puntajes de clasificación globales y carecen de la capacidad de interpretar la cinemática articular detalladamente. Se presenta una técnica innovadora utilizando redes convolucionales volumétricas que pueden aprender patrones posturales intermedios y distinguir entre pacientes con Parkinson y sujetos control. Este enfoque utiliza activaciones de OpenPose, y luego aplica una convolución jerárquica para minimizar la clasificación. En pruebas realizadas con 14 pacientes Parkinson y 16 sujetos control, este método alcanzó una precisión del 98% en clasificación.
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Referencias
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Tolosa, E., Garrido, A., Scholz, S. W., & Poewe, W. (2021). Challenges in the diagnosis of Par-kinson’s disease. The Lancet Neurology, 20(5), 385–397. https://doi.org/10.1016/S1474-4422(21)00030-2
Varol, G., Laptev, I., & Schmid, C. (2017). Long-term temporal convolutions for action recogni-tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1510–1517. https://doi.org/10.1109/TPAMI.2017.272304
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172–186. https://doi.org/10.1109/TPAMI.2019.2929257
Dorsey, E., Sherer, T., Okun, M. S., & Bloem, B. R. (2018). The emerging evidence of the Parkin-son pandemic. Journal of Parkinson’s Disease, 8(s1), S3–S8. https://doi.org/10.3233/JPD-181474
Feigin, V. L., Vos, T., Alahdab, F., Amit, A. M. L., Bärnighausen, T. W., Beghi, E., Beheshti, M., Chavan, P. P., Criqui, M. H., Desai, R., Dhamminda Dharmaratne, S., Dorsey, E. R., Wilder Eagan, A., Elgendy, I. Y., Filip, I., Giampaoli, S., Giussani, G., Hafezi-Nejad, N., Hole, M. K., … Murray, C. J. L. (2021). Burden of neurological disorders across the US from 1990–2017: A global burden of disease study. JAMA Neurology, 78(2), 165–176. https://doi.org/10.1001/jamaneurol.2020.4152
Guayacán, L. C., & Martínez, F. (2021). Visualising and quantifying relevant Parkinsonian gait patterns using 3D convolutional network. Journal of Biomedical Informatics, 123, 103935. https://doi.org/10.1016/j.jbi.2021.103935
The Lancet. (2017). Artificial intelligence in health care: Within touching distance. The Lancet, 390(10114), 2739. https://doi.org/10.1016/S0140-6736(17)32846-5
Rovini, E., Maremmani, C., & Cavallo, F. (2017). How wearable sensors can support Parkinson’s disease diagnosis and treatment: A systematic review. Frontiers in Neuroscience, 11, 555. https://doi.org/10.3389/fnins.2017.00555
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
Tolosa, E., Garrido, A., Scholz, S. W., & Poewe, W. (2021). Challenges in the diagnosis of Par-kinson’s disease. The Lancet Neurology, 20(5), 385–397. https://doi.org/10.1016/S1474-4422(21)00030-2
Varol, G., Laptev, I., & Schmid, C. (2017). Long-term temporal convolutions for action recogni-tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1510–1517. https://doi.org/10.1109/TPAMI.2017.272304
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