PDF
FLIP
How to Cite
Portilla , J., Rangel, E., Guayacán, L., & Martínez, F. (2024). A Volumetric Deep Architecture to Discriminate Parkinsonian Patterns from Intermediate Pose Representations. International Journal of Psychological Research, 17(2), 84–90. https://doi.org/10.21500/20112084.7405
More Citation Formats
License terms
▼
The work that is sent to this journal must be original, not published or sent to be published elsewhere; and if it is accepted for publication, authors will agree to transfer copyright to International Journal of Psychological Research.
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.
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.
Abstract
Parkinson’s disease (PD) is a common neurodegenerative disorder worldwide, with over 6.2 million registered cases. Gait analysis plays a fundamental role in evaluating motor abnormalities associated with this disease. However, current methods, such as marker-based systems, are intrusive and expert-dependent. Markerless alternatives, like video sequence analysis, have been proposed, but they tend to provide overall classification scores and lack the ability to interpret joint kinematics in detail. An innovative technique is presented using volumetric convolutional networks that can learn intermediate postural patterns and distinguish between Parkinson’s patients and control subjects. This approach utilizes OpenPose activations and then applies hierarchical convolution to minimize classification. In tests conducted with 14 Parkinson’s patients and 16 control subjects, this method achieved a classification accuracy of 98%.
References
Baker, R. (2006). Gait analysis methods in rehabilitation. Journal of NeuroEngineering and Reha-bilitation, 3(1), 1–10. https://doi.org/10.1186/1743-0003-3-1
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
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
Downloads
Download data is not yet available.
Cited by
