Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls
Abstract This study evaluated the discriminative potential of a machine learning model using movement features during functional tasks to distinguish between patients with non-traumatic chronic neck pain and asymptomatic controls. The study included patients with chronic mechanical neck pain and asy...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-06331-z |
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| author | Filippo Moggioli Óscar Rodríguez-López Elena Bocos-Corredor Constantino Antonio García Sonia Liébana Tomás Pérez-Fernández Cristina Sánchez Susan Armijo-Olivo José Angel Santos-Paz Aitor Martín-Pintado-Zugasti |
| author_facet | Filippo Moggioli Óscar Rodríguez-López Elena Bocos-Corredor Constantino Antonio García Sonia Liébana Tomás Pérez-Fernández Cristina Sánchez Susan Armijo-Olivo José Angel Santos-Paz Aitor Martín-Pintado-Zugasti |
| author_sort | Filippo Moggioli |
| collection | DOAJ |
| description | Abstract This study evaluated the discriminative potential of a machine learning model using movement features during functional tasks to distinguish between patients with non-traumatic chronic neck pain and asymptomatic controls. The study included patients with chronic mechanical neck pain and asymptomatic controls. Inertial sensors analyzed kinematics during two tasks: elevated weight transfer task and water drinking. Movement was characterized using fifteen features, incorporated into machine learning models to assess how movement patterns relate to patient condition. Features included range of motion, peak velocity, smoothness, spatiotemporal inter-plane coordination, energy distribution by frequencies, and movement heterogeneity. Fifty-three patients with neck pain (36.27 ± 14.3 years; 14 men and 39 women) and 53 asymptomatic participants (35.43 ± 14.65 years; 32 men and 21 women) completed the study. Permutation tests evaluated the discriminative potential of neck movement features between groups. The elevated weight transfer task showed significant discriminative power (P = .0337 ± .0239; Accuracy = 0.618 ± 0.02), while the water drinking task did not (P = .215 ± .202). Movement heterogeneity was the most important discriminative feature, with chronic neck pain patients showing higher movement intensity fluctuations over time. Although the elevated weight transfer task showed statistically significant discriminative potential between asymptomatic individuals and those with chronic neck pain, the models correctly classified participants only 61.8% of the time. This result questions the potential of kinematic analysis to identify patients with chronic neck pain. Future research should investigate these models during more challenging tasks in samples of patients with higher neck pain intensity or disability levels. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b65a9dca31384a20a1698653bbda31972025-08-20T03:45:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-06331-zMachine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controlsFilippo Moggioli0Óscar Rodríguez-López1Elena Bocos-Corredor2Constantino Antonio García3Sonia Liébana4Tomás Pérez-Fernández5Cristina Sánchez6Susan Armijo-Olivo7José Angel Santos-Paz8Aitor Martín-Pintado-Zugasti9Department of Physical Therapy, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartment of Physical Therapy, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartment of Physical Therapy, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartment of Physical Therapy, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartment of Physical Therapy, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeFaculty of Business and Social Sciences, University of Applied Sciences OsnabrückDepartamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeDepartment of Physical Therapy, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización MontepríncipeAbstract This study evaluated the discriminative potential of a machine learning model using movement features during functional tasks to distinguish between patients with non-traumatic chronic neck pain and asymptomatic controls. The study included patients with chronic mechanical neck pain and asymptomatic controls. Inertial sensors analyzed kinematics during two tasks: elevated weight transfer task and water drinking. Movement was characterized using fifteen features, incorporated into machine learning models to assess how movement patterns relate to patient condition. Features included range of motion, peak velocity, smoothness, spatiotemporal inter-plane coordination, energy distribution by frequencies, and movement heterogeneity. Fifty-three patients with neck pain (36.27 ± 14.3 years; 14 men and 39 women) and 53 asymptomatic participants (35.43 ± 14.65 years; 32 men and 21 women) completed the study. Permutation tests evaluated the discriminative potential of neck movement features between groups. The elevated weight transfer task showed significant discriminative power (P = .0337 ± .0239; Accuracy = 0.618 ± 0.02), while the water drinking task did not (P = .215 ± .202). Movement heterogeneity was the most important discriminative feature, with chronic neck pain patients showing higher movement intensity fluctuations over time. Although the elevated weight transfer task showed statistically significant discriminative potential between asymptomatic individuals and those with chronic neck pain, the models correctly classified participants only 61.8% of the time. This result questions the potential of kinematic analysis to identify patients with chronic neck pain. Future research should investigate these models during more challenging tasks in samples of patients with higher neck pain intensity or disability levels.https://doi.org/10.1038/s41598-025-06331-zAssessment technologyBiomechanicsKinematicsNeck painFunctional tasksMachine learning |
| spellingShingle | Filippo Moggioli Óscar Rodríguez-López Elena Bocos-Corredor Constantino Antonio García Sonia Liébana Tomás Pérez-Fernández Cristina Sánchez Susan Armijo-Olivo José Angel Santos-Paz Aitor Martín-Pintado-Zugasti Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls Scientific Reports Assessment technology Biomechanics Kinematics Neck pain Functional tasks Machine learning |
| title | Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls |
| title_full | Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls |
| title_fullStr | Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls |
| title_full_unstemmed | Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls |
| title_short | Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls |
| title_sort | machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls |
| topic | Assessment technology Biomechanics Kinematics Neck pain Functional tasks Machine learning |
| url | https://doi.org/10.1038/s41598-025-06331-z |
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