Numerous studies use numerical variables of neck movement to predict the level of severity of a pathology. However, the correlation between these numerical variables and disability levels is low, less than 0.4 in the best cases, even less in subjects with nonspecific neck pain. This work aims to use Functional Data Analysis (FDA), in particular scalar-on-function regression, to predict the Neck Disability Index (NDI) of subjects with nonspecific neck pain using the complete movement as predictors. Several functional regression models have been implemented, doubling the multiple correlation coefficient obtained when only scalar predictors are used. The best predictive model considers the angular velocity curves as a predictor, obtaining a multiple correlation coefficient of 0.64. In addition, functional models facilitate the interpretation of the relationship between the kinematic curves and the NDI since they allow identifying which parts of the curves most influence the differences in the predicted variable. In this case, the movement’s braking phases contribute to a greater or lesser NDI. So, it is concluded that functional regression models have greater predictive capacity than usual ones by considering practically all the information in the curve while allowing a physical interpretation of the results.

Early detection of knee osteoarthritis (KOA) is essential to improve treatment outcomes and reduce its long-term impact. However, early diagnosis of KOA (EKOA) remains difficult due to the absence of standardised criteria and the subtle or intermittent nature of early symptoms. This study evaluates the utility of Functional Logistic Regression (FLR) as a robust and efficient classifier that integrates clinical data and biomechanical signals–specifically, ground reaction force (GRF) curves represented through Functional Data Analysis—to support EKOA diagnosis. Fifty-six individuals with knee pain were assessed; 25 were diagnosed with EKOA based on Mahmoudian’s clinical criteria, and 31 were classified as healthy. Anthropometric data and pain scores (Visual Analogue Scale, VAS) were collected, and GRF signals in three directions (vertical, anteroposterior, and mediolateral) were recorded during walking trials. A FLR model was developed by combining GRF curves, anthropometric measurements, and VAS scores. The mediolateral GRF component showed the strongest discriminative power. The model that combined biomechanical and clinical variables outperformed models using either data source alone, achieving higher accuracy and sensitivity while maintaining parsimony and robustness. Functional Logistic Regression offers key advantages over classical logistic models based on scalar features and black-box machine learning approaches: it allows the direct use of biomechanical signals without prior discretisation and reduces the risk of overfitting while preserving statistical rigour. These results support FLR as a robust and efficient method for early KOA classification based on combined clinical and biomechanical data.

We have analyzed the relationship between disability and neck flexion-extension kinematics in non-specific neck pain subjects. A functional approach is used considering the angle, velocity, and acceleration curves. Different regression models have been fitted for each time in order to obtain these curves using scalar predictors such as the Neck Disability Index (NDI), age, sex, and neck length. In addition to classical regression, a limma (Linear Models for Microarray Data) model has been used, which improves the fit by modifying the estimation of the variances of the different fits using an empirical Bayes approach. As point-by-point adjustments are performed, this introduces a multiple comparison problem, and the corresponding p -values have to be adjusted in order to control the false discovery rate (FDR). In particular, a Benjamini-Hochberg method was used. The results show significant differences between raw and adjusted p -values for all variables, so spurious results were detected, e.g., the effect of neck length on velocity and acceleration curves. Differences between usual multiple linear regressions and the modified fits using the limma method (limma models) are minor, with a slight decrement of p -values in limma models. Once the p -values are adjusted, none of the variables analyzed significantly affects the angular curves. In contrast, NDI and age affect velocity and acceleration curves. Furthermore, the study of p -values throughout the movement shows that velocity and acceleration curves provide complementary information, so they should be used together in neck kinematics studies. Graphical

Las lesiones cervicales por accidentes sin impacto representan un creciente problema de salud, especialmente con la adopción de frenado automático y vehículos autónomos. La columna cervical es vulnerable a la deceleración brusca, donde la diferencia de inercia entre la cabeza y el torso genera un efecto latigazo que provoca daños musculoesqueléticos. Aunque existen estudios biomecánicos del comportamiento cervical utilizando modelos físicos y computacionales, la falta de biofidelidad limita su precisión, y los ensayos con voluntarios son escasos y no representan la diversidad poblacional. La mayoría se centra en el modelo masculino promedio, dejando una brecha significativa en la comprensión del riesgo no sólo para las mujeres sino también para otros subgrupos masculinos.El cinturón de seguridad desempeña un papel crucial en la dinámica del movimiento cabeza-torso, pero algunos estudios sugieren que podría incrementar el riesgo de lesiones cervicales a baja velocidad. La falta de investigaciones con voluntarios sin sistema de retención añade incertidumbre sobre su eficacia en este escenario, especialmente en transporte público (tren, metro o autobús), donde es común viajar sin sujeción. Estos hallazgos podrían ser relevantes para el diseño de vehículos autónomos que podrían prescindir de cinturones a baja velocidad.En respuesta a estas limitaciones, se propone un protocolo experimental con voluntarios de ambos sexos y diversas características antropométricas, evaluando situaciones con y sin cinturón. El objetivo es ampliar el conocimiento sobre seguridad vial, considerando variables como edad, género, peso y perímetro corporal, que influyen en la eficacia del cinturón. Las pruebas incluirán ensayos experimentales de baja deceleración y simulaciones a alta deceleración, así como estudios sobre la conciencia del pasajero frente al relajamiento en el instante de frenado.El análisis cinemático se realizará mediante marcadores, videoanálisis y sensores inerciales y de electromiografía para evaluar el movimiento y las fuerzas en distintas regiones anatómicas. Esto permitirá identificar patrones de lesión diferenciados por género, proporcionando criterios más precisos para mejorar la seguridad vial en futuros desarrollos tecnológicos y medios de transporte actuales.

Background : The Neck Disability Index (NDI) is widely used to assess neck pain-related disability. It is commonly combined with objective measures, particularly range of motion and peak velocity, but these variables usually show weak correlations with patient-reported outcomes. Functional data analysis (FDA) makes it possible to analyze complete kinematic waveforms and provides tools to identify relevant motion features most related to disability. Objective : This study aimed to examine whether scalar-on-function regression of cervical angular velocity curves can predict NDI scores in patients with nonspecific neck pain and which motion variables and sub-phases contribute most to the prediction. Methods : We analyzed 56 recordings from 28 patients, each paired with an NDI questionnaire, which were collected over two sessions. Cervical flexion–extension, lateral flexion, and axial rotation velocities were processed using functional principal component analysis to reduce dimensionality while retaining the main modes of variation. Univariate and bivariate scalar-on-function regression models were estimated. Model selection was based on a procedure that combined the Akaike Information Criterion, Bayesian Information Criterion, and measures of goodness-of-fit, ensuring a balance between model simplicity and predictive accuracy. Comparisons were made using non-functional regressions, including demographic and anthropometric variables. Results : The bivariate flexion–extension plus lateral flexion model showed the best performance (r = 0.677), which was clearly higher than that of the non-functional regressions (r ≤ 0.391). The coefficient functions indicated that the acceleration and deceleration phases were the most informative in explaining variability in NDI scores. The inclusion of anthropometric variables did not improve model performance. Conclusions : Functional regression of velocity curves improves the prediction of NDI scores and highlights specific phases of movement that are clinically relevant. Larger studies are required to confirm these findings and assess their clinical applicability.