Biology subjects

De luca, M.

Publications and source records attributed to De luca, M..

2 recordsLinked to original sources

bioRxiv2026

Data-driven gait cycle decomposition based on whole-body coordination dynamics

The study of human locomotion has long relied on descriptive frameworks of the gait cycle, which have provided essential insights into the functional phases of walking and their underlying biomechanical demands. While these models remain highly informative, they are largely based on observational analyses and may not fully capture the continuous, global coordination that characterizes human movement. The present study proposes an integrated framework to study whole-body coordination. This framework combines network theory with non-negative matrix factorization (NNMF) to treat gait as a dynamic system of coordinated joint interactions. Using three-dimensional kinematic data from 60 healthy subjects, we constructed a representation of whole-body coordination across time, named "dynamic kinectome". It was then decomposed using NNMF to extract spatial patterns of joint coordination and their corresponding temporal activations, allowing for an interpretable characterization of locomotor organization while preserving physiological meaning. Our analysis extracted six robust, highly consistent, and symmetrical coordination patterns across participants, effectively capturing the primary functional subtasks of locomotion. Rather than challenging classical phase descriptions, these findings enrich them by showing how coordination emerges as a continuous, often proactive process that can extend across conventional phase boundaries and systematically integrates the upper limbs for dynamic stability. Overall, this study provides a holistic, data-driven perspective on human locomotion, offering a promising basis for future investigations in motor control and may contribute to the development of sensitive biomarkers for clinical and rehabilitative applications.

bioengineering↗

bioRxiv2025

Artificial Intelligence for automatic movement recognition: a network-based approach

Introductionautomatic movement recognition is often used to support various fields such as clinical, sports, and security. To date, there is a lack of a classification feature that is both interpretable and not movement-specific, characteristics that would enhance generalization and adaptability. Previous studies on motion analysis have shown that coordination properties extracted from full-body movement using network theory can describe specific movement characteristics, making coordination a potential feature for classification. Methodstherefore, we leveraged kinematic data from 168 individuals performing 30 different movements, published in an online dataset. Using network theory, we reduced data dimensionality, obtaining a coordination matrix called the kinectome. By applying support vector machine algorithms, we compared the classification performance of the kinectome with that of principal component analysis, used as an alternative data reduction method. Resultsthe classification accuracy of the kinectome (0.99 {+/-} 0.01) was significantly higher (pFDR < 0.001) than that of PCA (0.96 {+/-} 0.04). Moreover, unlike PCA, the kinectome demonstrated resilience to data loss, robustness to derived measures, independence from the classification algorithm, and clear interpretability of features. Discussionour results suggest that kinnectome-based features could capture interpretable changes between movements that could pave the way to new automatic movement recognition approaches dedicated to a wide range of applications, in particular sport training and physical readaptation, and designed for non-data scientists experts.

bioengineering↗