Kinematic network of joint motion provides insight on gait coordination: An observational study on Parkinson's disease
Emahnuel Troisi Lopez,
Marianna Liparoti,
Roberta Minino,
Antonella Romano,
Arianna Polverino,
Anna Carotenuto,
Domenico Tafuri,
Giuseppe Sorrentino,
Pierpaolo Sorrentino
Affiliations
Emahnuel Troisi Lopez
Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
Marianna Liparoti
Department of Philosophical, Pedagogical and Quantitative-Economics Sciences, University of Studies G. D'Annunzio, Chieti-Pescara, Italy
Roberta Minino
Department of Medical, Human Movement and Well-being Sciences, University of Naples “Parthenope”, Naples, Italy
Antonella Romano
Department of Medical, Human Movement and Well-being Sciences, University of Naples “Parthenope”, Naples, Italy
Arianna Polverino
ICS Maugeri Hermitage, Naples, Italy
Anna Carotenuto
Department of Neurology, Cardarelli Hospital, Naples, Italy
Domenico Tafuri
Department of Medical, Human Movement and Well-being Sciences, University of Naples “Parthenope”, Naples, Italy
Giuseppe Sorrentino
Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy; Department of Economics, Law, Cybersecurity and Sport Sciences, University of Naples “Parthenope”, Nola, Italy; Corresponding author. Department of Economics, Law, Cybersecurity and Sport Sciences, University of Naples “Parthenope”, Nola, Italy.
Pierpaolo Sorrentino
Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy; Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France; Department of Biomedical Sciences, University of Sassari, Sassari, Italy
The analysis of gait kinematics requires to encode and collapse multidimensional information from multiple anatomical elements. In this study, we address this issue by analyzing the joints' coordination during gait, borrowing from the framework of network theory. We recruited twenty-three patients with Parkinson's disease and twenty-three matched controls that were recorded during linear gait using a stereophotogrammetric motion analysis system. The three-dimensional angular velocity of the joints was used to build a kinematic network for each participant, and both global (average whole-body synchronization) and nodal (individual joint synchronization, i.e., nodal strength) were extracted. By comparing the two groups, the results showed lower coordination in patients, both at global and nodal levels (neck, shoulders, elbows, and hips). Furthermore, the nodal strength of the left elbow and right hip in the patients, as well as the average joints' nodal strength were significantly correlated with the clinical motor condition and were predictive of it. Our study highlights the importance of integrating whole-body information in kinematic analyses and the advantages of using network theory. Finally, the identification of altered network properties of specific joints, and their relationship with the motor impairment in the patients, suggests a potential clinical relevance for our approach.
