Gait event detection using kinematic data in children with bilateral spastic cerebral palsy
Por:
Gómez-Pérez C, Martori JC, Puig Diví A, Medina Casanovas J, Vidal Samsó J and Font-Llagunes JM
Publicada:
1 dic 2021
Ahead of Print:
1 oct 2021
Resumen:
Background: Ground reaction forces are the gold standard for detecting gait events, but they are not always applicable in cerebral palsy. Ghoussayni's algorithm is an event detection method based on the sagittal plane velocity of heel and toe markers. We aimed to evaluate whether Ghoussayni's algorithm, using two different thresholds, was a valid event detection method in children with bilateral spastic cerebral palsy. We also aimed to define a new adaptation of Ghoussayni's algorithm for detecting foot strike in cerebral palsy, and study the effect of event detection methods on spatiotemporal parameters. Methods: Synchronized kinematic and kinetic data were collected retrospectively from 16 children with bilateral spastic cerebral palsy (7 males and 9 females; age 8.9 +/- 2.7 years) walking barefoot at self-selected speed. Gait events were detected using methods: 1) ground reaction forces, 2) Ghoussayni's algorithm with a threshold of 0.5 m/s, and 3) Ghoussayni's algorithm with a walking speed dependent threshold. The new adaptation distinguished how foot strikes were performed (heel and/or toe) comparing the timing when the foot markers velocities fell below the threshold. Differences between the three methods, and between spatiotemporal parameters calculated from the two Ghoussayni's thresholds were analyzed. Findings: There were statistically significant (P < 0.05) differences between methods 1 and 3, and between some spatiotemporal parameters calculated from methods 2 and 3. Ghoussayni's algorithm showed better performance for foot strike than for toe off. Interpretation: Ghoussayni's algorithm using 0.5 m/s is valid in children with bilateral spastic cerebral palsy. Event detection methods affect spatiotemporal parameters.
Filiaciones:
Gómez-Pérez C:
Research group on Methodology, Methods, Models and Outcomes of Health and Social Sciences (M(3)O), Faculty of Health Sciences and Welfare, Centre for Health and Social Care Research (CESS), University of Vic-Central University of Catalonia (UVIC-UCC), C. Sagrada Família 7, 08500 Vic, Spain
Martori JC:
Data Analysis and Modeling Research Group, Department of Economics and Business, Faculty of Business and Communication Studies, University of Vic - Central University of Catalonia (UVic-UCC), C. Sagrada Família 7, 08500 Vic, Spain
Puig Diví A:
Blanquerna School of Health Sciences - Ramon Llull University, C. Padilla 326, 08025 Barcelona, Spain
Medina Casanovas J:
Institut Guttmann, Hospital de Neurorehabilitació, Camí de Can Ruti, 08916 Badalona, Spain
Universitat Autònoma de Barcelona, Plaça Cívica, 08193 Cerdanyola del Vallès, Spain
Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Carretera Canyet, 08916 Badalona, Spain
Vidal Samsó J:
Institut Guttmann, Hospital de Neurorehabilitació, Camí de Can Ruti, 08916 Badalona, Spain
Universitat Autònoma de Barcelona, Plaça Cívica, 08193 Cerdanyola del Vallès, Spain
Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Carretera Canyet, 08916 Badalona, Spain
Font-Llagunes JM:
Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain
Institut de Recerca Sant Joan de Déu, C. Santa Rosa 39-57, 08950 Esplugues de Llobregat, Spain
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