Authors:
E. Medina
1
;
H. de Rosario
1
;
J. Olaso
1
;
A. Ballester
1
;
J. Navarro
1
and
A. Page
2
Affiliations:
1
Instituto de Biomecánica de Valencia, Spain
;
2
Universidad de Politécnica Valencia, CIBER de Bioingeniería and Biomateriales y Nanomedicina (CIBER-BBN), Spain
Keyword(s):
Running, Pronator, Functional Data Analysis, Functional Principal Component Analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Telecommunications
Abstract:
The correct classification of runners according to their gait patterns is a relevant issue for the design of sports footwear. Specifically, the classification of runners as neutral, pronators, and supinators is a problem that is not yet fully solved, and requires expert observation, since current models based on the automatic processing of kinematic measures are very limited. This work proposes a method based on Functional Data Analysis (FDA) for automatically describing the morphology of the curves that represent ankle movement patterns. By Functional Analysis of Principal Components, the information contained in each data stream is reduced to a small set of variables, that allows an efficient classification of subjects.