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Authors: Marta S. Santos 1 ; Vera Moniz-Pereira 2 ; André Lourenço 3 ; Ana Fred 4 and António P. Veloso 2

Affiliations: 1 Instituto Telecomunicações, Portugal ; 2 Univ Tecn Lisboa, Portugal ; 3 Instituto de Telecomunicações and Instituto Superior de Engenharia de Lisboa, Portugal ; 4 Instituto de Telecomunicações, Portugal

Keyword(s): Functional Fitness Level, Elderly Population, Clustering, Kinematic and Kinetic Parameters, Feature Selection.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biosignal Acquisition, Analysis and Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Locomotor tasks characterization plays an important role in trying to improve the quality of life of a growing elderly population. This paper focuses on this matter by trying to characterize the locomotion of two population groups with different functional fitness levels (high or low) while executing three different tasks - gait, stair ascent and stair descent. Features were extracted from gait data, and feature selection methods were used in order to get the set of features that allow differentiation between functional fitness level. Unsupervised learning was used to validate the sets obtained and, ultimately, indicated that it is possible to distinguish the two population groups. The sets of best discriminate features for each task are identified and thoroughly analysed.


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Paper citation in several formats:
Santos, M.; Moniz-Pereira, V.; Lourenço, A.; Fred, A. and P. Veloso, A. (2014). Relevant Elderly Gait Features for Functional Fitness Level Grouping. In Proceedings of the International Conference on Physiological Computing Systems - PhyCS, ISBN 978-989-758-006-2; ISSN 2184-321X, pages 153-160. DOI: 10.5220/0004726001530160

author={Marta S. Santos. and Vera Moniz{-}Pereira. and André Louren\c{C}o. and Ana Fred. and António {P. Veloso}.},
title={Relevant Elderly Gait Features for Functional Fitness Level Grouping},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - PhyCS,},


JO - Proceedings of the International Conference on Physiological Computing Systems - PhyCS,
TI - Relevant Elderly Gait Features for Functional Fitness Level Grouping
SN - 978-989-758-006-2
IS - 2184-321X
AU - Santos, M.
AU - Moniz-Pereira, V.
AU - Lourenço, A.
AU - Fred, A.
AU - P. Veloso, A.
PY - 2014
SP - 153
EP - 160
DO - 10.5220/0004726001530160