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.