Authors:
Bart Jansen
1
;
Maxine Tan
1
;
Ivan Bautmans
2
;
Bart Van Keymolen
3
;
Tony Mets
4
and
Rudi Deklerck
1
Affiliations:
1
Vrije Universiteit Brussel, Belgium
;
2
Vrije Universiteit Brussel; Vrije Universiteit Brussel; Universitair Ziekenhuis Brussel; Stichting Opleiding Musculoskeletale Therapie (SOMT), Belgium
;
3
Vrije Universiteit Brussel; Vrije Universiteit Brussel, Belgium
;
4
Vrije Universiteit Brussel; Vrije Universiteit Brussel; Universitair Ziekenhuis Brussel, Belgium
Keyword(s):
Accelerometry, Fall risk, Gait analysis, Step time asymmetry, Classification, Feature selection, FD-NEAT.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Monitoring and Telemetry
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
;
Wearable Sensors and Systems
Abstract:
This paper describes an accelerometer based gait analysis system for the assessment of fall risk. The assessment is based on 22 different features calculated from the signal. The different features are combined using machine learning algorithms in order to decide whether the subject has an increased fall risk. Results from Naive Bayes, Neural Networks, Locally Weighted Learning, Support Vector Machines and C4.5 are reported and compared. It is argued that the neural networks provide low accuracy results because of the high dimensionality of the feature space compared to the available data. It is shown that FD-NEAT (a method from neuro evolution which simultaneously learns the network topology, the network weights and the relevant features) outperforms the other methods in the given classification task. The system is evaluated on a database consisting of 40 elderly with known fall risk and 40 healthy elderly controls.