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Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in

Maria Martins¹, Lino Costa², Anselmo Frizera³

¹Industrial Electronics Department, University of Minho, Guimarães, Portugal.

Computer Methods and Programs in Biomedicine

|January 22, 2014

Summary

This summary is machine-generated.

Incorrectly prescribed walker devices cause patient discomfort. This study identifies key gait features using genetic algorithms and support vector machines to differentiate assisted versus non-assisted walking, improving device prescription.

Keywords:

Evolutionary algorithms Gait analysis Walker assistance

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Area of Science:

Biomechanics
Rehabilitation Engineering
Data Science

Background:

Walker devices are frequently prescribed incorrectly, leading to patient dissatisfaction, pain, and discomfort.
Objective evaluation of assisted gait's impact on patient mobility is crucial for effective rehabilitation.
Gait analysis provides extensive data, but feature selection is challenging for clinical interpretation.

Purpose of the Study:

To objectively evaluate the effects of assisted gait on walker users' gait patterns compared to non-assisted gait.
To identify the most relevant gait features for discriminating between assisted and non-assisted gait.
To present an efficient feature selection approach combining evolutionary and machine learning techniques.

Main Methods:

Gait analysis focusing on spatiotemporal and kinematics parameters.
Application of evolutionary techniques (genetic algorithms) for feature selection.
Utilizing support vector machine algorithms for gait pattern classification.
Comparison with other classification algorithms for validation.

Main Results:

The developed approach efficiently selects relevant gait features to differentiate assisted and non-assisted gait.
Key differences in gait were identified in balance and joint excursion in the sagittal plane for healthy subjects.
The technique proved effective in discriminating between assisted and non-assisted gait patterns.

Conclusions:

The combined genetic algorithm and support vector machine approach is an efficient method for gait feature selection.
Identified gait characteristics (balance, sagittal plane joint excursion) offer clinical insights for walker prescription.
This methodology can help reduce patient dissatisfaction and problems associated with incorrect walker device use.