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

Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, compar...

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Vydáno v:	Computer methods and programs in biomedicine Ročník 113; číslo 3; s. 736 - 748
Hlavní autoři:	Martins, Maria, Costa, Lino, Frizera, Anselmo, Ceres, Ramón, Santos, Cristina
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ireland Ltd 01.03.2014 Elsevier
Témata:	Adult Algorithms Applied sciences Biological and medical sciences Biomechanical Phenomena Computational Biology Computer science; control theory; systems Data processing. List processing. Character string processing Dependent Ambulation - physiology Dependent Ambulation - statistics & numerical data Diseases of the osteoarticular system. Orthopedic treatment Evolutionary algorithms Exact sciences and technology Gait - physiology Gait analysis Humans Internal Medicine Medical sciences Memory organisation. Data processing Models, Biological Other Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects) Rehabilitation - instrumentation Rehabilitation - statistics & numerical data Software Support Vector Machine Walker assistance Walkers Young Adult Walker assistance Gait analysis Evolutionary algorithms Space time correlation Gait Redundancy Evolutionary algorithm Handicapped aid Pedestrian Hybridization Posture Walking Pain Genetic algorithm Annoyance Kinematics Vector support machine Selection criterion Medical application
ISSN:	0169-2607, 1872-7565, 1872-7565
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Abstract	Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach.
AbstractList	Abstract Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach.Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach.
Author	Santos, Cristina Martins, Maria Ceres, Ramón Frizera, Anselmo Costa, Lino
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Cites_doi	10.1109/72.554193 10.1007/978-1-4020-6182-0_4 10.1007/BF02823145 10.1136/bmj.308.6943.1552 10.1016/j.patrec.2005.10.010 10.1136/bmj.309.6947.102 10.1109/TSMCA.2008.2007977 10.1007/978-3-642-13214-8_11 10.5019/j.ijcir.2006.64 10.1007/978-3-540-71618-1_27 10.1109/TITB.2009.2022913 10.1016/j.apmr.2004.03.026 10.1109/4235.996017
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Keywords	Walker assistance Gait analysis Evolutionary algorithms Space time correlation Gait Redundancy Evolutionary algorithm Handicapped aid Pedestrian Hybridization Posture Walking Pain Genetic algorithm Annoyance Kinematics Vector support machine Selection criterion Medical application
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Snippet	Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort... Abstract Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as,...
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SubjectTerms	Adult Algorithms Applied sciences Biological and medical sciences Biomechanical Phenomena Computational Biology Computer science; control theory; systems Data processing. List processing. Character string processing Dependent Ambulation - physiology Dependent Ambulation - statistics & numerical data Diseases of the osteoarticular system. Orthopedic treatment Evolutionary algorithms Exact sciences and technology Gait - physiology Gait analysis Humans Internal Medicine Medical sciences Memory organisation. Data processing Models, Biological Other Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects) Rehabilitation - instrumentation Rehabilitation - statistics & numerical data Software Support Vector Machine Walker assistance Walkers Young Adult
Title	Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait
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