Abstract:
:The aim of this study was to develop an empirical model of parameter-based gait data, based on an artificial neural network and a genetic algorithm, for the assessment of patients after ankle arthrodesis. Ground reaction force vectors were measured by force platforms during level walking. Nine force parameters expressed in percentage of body weight and their chronologic incidence of occurrence expressed in percentage of stance phase period were used in modeling. Ten healthy persons and ten patients who had solid arthrodesis of the ankle were recruited in this study for developing the model. By applying the genetic algorithm neural network, the percentage of correct classification was 98.8% and the subset of discriminant parameters was be reduced to 9 out of 18. These key parameters were mainly related to the loading response and propulsive phase. This indicates that there was a reduction in the abilities in cushion impact and push off in the patients after ankle arthrodesis. Finally, the relative distance (Dr) was defined in this study and used in two new patients' examinations to demonstrate its clinical utility.
journal_name
Ann Biomed Engjournal_title
Annals of biomedical engineeringauthors
Wu WL,Su FC,Cheng YM,Chou YLdoi
10.1114/1.1342053subject
Has Abstractpub_date
2001-01-01 00:00:00pages
83-91issue
1eissn
0090-6964issn
1573-9686journal_volume
29pub_type
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