Classification of gait parameters in stroke with peripheral neuropathy (PN) by using k-Nearest Neighbors (kNN) algorithm / N. Anang ...[et al.]

—This paper presents the gait pattern classification
between 3 groups which are control, stroke only and stroke with
Peripheral Neuropathy (SPN) using k-Nearest Neighbors (kNN)
algorithm. Control group has been used as a reference or
baseline in order to see the difference in the gait pattern. The
model able to classify patients into their respective group based
on the gait parameters collected. Furthermore, the findings also
will help them to monitor patient’s performances in
rehabilitation program from time to time. 29 subjects has been
recruited (9 SPN, 10 stroke subjects and 10 control subjects) with
range of age between 40 to 65 years old. Additionally, all subjects
must be able to walk freely without any cane or mechanical aid
during walking. Vicon® Nexus Plug-in-Gait has been used to
compute the kinematic gait parameters. From the results, it is
found that there are 9 significant differences in kinematic angles
and spatio-temporal data. The classification model developed has
been successfully discriminate three different groups with
83.33% accuracy.