Classification of hemiplegia through gait analysis and machine learning methods

Abstract

Objective: Gait analysis is a method that is used for understanding normal walking and determining the stage of the disease as it affects
 walking. It is important to objectively determine the stage of the disease in order to decide interventions and treatment strategies. This
 study aims to determine the Brunnstrom Stage of the hemiplegic patients with an analysis of gait data.
 Patients and Methods: In the first part of the study, the gait signal data were taken from 28 post-stroke hemiplegic patients and
 7 healthy individuals with three-axis accelerometers. In the second part, new gait data were collected from 15 healthy individuals
 through an accelerometer on the anteroposterior axis.
 First the accelerometer signals were decomposed to Daubechies 5 (Db5) level six wavelets using MATLAB software. Subsequently,
 these attributes were classified through several classifier and machine learning algorithms on WEKA and MATLAB software packages
 to predict the stages of hemiplegia.
 Results: The highest accuracy rate in the prediction of hemiplegia stage was achieved with the LogitBoost algorithm on WEKA with
 91% for 35 samples, and 90% for 50 samples. This performance was followed by the RUSBoosted Trees algorithm on the MATLAB
 software with an accuracy of 86.1% correct prediction.
 Conclusion: The Brunnstrom Stage of hemiplegia can be predicted with machine learning algorithms with a good accuracy, helping
 physicians to classify hemiplegic patients into correct stages, monitor and manage their rehabilitation.