# Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection
## 2 Literature Review  
Existing methods are focused on different aspects, including stroke and hemiplegic patients' gait. The authors in [PERSON] et al. [10] proposed a machine learning method based on deep learning and convolutional neural networks (CNNs) to identify hemiplegic gait. The study [PERSON] [11] used a fuzzy logic system that utilized a Tsukamoto-type inference method for the classification task between healthy and hemiplegic patients. The authors in [PERSON] et al. [12] used hierarchical cluster analysis for classifying the gait patterns of patients having equinus foot deformity. The work in [PERSON] et al. [13] used non-hierarchical cluster analysis, which utilizes the temporal/spatial and kinematic parameters of walking to categorize the gait patterns of stroke patients. The authors in [PERSON] et al. [14] utilized cluster analysis for identifying gait patterns from Spatio-temporal parameters, walking endurance, and sagittal plane kinematic profiles. This study aimed to categorize the walking performance of hemiplegic patients. The study [PERSON] et al. [15] used the gait data from 20 hemiplegic and 40 healthy individuals taken from low-cost depth cameras. Then, they used the random forest (RF) classification algorithm to analyze the importance of different gait features. The authors in [PERSON] et al. [16] utilized the second version of Microsoft Kinetic for Windows to record the walking trajectory data from patients with hemiplegia. The data were used for gait identification feature extraction and introduced as input to a Bayesian classification algorithm for automatic recognition of hemiplegic gait. Finally, the RF algorithm was selected to identify each feature's significance. [PERSON] and [PERSON] [17] conducted a gait analysis by comparing clustering and bi-clustering algorithms in 41 patients with hemiplegia. Moreover, they proposed a new bi-clustering algorithm named KMB. [PERSON] et al. [18] recorded body surface electrical signals from 16 stroke patients having various degrees of rehabilitation and eight healthy individuals. These signals were filtered and underwent a wavelet transform procedure for extracting features. Then, they were introduced into linear regression (LR), ridge regression (RR), extreme gradient boosting (XGBoost), and support vector machine (SVM) models for condition evaluation and classification (healthy, mild, or severe hemiplegia). The work in [PERSON] et al. [19] made a foot contact pattern analysis in 60 hemiplegic stroke patients and 30 healthy subjects. The research's objective was two-fold. First, they wanted to explore the feasibility of using a foot contact pattern for neurologic recovery prediction. Second, they wanted to investigate the ambulation training effect in patients with hemiplegic stroke. The authors in [PERSON] et al. [20] applied deep neural networks (DNNs) for stroke gait detection and abnormal gait pattern classification. The work in [PERSON] et al. [21] created a functional electrical stimulation (FES) control system for enhancing gait rehabilitation in patients suffering from hemiplegia after a stroke. They used DNNs to control the muscles' electrical stimulation on the paretic side. The DNNs were trained using muscle activity data from healthy subjects during gait. A significant percentage of studies used data taken from children suffering from hemiplegia, diplegia, or cerebral palsy. The authors in [PERSON] et al. [22] quantified the asymmetric behavior of children with mild hemiplegia during self-selected walking using features taken from surface-electromyography (EMG) and foot-floor contacts. They used statistical gait analysis in 16 hemiplegic and 100 healthy children. The study [PERSON] and [PERSON] [23] studied kinematic, kinetic, and EMG data from 278 children with spastic hemiplegia in order to find useful gait patterns. The authors in [PERSON] et al. [24] created a gait phase detection approach that is based on a hidden Markov model. It utilizes single-axis foot-mounted wearable gyroscopes to record the input data.