# Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection
## 1 Introduction  
Hemiplegia is caused by brain damage or spinal cord injury resulting in motor control loss in half of a person's body. The affected body area has issues, including movement inability of one arm and leg in the right or left body part area. Other recorded problems include movement in spastic patterns and stereotyped synergies. Additionally, there is selective activity loss in the muscles responsible for trunk control, specifically in the flexion, rotation, and lateral flexion muscles. Hemiplegia patients also suffer from difficulties in trunk movement regardless of the muscle action type, and the abdomen's muscles demonstrate very high activity and tone loss [1, 2]. The patient's problems are not restricted only to the affected areas, they are extended to a certain degree in all brain functions since the human brain is a massive network of interconnected neurons. The person suffering from hemiplegia will face motor impairment in both body sides, affecting balance, coordination, sensory perception, and spatial orientation. The behavior, cognition, and memory will be affected, causing a significant challenge in the rehabilitation procedure [3, 4].  
The current article proposes an automated system for diagnosing the hemiplegia type (right or left body part is paralyzed) between healthy and non-healthy individuals. The motivation behind creating such a system is to provide an inexpensive supplementary tool that does not require expensive three-dimensional (3D) cameras for hemiplegia typediagnosis. The proposed solution is based on the RehaGait mobile gait analysis system from HASOMED. The RehaGait mobile gait analysis system utilizes seven sensors placed in four different body parts of the individual for documenting spatio-temporal-specific data during walking. The sensors are provided by the RehaGait system, where one sensor is placed on the patient's hip and one sensor on each foot, shank, and thigh [5, 6].  
The selected gait analysis system includes an integrated video capture capability for observing the patient's condition. It can find the damaged areas and evaluates the gait pattern. It is suitable for mobile use, identifies asymmetries in the lower limbs, has movement freedom, and can visualize the results using a graphical representation [7, 8]. Each sensor can capture signals from three different sources (accelerometer, magnetometer, and gyroscope), although the proposed system utilizes only the data taken from the accelerometer [6]. The captured data were divided into 2-second windows and underwent a pre-processing and feature extraction process. Then, they were introduced as input to the feature construction method for radial basis function (RBF) networks (FC4 RBF) based on grammatical evolution (GE) by [PERSON] et al. [9]. The training procedure utilized a dataset composed of thirty individuals. The dataset is divided into ten healthy patients, eight patients with left hemiplegia, and twelve patients with right hemiplegia.  
The paper contains seven sections starting with Section 1, which presents the problem's description and motivation. Section 2 documents the related work regarding existing works on hemiplegia. It also describes the algorithms used to compare the proposed method in the paper's experimental part. Section 3 describes the architecture of the selected approach for the classification task. Section 4 depicts the proposed system's analysis. Section 5 compares the method with four machine learning algorithms, while Sections 6 and 7 include the \"Discussion\" and \"Conclusions\".