Automatic Hemiplegia Type Detection (Right or Left) Using the Levenberg-Marquardt Backpropagation Method

Abstract

Hemiplegia affects a significant portion of the human population. It is a condition that causes motor impairment and severely reduces the patient’s quality of life. This paper presents an automatic system for identifying the hemiplegia type (right or left part of the body is affected). The proposed system utilizes the data taken from patients and healthy subjects using the accelerometer sensor from the RehaGait mobile gait analysis system. The collected data undergo a pre-processing procedure followed by a feature extraction stage. The extracted features are then sent to a neural network trained by the Levenberg-Marquardt backpropagation (LM-BP) algorithm. The experimental part of this research involved creating a custom-created dataset containing entries taken from ten healthy and twenty non-healthy subjects. The data were taken from seven different sensors placed in specific areas of the subjects’ bodies. These sensors can capture a three-dimensional (3D) signal using the accelerometer, magnetometer, and gyroscope device types. The proposed system used the signals taken from the accelerometers, which were split into 2-sec windows. The proposed system achieved a classification accuracy of 95.12% and was compared with fourteen commonly used machine learning approaches.

Artificial Neural Network Accuracy Optimization Using Transfer Function Methods on Various Human Gait Walking Environments

“…Based on previous research [12], [27]- [30] Following the finding that Levenberg-Marquardt backpropagation had the best accuracy result for gait detection, this present research used the same training algorithm. Fig.…”

“…Levenberg-Marquardt backpropagation was chosen as the training algorithm due to its better performance than another training algorithm [12]. Some studies found that Levenberg-Marquardt backpropagation has classification results above 95% [27]- [30]. EMG signals were classified into 8 classes, i.e., treadmill walking with speed 1, labeled as T1, treadmill walking with speed 2, labeled as T2, treadmill walking with speed 3, labeled as T3, treadmill walking with speed 4, labeled as T4, treadmill walking with speed 5 labeled as T5, ground walking labeled as GW, walked upstairs Labelled as UW and walked down stair labeled as DW.…”

Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

“…Then, a typical BP neural network [27,28], which usually has a two-layer feed-forward framework, sigmoid hidden neurons, and linear output neurons, is established, as shown in Figure 3. For the training process of the BP neural network, the dataset is split into training, validation, and testing sets in a ratio of 7:1.5:1.5, and the Levenberg-Marquardt algorithm is implemented [29,30]. The predicted spectrum coefficients of the BP neural network can be used to reconstruct the temperature field by substituting them into Equation (12).…”

The Flexural Strength Prediction of Carbon Fiber/Epoxy Composite Using Artificial Neural Network Approach

“…Artificial neural network prediction has many algorithmic methods (i.e., Levenberg-Marquardt: LM, Bayesian regularization: BP, scaling conjugate gradient: SCG). The choice of algorithm depends on the extent of the data and the amount of data [40][41][42][43][44]. Jiaojiao F. (2018) [41] studied the estimated monthly mean daily global solar radiation using the neural network approach with the LM and BP algorithms.…”

“…The LM algorithm is widely used for predicting mathematical data, and can reduce the range between mathematical data. Therefore, the LM algorithm is appropriately used in this research [42].…”