Speech processing
Hamid Azadi; Mohammad Ali Khalil Zade; Mohammad Reza Akbarzade Toutounchi; Hamid Reza Kobravi; Fariborz Rezaei Talab; Seyed Amir Ziafati Bagherzade; Alireza Noei Sarcheshme; Nina Shahsavan Pour
Volume 10, Issue 1 , May 2016, , Pages 41-47
Abstract
In recent years, researchers have tried hardly to diagnose Parkinson's disease through finding its relation with the patient's speech signal. Also, many studies have been performed on determining the intensity of the disease and its relation with vocal impairment measures. In this paper, we aim to assess ...
Read More
In recent years, researchers have tried hardly to diagnose Parkinson's disease through finding its relation with the patient's speech signal. Also, many studies have been performed on determining the intensity of the disease and its relation with vocal impairment measures. In this paper, we aim to assess and compare the ability of extracting different feature sets from speech signal in order to Parkinson's disease diagnosis. Therefore, 132 features were used to measure vocal impairments from the voice signal of individuals vocalizing phoneme /a/. Then, we used RELIEF feature selection method and applied it to Support Vector Machine (SVM) classifier to choose the best feature of each class. A comparison was made between different feature sets, and finally discrimination percent 95.93 was reached to separate patients from the healthy ones using the combination of selected features. Results obtained from this research can be a very important step toward diagnosing Parkinson's disease non-invasively.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Sanaz Ahmadzadeh; Hamid Reza Kobravi; Saeed Tosizadeh
Volume 8, Issue 3 , September 2014, , Pages 293-304
Abstract
Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel ...
Read More
Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel syndrom or incomplete spinal cord injury, the correct muscle coordination patterns between the finger muscles and wrist muscles must be reestablished. So, in this paper a prediction methodology based on artificial neural networks (ANN) is proposed to approximate the Thumb fingure extensor and flexor muscles desired activation pattern during the hand writing and Painting. In the presented strategy, A nonlinear auto-regressive neural network (NARX), Recurrent Neural Network (RNN), Radial Basis Function (RBF), Multy Layer Perceptron (MLP) and an Adaptive-network-based fuzzy inference system (ANFIS) are trained to forecast the Extensor pollicis longus and Flexor pollicis brevis muscles activity of one thumb finger of hand using Extensor carpi radialis brevis and Flexor carpi ulnaris muscles activity of forearm. Quantitative evaluations show the promising performance of developed neural networks. Eight healthy volunteers participated in the experiments.
Neuro-Muscular Engineering
Hamid Reza Kobravi; Abbas Erfanian Omidvar
Volume 2, Issue 4 , June 2008, , Pages 335-349
Abstract
In this paper an adaptive robust fuzzy controller based on sliding mode control (SMC) approach is proposed to control the knee joint position using quadriceps electrical stimulation and it has been tested on three subjects. The proposed method is based on SMC. The main advantage of SMC derives from the ...
Read More
In this paper an adaptive robust fuzzy controller based on sliding mode control (SMC) approach is proposed to control the knee joint position using quadriceps electrical stimulation and it has been tested on three subjects. The proposed method is based on SMC. The main advantage of SMC derives from the property of robustness to system uncertainties and external disturbances. However, a large value has to be applied to the control gain when the boundary of uncertainties is unknown. Unfortunately, this large control gain may cause chattering on the sliding surface and therefore deteriorate the system performance. In this paper a robust control strategy proposed which is based on the combination of sliding mode, fuzzy logic systems, and an adaptive compensator to reduce the system uncertainties while alleviating the effects of chattering. The fuzzy logic system is used to identify the muscle-joint dynamics. The parameters of this fuzzy system were estimated using another fuzzy system. The controller is evaluated through the simulation studies on a virtual patient and experimental studies on intact subjects. The results show that the adaptive robust controller provides an accurate tracking of desired knee-joint angle for different subjects and different days and can generate control signals to compensate the muscle fatigue and reject the external disturbance.
