Mochammad Ariyanto

Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor

In this study, the EMG signals are processed using 16 time-domain features extraction to classify the finger movement such as thumb, index, middle, ring, and little. The pattern recognition of 16 extracted features are classified using artificial neural network (ANN) with two layer feed forward network. The network utilizes a log-sigmoid transfer function in hidden layer and a hyperbolic tangent sigmoid transfer function in the output layer. The ANN uses 10 neurons in hidden layer and 5 neurons in output layer. The training of ANN pattern recognition uses Levenberg-Marquardt training algorithm and the performance utilizes mean square error (MSE). At about 22 epochs the MSE of training, test, and validation get stabilized and MSE is very low. There is no miss classification during training process. Based on the resulted overall confusion matrix, the accuracy of thumb, middle, ring, and little is 100%. The confusion of index is 16.7%. The overall confusion matrix shows that the error is 3.3% and overall performance is 96.7%.

Pattern recognition methods for multi stage classification of parkinson's disease utilizing voice features

A number of papers has presented a pattern recognition method for Parkinsons Disease (PD) detection. However, the literatures only able to classify subjects as either healthy of suffering from PD. This paper presents a pattern recognition method for multi stage classification of PD utilizing voice features. 22 features are obtained from University of California-Irvine (UCI) data repository. These features are extracted using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). It is found that PCA is better than LDA in terms of extracting significant features. Some classifiers such as Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbor (KNN) and Adaptive Resonance Theory-Kohonen Neural Network (ART-KNN) are then used and compared. These methods are applied in multi stage classification. The classification results show that SVM has better testing accuracy than the other methods.

Development of robotic hand integrated with SimMechanics 3D animation

In this study, a robotic hand is developed using low cost material to meet the demand for prosthetic hand in Indonesia. It has five actuators utilizing five micro servo motors. The robot consists of three joints for index, middle, ring and little respectively and two joints for the thumb. The robotic hand movement is triggered using electromyography (EMG) sensor. The sensors read the information of muscle activities and send them to the microcontroller to drive the robotic hand. By using the same signal for opening and closing fingers, the three pattern grip is embedded in the robotic grip pattern. The patterns are power grip, hook and OK sign. The 3D CAD model of robotic hand is exported into SimMechanics First Generation. The 3D animation in SimMechanics First Generation is augmented to visualize the 3D virtual hand. Based on the experimental result, the robotic hand successfully shows three patterns grip motion driven by EMG sensor. The present roboctic hand is the initial model of the prosthetic hand.

A low cost anthropomorphic prosthetic hand using DC micro metal gear motor

This research focus on developing of low cost anthropomorphic prosthetic hand using DC micro metal gear motor. The DC metal gear motor is selected as actuator because it is easy to find, low cost, and light weight. The prosthetic hand is based on 3D printed material that enables it light weight, low cost, easy to manufacture and easy to maintain. The mechanism of the hand is based on the tendon spring mechanism. The prosthetic hand has five degree of freedom (DOF) and two joints in each finger. For performing the activities of daily living (ADLs), the hand is designed with seven grip patterns. Based on the experimental results in grasping test and writing test on the white board, the hand can be used as low cost prosthetic hand replacing the passive prosthetic hand that has been available on the market.

Electromyography (EMG) signal recognition using combined discrete wavelet transform based on Artificial Neural Network (ANN)

Rapid disability patients increasing over time and need a solution in the future. Hand amputation is one form of disability that common in Indonesian society. A possible solution would be necessary at the moment is the development of prosthetic hand that has the ability as a human hand. The development of neuroscience has now reached the stage of the bodys ability to use the signal as an input signal to operate a system. One of the applications of the science development is the use of electromyography (EMG) signals as an input to the control system to operate the prosthetic hand. This study is divided into two stages: a preliminary study and further research. Initial research focus in the process of EMG signal pattern recognition and advanced research focus in the development of a prototype prosthetic hand that is integrated with the controller system. Preliminary research indicates that the results of pattern recognition EMG signal using wavelet transform and Artificial Neural Network (ANN) classification has an accuracy rate of about 77.5 %. Based on these results, it can be concluded that the study results could be used as a signal input to program control of the prosthetic hand that will be developed in phase two.

A pattern recognition method for stage classification of Parkinson's disease utilizing voice features

This paper presents a pattern recognition method for multi-class classification of Parkinsons disease based on PCA, LDA and SVM. 22 voice features which are extracted and reduced using PCA and LDA. SVM is then used during the classification step. The classification accuracy between single features and PCA and LDA features are presented and the results show that the PCA features have greater accuracy than LDA features and the single features.

Mathematical and physical modelling with dynamic change in the center of gravity of quadrotor

One of challenges in aerial grasping is the dynamic change in the center of gravity (CoG). The control system design of quadrotor must be able to compensate for the dynamic change in the CoG of the quadrotor. It is caused when a quadrotor flies and carries a payload, the CoG of quadrotor does not coincide with the center of the quadrotors geometry. Therefore, for designing a robust control system with respect to an added payload mass, an accurate dynamic model of a quadrotor is highly required. In this paper, the dynamic change in the CoG location of a quadrotor will be developed in mathematical and physical model when the quadrotor carries a payload at hover. The physical model will be utilized to verify the mathematical model. In the simulation of mathematical and physical model, the Euler angle and the altitude of quadrotor are controlled by PID compensator. Based on the simulation results, the quadrotor has the same response especially in transient and steady state responses. For further complex dynamic modelling of quadrotor, physical model can be used for control design purpose with ease of development.

Speech control of robotic hand augmented with 3D animation using neural network

In this paper, speech control of robotic hand augmented with 3D animation system has been proposed and presented. Artificial neural network is employed for speech recognition method with tansig and softmax transfer function in hidden layer and output layer. Stream processing method is incorporated for processing the input signal in real time. Thirteen features in frequency domain and time domain that are commonly used in the EMG analysis are utilized in this system. To reduce the influence of noise, voice in noisy environment in the room is recorded as training data set. From the experimental results in offline speech recognition, ANN can recognize the voice command with very high accuracy. In the online real time speech recognition incorporating stream processing, the recognition accuracy decreased about 10%. The proposed speech control of robotic hand augmented with 3D animation is reliable enough with noisy environment.

Low Cost Parkinson’s Disease Early Detection and Classification Based on Voice and Electromyography Signal

Parkinson’s disease (PD) is one of the health problems concerning for elderly population. Manageable symptom is an important thing for Parkinson’s sufferer in order to be independent enough to do daily activities. As a solution to Parkinson’s early detection method, this research purpose is to develop a low cost diagnostic tool for PD which inexpensive yet accurate and easy to use by neurologist, enriching and giving new insight for neurologist about voice and electromyography (EMG) signal analysis result. It can be very useful for PD clinical evaluation and spreading awareness about PD as well as the important of early diagnose to citizen. Parkinson’s detection method in this research uses pattern recognition method, the first step is initiated with voice and EMG data acquisition. Second step is feature extraction using five features for voice and EMG signal. The last step is classification using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural network (ANN) methods. The pattern recognition of PD is divided in two sections, the first is for two class classification, and the second is four stage classification based on Hughes Scale which commonly used in Indonesia as PD diagnose guideline. Based on the results, voice method classification has higher accuracy than EMG classification because the feature for voice is a good feature which can well classified the voice data. Voice data sampling rate is higher than EMG data sampling rate which means voice data recording has more data each second than EMG data. Two class classification has higher accuracy than four class classification both in ANN and ANFIS. Based on the four class classification results in both of voice and EMG signals using ANN and ANFIS, the probable class has the lowest accuracy of all classes.

Design and simulation of PID controller for lower limb exoskeleton robot

Lower limb exoskeleton robot is a robot that functions as a walking tool of movement for the lower of the human body (usually made for patients with stroke and paraplegic). However, as time goes by lower limb exoskeleton robot is no longer reserved for patients with stroke and paraplegic, some of them are made to military requirements or the need to lift heavy stuff. In this case, the lower limb exoskeleton robot is used for rehabilitation aids for people with paraplegic who suffered an accident on the lower part of the human body (lower limb). This paper presents the design and simulation of the lower limb exoskeleton robot using CAD software to design, and Matlab/Simulink for simulation. The first step after the design of the lower limb exoskeleton robot is completed, we export the file of robot design to the SimMechanics (using first generation). After we obtain the results of the export file, we give a Proportional-Integral-Derivative (PID) controller at each revolute joint. Then, we use a Matlab/Simu...