Journal of Bioengineering & Biomedical Science

Bionic arm is a robotic arm that offers many of human arm features such as hand grasp and release, flexionextension, elbow flexion-extension, supination-pronation etc. which is integrated with the nervous system and controlled by Electromyogram signals. Invasive and non-invasive methods are used to collect the EMG signal from amputees. In spite of difficulty caused by invasive methods, non-invasive methods are being opted in today's recent Bionic Arms. To overcome the some drawbacks of non-invasive methods proper classification algorithms has to be chosen for controlling individual finger movements in Bionic Arm. In this paper, initially various feature extraction; reduction and classification algorithms are implemented on EMG data of different subjects which is available from Ninapro database. From the results obtained, MAV algorithm for feature extraction, PCA algorithm for feature reduction and KNN algorithm for feature classification are chosen since they gave more accuracy compared to others after implementing on EMG data of different subjects. By employing this algorithms 95% accuracy is achieved for controlling individual finger movements in Bionic Arm. Response time between grasp and release actions of fingers in Bionic Arm obtained after implementing on processor is less than 1ms.

Cardiovascular diseases are a major cause of death. Change in normal human heart beat may result in different types of cardiac arrhythmias. An Irreversible damage to the heart is possible. In this paper a method is proposed to classify different arrhythmias and normal sinus rhythm, through a combination of wavelet Transform and Artificial Neural Networks (ANN) accurately and efficiently. Adaptive filtering using Recursive Least squares (RLS) adaptive algorithm is utilized to nullify AC and DC noises from the sample ECG signal set. ECG data’s are collected from MITBIH database. As ECG signal is a non- stationary signal wavelet transform is used to decompose the signal at various resolutions. This allows accurate detection and extraction of features. In our approach, discrete wavelet transforms (DWT) coefficients set is obtained from wavelet decomposition which would contain the maximum information about the arrhythmia. RR interval, QRS duration, PR duration is extracted from the wavelet decomposition. With these parameters classification of arrhythmia is done. Multilayer feed forward ANNs employ error back propagation (EBP) learning algorithm were trained and tested using the extracted parameters are used for training and testing the error back propagation (EBP) algorithm. Multilayer feed forward ANNs are employed through this EBP learning algorithm. This classification is done for 84 patient samples. The overall accuracy of our approach is 98.8%.

One of the problems that mostly arises in kidney diseases, is abnormal reduction of fluid removal in the body, which increases the level of acids and increase normal range of molecules such as urea, glucose, creatinine, beta 2 -micro globulin and complement factor D. Due to the availability of technologies to produce membranes and fibers, it is possible to remove these toxins by producing membrane sets, such as dialysis. The heart of a dialysis is dialyzer that filters toxins and extra water of the body through its hollow membranes. In this regard, a model is needed to investigate and optimize process of toxin clearance, in order to reduce manufacturing costs and increase efficiency of dialysis. Since process parameters and dialysis structure determines elimination level and also the stay of useful molecules in blood, manufacturing design is of great importance. In this paper, a two-dimensional model of speed, pressure and flow concentration was prepared for both blood and dialysate phase, and discretized using finite difference method. Here, the simulation was carried out using MATLAB software. TIn he present work we try to build a 2-D model of blood and dialysate flows as a platform to study dialyzer performance in the future.