Abstract:
Prosthesis implants had been adored around the globe for amputee. The further advanced hand
prostheses using electromyography signals getting common. The immense importance assisting
biomedical domain the robotic applications are immersing. Command to our muscles generated
by our mind are equivalent to EMG signals in electrical terms, therefore configuration of these
signals reckon in the muscle activities. The aim of this study is synthesis of EMG signals
produced during hand movement and analysis of the data by adopting different methods. To get
the best results these signals are needed to be preprocessed to reduce the noise and unwanted
signals inferred while recording data, as data can be collected by installing electrodes on
patient’s hand. NinaPro shared the data of previous the researchers of various streams on
numerous hand movements, which simplifies the prosthesis implants to accomplish better
movements. Intention of this thesis is to dive deep into the analysis of this available data set for
its different classes, strategy to be followed is the processing of the EMG signals and then
formation of window segmentation facilitating for smooth management of data for further steps.
Moreover, to extract features which is the task to be achieved in a way to get a control on the
hand prosthesis in frequency domain. Furthermore, the classifiers Linear Discriminant Analysis
(LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Artificial Neural
Network (ANN) applied on the extracted features and highest of 85.31% accuracy achieved with
conventional classifier KNN. However ANN from deep learning gave far better results than the
machine learning techniques.
