Chapters authored
Control Strategy for Underactuated Multi-Fingered Robot Hand Movement Using Electromyography Signal with Wearable Myo Armband
The main goal of this research is to develop a control strategy for an underactuated robotic hand, based on surface electromyography (sEMG) signal obtained from a wireless Myo gesture armband, to distinguish six, several hand movements. The pattern recognition system is employed to analyze these gestures and consists of three main parts: segmentation, feature extraction, and classification. A series of 150 trials is carried out for each movement and it is established which was most suitable for electromyography signals that can be later used in recognition systems. A backpropagation neural network was used as a classifier. The architecture has a hidden network and six output layers. The number of neurons of the hidden network (20) was determined based on the performance in training progress. The proposed system is tested on datasets extracted from five healthy subjects. A great accuracy (94.94% correct assessment). between the experimentally values and those predicted by the artificial neural network (ANN) was achieved. In addition, kinematic analysis of the proposed underactuated hand has been carried out to verify the motion range of the joints. Simulations and experiments are carried out to verify the effectiveness of the proposed fingers mechanism and the hand prosthesis to generate grasp or postures.
Part of the book: Biosensors
Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks
This research compares classification accuracy obtained with the classical classification techniques and the presented convolutional neural network for the recognition of hand gestures used in robotic prostheses for transradial amputees using surface electromyography (sEMG) signals. The first two classifiers are the most used in the literature: support vector machines (SVM) and artificial neural networks (ANN). A new convolutional neural network (CNN) architecture based on the AtzoriNet network is proposed to assess performance according to amputation-related variables. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods and The performance it is compared with other CNN proposed by other authors. The performance of the CNN is evaluated with different metrics, providing good results compared to those proposed by other authors in the literature.
Part of the book: Human-Robot Interaction