The following paper presents the implementation of a versatile convolutional neural network architecture (CNN) for the recognition of 6 different commands by means of hand gestures using electromyographic signals. For this, a database consisting of 2880 multi-channel feature maps is built, that is, each dataset is composed of the processed signals of the 8 sensors of a Myo Armband, making use of power spectral density maps. The database is divided into 3 sets of equal size for training, validation and testing. With this, the architecture is trained, obtaining 98.4% accuracy in the validation and 99% in the tests, as well as the verification of the processing time that the network takes to obtain a result, this being 4 ms, demonstrating the ability of a shallow CNN to support multiple channels belonging to different sensors, achieving a high performance and having a reduced execution time that gives the possibility of being implemented in an application in real time.

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD (n = 77), ADHD (n = 43), ASD + ADHD (n = 21), and control (n = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. For ADHD, a BA of 0.84 was achieved for female participants. When a three-group model (ASD, ADHD, and control) the BA was lower, at 0.70, and fell further to 0.53 when all groups were included (ASD, ADHD, ASD + ADHD, and control). The findings support a role for the ERG in establishing a broad two-group classification of ASD or ADHD, but the model’s performance depends upon sex and is limited when multiple classes are included in machine learning modeling.

The following paper presents the development, operation and comparison of two methods of object recognition trained for the classification of surgical instrumentation, where a video sequence is used to capture scene information constantly, in order to allow the selection of some of the instruments according to the needs of the doctor. The methods used were Convolutional Neural Networks (CNN) and Haar classifiers, where the first was added a previous element detection stage, and the second one was conditioned to allow it not only to detect elements, but also to classify them. With the CNN an accuracy of 96.4% in the classification of the two categories of the first branch of the tree was reached, while for Haar classifiers 90% accuracy was achieved in the detection of one of the five instruments, whose classifier was the one that presented the best results.