Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

A CNN-based Vision System for Pattern Recognition in Mobile Robots

Abstract -In the field of robotics real-time image processing can provide the information necessary for mobile robots to execute a task in indoor environment. In this paper the application of a simple CNN-based system to translation and scale-invariant object recognition in the artificial vision structure of a mobile robot is proposed. The suggested bio-inspired vision system is mainly constituted by an encoder and a cellular associative memory. Bipolar images constitute the input to the cellular associative memory, which performs the recognizing stage of the bio-inspired vision system. The capability of the proposed system to detect and recognize scaled and translated targets is investigated on suitable test situations

On Modeling an Innovative Monitoring Network for Protecting and Managing Cultural Heritage from Risk Events

In this paper the model of an Innovative Monitoring Network involving properly connected nodes to develop an Information and Communication Technology (ICT) solution for preventive maintenance of historical centres from early warnings is proposed. It is well known that the protection of historical centres generally goes from a large-scale monitoring to a local one and it could be supported by a unique ICT solution. More in detail, the models of a virtually organized monitoring system could enable the implementation of automated analyses by presenting various alert levels. An adequate ICT solution tool would allow to define a monitoring network for a shared processing of data and results. Thus, a possible retrofit solution could be planned for pilot cases shared among the nodes of the network on the basis of a suitable procedure utilizing a retrofit catalogue. The final objective would consist in providing a model of an innovative tool to identify hazards, damages and possible retrofit solutions for historical centres, assuring an easy early warning support for stakeholders. The action could proactively target the needs and requirements of users, such as decision makers responsible for damage mitigation and safeguarding of cultural heritage assets

Seismic risk assessment of a medieval tower: the case study of Craco

In the present paper the risk assessment of the medieval Norman tower of Craco (Matera, Italy) is discussed. Craco is a totally abandoned little town because of the activation of landslide motions of its soil depth. The medieval tower is one of the few buildings still standing as it is built on a fixed stiff foundation ground. Nevertheless, the tower is, indirectly, subjected to the movements of the close landslide. The tower is located in the highest and more stable part of the hill where the old town was built in the XII century for protection from enemy attacks. It is 20 m tall and has a (11 x 11) m2 square plan. The basement has a truncated pyramid shape; originally it had two masonry vaults, one barrel on the first floor, which no longer exists, and a still visible cruise at the second order, connected by a now destroyed internal staircase. In 1949 a reinforced concrete cistern was placed inside the tower. Craco is classified as a town with a medium level seismic hazard. The main aim of the present study is to evaluate the seismic risk by means of a Finite Element model, calibrated through dynamic tests performed on the tower and considering the historical value of the structure and the context in which it stands. In fact, the structure is characterized by several peculiarities: the presence of a reinforced concrete cistern, the interaction with the surrounding buildings, the closeness to the landslide, the topographical exposition, etc. Moreover, the structure has a great impact on the society due to its touristic interest, as it is an emblem of the, so-called, “ghost town” of the Appennino mountains. A new approach is proposed to evaluate the effects of all the previously cited features on the evaluation of the seismic risk of the tower, introducing also economical and sociological parameters.The Italian project PRIN 2015 - “Mitigating the impacts of natural hazards on cultural heritage sites, structures and artefacts (MICHe)” is acknowledged for the support given to the present research

Findings from the RES NOVAE Project: new scenarios, devices and applications for smart grids and active distribution grids

This paper summarizes all research advancements obtained by the Politecnico di Bari through the RES NOVAE Project activities in the field of the smart grids. Such activities have been aimed at bringing substantial contributions in the development of smart grid in medium-voltage and low-voltage distribution systems. Since the scientific approach to smart grids is intrinsically multidisciplinary, specific researches were addressed in all the fields of electrical and information engineering that are involved in the creation of future distribution systems. The paper is organized in seven sections, each providing description of the research results developed in a specific field (power systems, communication systems, power electronics, electric measurements, circuits and systems, information processing systems, automatic control)