A Lightweight Machine Learning Approach to Detect Depression from Speech Analysis

The growing number of people suffering from depression makes it increasingly necessary to find new approaches able to support medical experts in its diagnosis. The early detection of depressive symptoms is crucial in limiting the co-occurrence of associated behavioural disorders such as psycho-motor retardation symptoms and social withdrawal. Therefore, automatic detection systems represent promising solutions not only for supporting the early diagnosis of the disease but also for monitoring patient’s health status, thus improving both the quality of the care process and life quality of patients. At the light of these considerations, this paper proposes an automatic system exploiting a machine learning algorithm, to distinguish among depressed and healthy subjects through the analysis of selected acoustic features extracted from spontaneous speech narratives produced by healthy and depressed subjects. The proposed system achieves a classification accuracy of about 85%, proving to be a promising solution for supporting the diagnosis of depression in real-time in a reliable, fast, inexpensive and non-intrusive ways

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data

Toward a Plug-and-Work Reconfigurable Cobot

The ongoing trend from mass-produced to mass-customized products with batch sizes as small as a single unit has highlighted the need for highly adaptable robotic systems with lower downtime for maintenance. To address these demands, this article proposes the development of a novel reconfigurable collaborative robot (cobot), which has the potential to open up many new scenarios within the rapidly emerging flexible manufacturing environments. As the technological contribution, we present a complete hard- and software architecture for a quickly reconfigurable EtherCAT-based robot. This novel approach allows to automatically reconstruct the topology of different robot structures, composed of a set of body modules, each of which represents an EtherCAT slave. As the theoretical contribution, we propose a method to obtain in an automatic way the kinematic and dynamic model of the robot and store it in universal robot description format (URDF) as soon as the physical robot is assembled or reconfigured. The method also automatically reshapes a generic optimization-based controller to be instantly used after reconfiguration. While this article focuses on reconfigurable manipulators, the proposed concept can support arbitrary serial kinematic tree-like configurations. We demonstrate the contributions with examples of the following: how the topology of the robot is reconstructed and the URDF model is generated, and a Cartesian task application for a cobot built with the basic modules, demonstrating the quick reconfigurabilty of the system from a 4-degrees-of-freedom (DOF) robot to a 5-DOF robot, in order to satisfy new workspace requirements

Transforming Growth Factor Beta 1 and Vascular Endothelial Growth Factor Levels in the Pathogenesis of Periodontal Disease

Periodontal disease is characterized by inflammation and bone loss. The balance between inflammatory mediators and their counter-regulatory molecules may be fundamental for determining the outcome of immune pathology of periodontal disease. Cytokines play crucial roles in the maintenance of tissue homeostasis, a process which requires a delicate balance between anabolic and catabolic activities. In particular, two families of growth factors-such as transforming growth factor-β1 (TGF-β1) and vascular endothelial growth factor (VEGF) are thought to play important roles in modulating the proliferation and/or migration of structural cells involved in inflammation and regulation of immune responses. The aim of this work was to analyze gingival samples and periodontal tissue specimens collected from thirty-eight patients with chronic periodontal disease and from forty healthy individuals, in order to detect the expression and distribution of TGF-β1 and VEGF between the two groups. TGF-β1 and VEGF expression levels were detected using immunohistochemical analysis and computer-assisted morphometric analysis. The findings presented here suggest that biomarker such as TGF-β1 and VEGF have an important regulating role in the orchestration of the immune response, which in turn influence the outcome of disease establishment and evolution

Uso dei codici di calcolo per l’analisi sismica nonlineare di edifici in muratura: confronto dei risultati ottenuti con diversi software su un caso studio reale = Use of computer programs for the nonlinear seismic analysis of masonry buildings: comparison of the results obtained with different software on an actual case study

L’articolo presenta il confronto dei risultati ottenuti su un edificio in muratura da analisi statiche nonlineari utilizzando diversi software, disponibili anche a livello professionale, che operano nell’ambito di vari approcci di modellazione. La struttura esaminata è ispirata ad un edificio reale, la scuola “P. Capuzi” di Visso (MC), gravemente danneggiata a seguito degli eventi sismici che hanno interessato il Centro Italia nel 2016/2017. L’attività descritta si inquadra in un più ampio programma di ricerca svolto in sinergia da varie unità coinvolte nel progetto ReLUIS 2017/2108 – Linea Strutture in Muratura e avente come oggetto l’analisi di strutture benchmark per la valutazione dell’affidabilità di codici di calcolo. Obiettivi più generali dell’attività sono quelli di sensibilizzare i professionisti ad un uso più consapevole dei software e fornire loro strumenti utili ad analizzare criticamente qualità e correttezza delle soluzioni ottenute. I dati di input e alcune scelte di modellazione sono stati condivisi al fine di limitare la potenziale dispersione dei risultati e renderne meno problematico il confronto. Quest’ultima finalità è guidata dalla volontà di ottimizzare la fase di controllo dei risultati, per la quale sono state individuate precise modalità operative. Il confronto delle analisi è stato svolto in relazione a parametri: globali (inerenti le proprietà dinamiche, le curve di capacità globale e le relative curve bilineari equivalenti), sintetici della sicurezza strutturale (quale, ad esempio, l’accelerazione massima compatibile con lo stato limite di salvaguardia della vita) e interpretativi della risposta complessiva (relativi al quadro di danno simulato). I risultati presentati consentono di eseguire delle riflessioni sull’utilizzo dei software, sulla dispersione dei risultati ottenibili e sulle potenziali ricadute in ambito professionale.----The paper presents the comparison of the results of nonlinear static analyses performed on a masonry building by adopting different computer programs (available also for professional use) that work within various modelling approaches. The examined case study is inspired by an existing building, the school “P. Capuzi” of Visso (MC), significantly damaged after the 2016/2017 seismic events which hit the Central Italy. The activity here described is part of a wider research program carried out within the ReLUIS 2017/2018 project – Line Masonry Structures focused on the analysis of benchmark structures for assessing the reliability of software packages. The general activity’s objectives are to support the professional engineers in acquiring a proper awareness in the use of software packages and to provide them proper tools for the critical analysis of the obtained results. The input data and some modelling assumptions have been shared in order to limit the possible scattering of the results and make less problematic their comparison. This latter aim is driven by the will to optimize the further phase of results’ check, for which specific operative procedures have been defined. The comparison of the analyses involves different aspects: the global scale response (in terms of dynamic properties, capacity curves and corresponding equivalent bi-linear curves); concise parameters concerning the structural safety (such as, for example, the maximum acceleration corresponding to the achievement of the “life safety” limit state); parameters aimed to interpret the whole global response (related to the predicted damage pattern). The presented results allow thinking about the use of software, the scattering of the obtained results and the possible consequences on the engineering practice