Deep Learning Systems for Advanced Driving Assistance

Next generation cars embed intelligent assessment of car driving safety through innovative solutions often based on usage of artificial intelligence. The safety driving monitoring can be carried out using several methodologies widely treated in scientific literature. In this context, the author proposes an innovative approach that uses ad-hoc bio-sensing system suitable to reconstruct the physio-based attentional status of the car driver. To reconstruct the car driver physiological status, the author proposed the use of a bio-sensing probe consisting of a coupled LEDs at Near infrared (NiR) spectrum with a photodetector. This probe placed over the monitored subject allows to detect a physiological signal called PhotoPlethysmoGraphy (PPG). The PPG signal formation is regulated by the change in oxygenated and non-oxygenated hemoglobin concentration in the monitored subject bloodstream which will be directly connected to cardiac activity in turn regulated by the Autonomic Nervous System (ANS) that characterizes the subject's attention level. This so designed car driver drowsiness monitoring will be combined with further driving safety assessment based on correlated intelligent driving scenario understanding

Visual Saliency Detection in Advanced Driver Assistance Systems

Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of interest in the field of automotive research regarding the estimation of visual saliency. While operating a vehicle, drivers naturally direct their attention towards specific objects, employing brain-driven saliency mechanisms that prioritize certain elements over others. In this investigation, we present an intelligent system that combines a drowsiness detection system for drivers with a scene comprehension pipeline based on saliency. To achieve this, we have implemented a specialized 3D deep network for semantic segmentation, which has been pretrained and tailored for processing the frames captured by an automotive-grade external camera. The proposed pipeline was hosted on an embedded platform utilizing the STA1295 core, featuring ARM A7 dual-cores, and embeds an hardware accelerator. Additionally, we employ an innovative biosensor embedded on the car steering wheel to monitor the driver drowsiness, gathering the PhotoPlethysmoGraphy (PPG) signal of the driver. A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series, enabling us to assess the driver level of attentiveness. Ultimately, we compare the determined attention level of the driver with the corresponding saliency-based scene classification to evaluate the overall safety level. The efficacy of the proposed pipeline has been validated through extensive experimental results

Infrastruttura VR OASI

Presentazione della sede di Troina (EN) dell'infrastruttura del progetto

A baseline on continual learning methods for video action recognition

Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models. However, most research on this subject has tackled continual learning in simple image classification scenarios. In this paper, we present a benchmark of state-of-the-art continual learning methods on video action recognition. Besides the increased complexity due to the temporal dimension, the video setting imposes stronger requirements on computing resources for top-performing rehearsal methods. To counteract the increased memory requirements, we present two method-agnostic variants for rehearsal methods, exploiting measures of either model confidence or data information to select memorable samples. Our experiments show that, as expected from the literature, rehearsal methods outperform other approaches; moreover, the proposed memory-efficient variants are shown to be effective at retaining a certain level of performance with a smaller buffer size

Early detection of hip periprosthetic joint infections through CNN on Computed Tomography images

Early detection of an infection prior to prosthesis removal (e.g., hips, knees or other areas) would provide significant benefits to patients. Currently, the detection task is carried out only retrospectively with a limited number of methods relying on biometric or other medical data. The automatic detection of a periprosthetic joint infection from tomography imaging is a task never addressed before. This study introduces a novel method for early detection of the hip prosthesis infections analyzing Computed Tomography images. The proposed solution is based on a novel ResNeSt Convolutional Neural Network architecture trained on samples from more than 100 patients. The solution showed exceptional performance in detecting infections with an experimental high level of accuracy and F-score

Remote Home-Based Virtual Training of Functional Living Skills for Adolescents and Young Adults With Intellectual Disability: Feasibility and Preliminary Results

Background: Virtual Reality (VR) is acquiring increasing credibility as a tool for teaching independent living skills to people with Intellectual Disability (ID). Generalization of skills acquired during VR training into real environment seems to be feasible.Objective: To assess feasibility and verify effectiveness of a remote home-based rehabilitation, focused on functional living skills, for adolescents and young adults with ID, by using virtual apps installed on tablets. In particular, to assess if this tool can be managed independently, if it is enjoyable and simple to be used, and if the acquired skills can be generalized to the real environment of everyday life.Subjects and method: A single group, pre- and post-test research design was used. Sixteen participants with ID were included. A digital system was arranged, with a server managing communication between the database and the apps installed on tablets. In vivo tests were performed before and after the eleven sessions of VR training. Satisfaction questionnaires were also administered.Results: Statistically significant improvements were found between the pre- and post-in vivo tests, as well as between the VR training sessions, in almost all the parameters taken into account, for each app. Final questionnaires showed a good satisfaction level for both the participants and their families.Conclusion: The highly technological system was managed independently by participants with ID, who found it simple to be used, useful and even fun; generalization across settings was obtained. Results obtained require to be confirmed by future controlled studies, with larger samples

Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices

Inverse modelling with deep learning algorithms involves training deep architecture to predict device's parameters from its static behaviour. Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. There are many variables that can influence the performance of an inverse modelling method. In this work the authors propose a deep learning method trained for retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET (SiC Power MOS). The SiC devices are used in applications where classical silicon devices failed due to high-temperature or high switching capability. The key application of SiC power devices is in the automotive field (i.e. in the field of electrical vehicles). Due to physiological degradation or high-stressing environment, SiC Power MOS shows a significant drift of physical parameters which can be monitored by using inverse modelling. The aim of this work is to provide a possible deep learning-based solution for retrieving physical parameters of the SiC Power MOSFET. Preliminary results based on the retrieving of channel length of the device are reported. Channel length of power MOSFET is a key parameter involved in the static and dynamic behaviour of the device. The experimental results reported in this work confirmed the effectiveness of a multi-layer perceptron designed to retrieve this parameter.Comment: 13 pages, 8 figures, to be published on Journal of Physics: Conference Serie

Le attività di VR all'IRCCS Oasi Maria SS

La presentazione mostra le attività che fanno uso della tecnologia della realtà virtuale (VR) che sono condotte presso l'IRCCS Oasi Maria SS di Troina (EN)

Modulation of the Muscle Activity During Sleep in Cervical Dystonia

Introduction: Impaired sleep has been reported as an important nonmotor feature in dystonia, but so far, self-reported complaints have never been compared with nocturnal video-polysomnographic (PSG) recording, which is the gold standard to assess sleep-related disorders. Methods: Twenty patients with idiopathic isolated cervical dystonia and 22 healthy controls (HC) underwent extensive clinical investigations, neurological examination, and questionnaire screening for excessive daytime sleepiness and sleep-related disorders. A full-night video PSG was performed in both patients and HC. An ad hoc montage, adding electromyographic leads over the muscle affected with dystonia, was used. Results: When compared to controls, patients showed significantly increased pathological values on the scale assessing self-reported complaints of impaired nocturnal sleep. Higher scores of impaired nocturnal sleep did not correlate with any clinical descriptors but for a weak correlation with higher scores on the scale for depression. On video-PSG, patients had significantly affected sleep architecture (with decreased sleep efficiency and increased sleep latency). Activity over cervical muscles disappears during all the sleep stages, reaching significantly decreased values when compared to controls both in nonrapid eye movements and rapid eye movements sleep. Conclusions: Patients with cervical dystonia reported poor sleep quality and showed impaired sleep architecture. These features however cannot be related to the persistence of muscle activity over the cervical muscles, which disappears in all the sleep stages, reaching significantly decreased values when compared to HC