Emanuele Frontoni

Emanuele Frontoni
University of Macerata | UNIMC

Professor of computer engineering
We are looking forward for post-doc collaborators in Ai for Digital Humanities with 3 open positions in ML/DL & CV.

About

317
Publications
110,262
Reads
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3,701
Citations
Introduction
Emanuele Frontoni joined the Dept. of Information Engineering (DII) at the Università Politecnica delle Marche, as a Ph.D. student in "Intelligent Artificial Systems". He obtained his PhD in 2006 discussing a thesis on Vision Based Robotics. At present he has an Assistant Professor position in the same department. His research focuses on applying artificial intelligence and computer vision techniques to mobile robots. He is a member of IEEE, MESA, GIRPR and AI*IA.
Additional affiliations
January 2010 - October 2017
Università Politecnica delle Marche
Position
  • Professor (Associate)
May 2009 - June 2009
ETH Zurich
Position
  • Visiting student
January 2005 - present
Università Politecnica delle Marche
Education
March 2003 - January 2006
Università Politecnica delle Marche
Field of study
  • Electronic Engineering, Artificial Intelligence

Publications

Publications (317)
Article
Full-text available
Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously...
Article
Background and objectives: The use of deep learning for preterm infant's movement monitoring has the potential to support clinicians in early recognizing motor and behavioural disorders. The development of deep learning algorithms is, however, hampered by the lack of publicly available annotated datasets. Methods: To mitigate the issue, this pap...
Chapter
Archival institutions and program worldwide work to ensure that the records of governments, organizations, communities and individuals be preserved for the next generations as cultural heritage, as sources of rights, and to hold the past accountable. The digitalization of ancient written documents made of parchment were an important communication m...
Article
Full-text available
Nowadays, the volume of the multimedia heterogeneous evidence presented for digital forensic analysis has significantly increased, thus requiring the application of big data technologies, cloud-based forensics services, as well as Machine Learning (ML) techniques. In digital forensics domain, ML algorithms have been applied for cybercrime investiga...
Article
Full-text available
Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking int...
Article
Nowadays, understanding and analysing visitors activities and behaviours is becoming imperative for personalising and improving the user experience in a museum environment. Users’ behaviour can provide important statistics, insights and objective information about their interactions, such as attraction, attention and action. These data represent a...
Article
Aim To construct predictive models of diabetes complications (DCs) by big data machine learning, based on electronic medical records. Methods Six groups of DCs were considered: eye complications, cardiovascular, cerebrovascular, and peripheral vascular disease, nephropathy, diabetic neuropathy. A supervised, tree-based learning approach (XGBoost)...
Article
Full-text available
Over the past few decades, the substantial growth in enterprise-data availability and the advancements in Artificial Intelligence (AI) have allowed companies to solve real-world problems using Machine Learning (ML). ML Operations (MLOps) represents an effective strategy for bringing ML models from academic resources to useful tools for solving prob...
Article
Full-text available
The Internet of Things (IoT), Big Data and Machine Learning (ML) may represent the foundations for implementing the concept of intelligent production, smart products, services, and predictive maintenance (PdM). The majority of the state-of-the-art ML approaches for PdM use different condition monitoring data (e.g. vibrations, currents, temperature,...
Article
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Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objec...
Article
Continuing monitoring preterm infants’ spontaneous motility during and after the hospitalization is of primary importance to early recognising preterm-birth neurological complications. This work proposes a convolutional neural network (CNN)-based pipeline to estimate preterm infants’ limb pose from depth images acquired in the neonatal intensive ca...
Chapter
This work proposes a pipeline that aims to recognize the products in a shelf, at the level of the single SKU (Stock Keeping Unit), starting from a photo of that shelf. It is composed of a first neural network that detects the individual products on the shelf and has been trained with the SKU110K dataset and a second network, designed and built with...
Article
Full-text available
This paper introduces a new method for determining the shape similarity of complex three-dimensional (3D) mesh structures based on extracting a vector of important vertices, ordered according to a matrix of their most important geometrical and topological features. The correlation of ordered matrix vectors is combined with perceptual definition of...
Article
Retailers need to manage a series of complex decisions relating to numerous products. To reduce this complexity, they have introduced category management practices, which consider groups of similar products (categories) that can be managed separately as single business units (SBUs). Although the concept that the store offer should be organised as a...
Preprint
Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities, and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the past accountable and to inform the future. This commitment is guaranteed through the adoption of str...
Article
Full-text available
Background Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerv...
Preprint
Full-text available
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a t...
Article
Full-text available
Fashion knowledge encourages people to properly dress and faces not only physiological necessity of users, but also the requirement of social practices and activities. It usually includes three jointly related aspects of: occasion, person and clothing. Nowadays, social media platforms allow users to interact with each other online to share opinions...
Article
Objective Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients. Cartilage damage is nowadays assessed by clinicians, which manually measure carti...
Conference Paper
Purpose – In last decades, digital technologies have progressively transformed tourism becoming an opportunity to satisfy the demand for cultural tourism, increasingly asking for immersive and interactive experiences. This paper investigates the connections among tourism, cultural heritage, and ICT, by providing an assessment of how these applicati...
Article
Full-text available
Diabetic Retinopathy (DR) is the most common and insidious microvascular complication of diabetes, and can progress asymptomatically until a sudden loss of vision occurs. Although DR is prevalent nowadays, its prevention remains challenging. The multiple aim of this study was to predict the the risk of developing DR as diabetic complication (task 1...
Article
This study defines a methodology to measure physical activity (PA) in ageing people working in a social garden while maintaining social distancing (SD) during COVID-19 pandemic. A real-time location system (RTLS) with embedded inertial measurement unit (IMU) sensors is used for measuring PA and SD. The position of each person is tracked to assess t...
Conference Paper
Preterm infants' spontaneous motility is a valuable diagnostic and prognostic index of motor and cognitive impairments. Despite being recognized as crucial, preterm infant's movement assessment is mostly based on clinicians' visual inspection. The aim of this work is to present a 2D dense convolutional neural network (denseCNN) to detect preterm in...
Conference Paper
Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting preterm-birth complications. With such a view, in this work we propose a deep-learning framework for preterm infants' pose estimation from depth videos acquired in the actual clinical practice. The pipelin...
Conference Paper
Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy. Ultrasound imaging (US) may help to diagnose and assess CTS, through the evaluation of median nerve morphology. To support sonographers, this paper proposes a fully-automatic deep-learning approach to median nerve segmentation from US images. The approach relies on Mask R-CNN, a...
Article
Full-text available
Background : Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as...
Chapter
This paper presents a new technique for the virtual reality (VR) visualization of complex volume images obtained from computer tomography (CT) and Magnetic Resonance Imaging (MRI) by combining three-dimensional (3D) mesh processing and software coding within the gaming engine. The method operates on real representations of human organs avoiding any...
Chapter
The motor synthesis of humanoid characters is one of the main problems in data-driven animations, with applications in robotics, entertainment and game development. Both in the commercial and academic fields there is a strong interest in developing new synthesis techniques. The attention of researchers in this field has recently turned to artificia...
Article
Wastewater treatment is one of the major carriers of the water-energy-food-climate (WEFC) nexus, and although the relationship between water and energy is well recognized, there is still a lack of adequate analysis of the cyber-physical framework to address and assess urban and peri-urban WEFC nexus in an integrated approach. In this review paper,...
Article
Full-text available
Food legumes are crucial for all agriculture-related societal challenges including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating signific...
Conference Paper
Monitoring fish stocks and fleets’ activities is key for Marine Spatial Planning. In recent years Vessel Monitoring System and Automatic Identification System have been developed for vessels longer than 12 and 15m in length, respectively, while small scale vessels (< 12m in length) remain untracked and largely unregulated, even though they account...
Conference Paper
A planogram is the graphical representation of the way a given number of products are positioned within the shelves in a store. The creation of a correct planogram is a fundamental tool for a store’s performance: it helps to increase sales and achieve maximum customer satisfaction by reducing out-of-stocks. To this end, this work aims to provide an...
Article
Full-text available
Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent tracking systems. This complex subject comes with different challenges, such as human-space interaction, human-human interaction, multimodality, and generaliza...
Article
Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-bo...
Article
Full-text available
The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced natur...
Preprint
Full-text available
Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-bo...
Article
Full-text available
Background and objectives Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based appr...
Article
Full-text available
Sentiment analysis on social media such as Twitter is a challenging task given the data characteristics such as the length, spelling errors, abbreviations, and special characters. Social media sentiment analysis is also a fundamental issue with many applications. With particular regard of the tourism sector, where the characterization of fluxes is...
Conference Paper
Full-text available
In the Cultural Heritage (CH) domain, the semantic segmentation of 3D point clouds with Deep Learning (DL) techniques allows to recognize historical architectural elements, at a suitable level of detail, and hence expedite the process of modelling historical buildings for the development of BIM models from survey data. However, it is more difficult...
Article
Full-text available
Kidney Disease (KD) may hide complex causes and is associated with a tremendous socio-economic impact. A timely identification and management from the first level of medical care represent the most effective strategy to address the growing global burden sustainably. Clinical practice guidelines suggest utilizing estimated Glomerular Filtration Rate...
Chapter
Full-text available
The traditional data quality control (QC) process was usually limited by the high time consuming and high resources demand, in addition to a limit in performance mainly due to the high intrinsic variability across different annotators. The application of Deep Learning (DL) strategies for solving the QC task open the realm of possibilities in order...
Chapter
Full-text available
Smart production is trying to bring companies into the world of industry 4.0. In this field, leather is a natural product commonly used as a raw material to manufacture luxury objects. To ensure good quality on these products, one of the fundamental processes is the visual inspection phase to identify defects on leather surfaces. A typical exercise...
Article
Full-text available
Objectives: This study aims to develop an automatic deep-learning algorithm, which is based on Convolutional Neural Networks (CNNs), for ultrasound informative-image selection of hyaline cartilage at metacarpal head level. The algorithm performance and that of three beginner sonographers were compared with an expert assessment, which was considered...
Chapter
Full-text available
Detecting and tracking people is a challenging task in a persistent crowded environment as retail, airport or station, for human behaviour analysis of security purposes. Especially during the global spread of SARS-CoV-2 virus that has become part of everyday life in every country, it is important to be able to manage the flows inside and outside bu...
Chapter
The purpose of this study is to investigate new forms of marketing data-driven knowledge discovery in the vending machine (VM) industry. Data of shopping activities understanding were gathered and analyzed by a system technology based on a RGBD camera. An RGBD camera, already tested in retail environments, is installed in top-view configuration on...
Chapter
Nowadays Human Pose Estimation (HPE) represents one of the main research themes in the field of computer vision. Despite innovative methods and solutions introduced for frame processing algorithms, the use of standard frame-based cameras still has several drawbacks such as data redundancy and fixed frame-rate. The use of event-based cameras guarant...
Chapter
Spine surgery is nowadays performed for a great number of spine pathologies; it is estimated that 4.83 million surgeries are carried out globally each year. This prevalence led to an evolution of spine surgery into an extremely specialized field, so that traditional open interventions to the spine were integrated and often replaced by minimally inv...
Article
Background: The use of innovative methodologies, such as Surgical Data Science (SDS), based on artificial intelligence (AI) could prove to be useful for extracting knowledge from clinical data overcoming limitations inherent in medical registries analysis. The aim of the study is to verify if the application of an AI analysis to our database could...
Chapter
Measuring the behaviour and health status of informal caregivers of people with dementia can predict the personal well-being of the caregivers. Informal caregivers struggle to remain active during the daily life activities avoiding the care burden. For this reason, in this work, an analysis of both the environmental data coming from PIR sensors, in...
Article
Background and objectives: During Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the fetuses. In the current practice, this syndrome is surgically treated by closing the abnormal connections using laser ablation. Surgeons commonly use the inter-fetal membr...
Article
Background and Objectives Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intra- and inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressi...
Article
Augmented Reality (AR) and Virtual Reality (VR) applications has been investigated in several domains. As such, their application in educational settings has witnessed to a growing interest by the research community. Teachers can be assisted by AR/VR, in a way that students can strength the learning outcomes, gained during the classroom lecture. Ho...
Article
Background Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as th...