Recognition of human activities through eSteps insoles

Recognition of everyday human activity through mobile personal sensing technology plays a central role in the field of pervasive healthcare. The Bologna-based American company eSteps Inc. addresses the growing motor disability of the lower limbs by offering pre-, during and post-hospitalisation monitoring solutions with biomechanics and telerehabilitation protocol. It has developed a smart, customised and sustainable device to monitor motor activity, fatigue and injury risk for patients and a special app to share data with caregivers and medical specialists. The objective of this study is the development of an Artificial Intelligence model to recognize the activity performed by a person with Multiple Sclerosis or a healthy person through eSteps devices.

Rilevamento della qualità del manto stradale: un approccio sperimentale tramite Machine Learning

Il riconoscimento delle condizioni del manto stradale partendo esclusivamente dai dati raccolti dallo smartphone di un ciclista a bordo del suo mezzo è un ambito di ricerca finora poco esplorato. Per lo sviluppo di questa tesi è stata sviluppata un'apposita applicazione, che combinata a script Python permette di riconoscere differenti tipologie di asfalto. L’applicazione raccoglie i dati rilevati dai sensori di movimento integrati nello smartphone, che registra i movimenti mentre il ciclista è alla guida del suo mezzo. Lo smartphone è fissato in un apposito holder fissato sul manubrio della bicicletta e registra i dati provenienti da giroscopio, accelerometro e magnetometro. I dati sono memorizzati su file CSV, che sono elaborati fino ad ottenere un unico DataSet contenente tutti i dati raccolti con le features estratte mediante appositi script Python. A ogni record sarà assegnato un cluster deciso in base ai risultati prodotti da K-means, risultati utilizzati in seguito per allenare algoritmi Supervised. Lo scopo degli algoritmi è riconoscere la tipologia di manto stradale partendo da questi dati. Per l’allenamento, il DataSet è stato diviso in due parti: il training set dal quale gli algoritmi imparano a classificare i dati e il test set sul quale gli algoritmi applicano ciò che hanno imparato per dare in output la classificazione che ritengono idonea. Confrontando le previsioni degli algoritmi con quello che i dati effettivamente rappresentano si ottiene la misura dell’accuratezza dell’algoritmo.

Deep learning models for the automatic analysis of volleyball plays

Collecting and analysing data is an important element in any field of human activity and research. Even in sports, collecting and analyzing statistical data is attracting a growing interest. Some exemplar use cases are: improvement of technical/tactical aspects for team coaches, definition of game strategies based on the opposite team play or evaluation of the performance of players. Other advantages are related to taking more precise and impartial judgment in referee decisions: a wrong decision can change the outcomes of important matches. Finally, it can be useful to provide better representations and graphic effects that make the game more engaging for the audience during the match. Nowadays it is possible to delegate this type of task to automatic software systems that can use cameras or even hardware sensors to collect images or data and process them. One of the most efficient methods to collect data is to process the video images of the sporting event through mixed techniques concerning machine learning applied to computer vision. As in other domains in which computer vision can be applied, the main tasks in sports are related to object detection, player tracking, and to the pose estimation of athletes. The goal of the present thesis is to apply different models of CNNs to analyze volleyball matches. Starting from video frames of a volleyball match, we reproduce a bird's eye view of the playing court where all the players are projected, reporting also for each player the type of action she/he is performing.

Robotics and neurodevelopmental disorder: design and implementation of a system of engagement metrics in human-robot interactions.

In the recent decades, robotics has become firmly embedded in areas such as education, teaching, medicine, psychology and many others. We focus here on social robotics; social robots are designed to interact with people in a natural and interpersonal way, often to achieve positive results in different applications. To interact and cooperate with humans in their daily-life activities, robots should exhibit human-like intelligence. The rapid expansion of social robotics and the existence of various kinds of robots on the market have allowed research groups to carry out multiple experiments. The experiments carried out have led to the collections of various kinds of data, which can be used or processed for psychological studies, and studies in other fields. However, there are no tools available in which data can be stored, processed and shared with other research groups. This thesis proposes the design and implementation of visual tool for organizing dataflows in Human Robot Interaction (HRI).

Centrifuge Modeling of Sandy Slopes

Slope failure occurs in many areas throughout the world and it becomes an important problem when it interferes with human activity, in which disasters provoke loss of life and property damage. In this research we investigate the slope failure through the centrifuge modeling, where a reduced-scale model, N times smaller than the full-scale (prototype), is used whereas the acceleration is increased by N times (compared with the gravity acceleration) to preserve the stress and the strain behavior. The aims of this research “Centrifuge modeling of sandy slopes” are in extreme synthesis: 1) test the reliability of the centrifuge modeling as a tool to investigate the behavior of a sandy slope failure; 2) understand how the failure mechanism is affected by changing the slope angle and obtain useful information for the design. In order to achieve this scope we arranged the work as follows: Chapter one: centrifuge modeling of slope failure. In this chapter we provide a general view about the context in which we are working on. Basically we explain what is a slope failure, how it happens and which are the tools available to investigate this phenomenon. Afterwards we introduce the technology used to study this topic, that is the geotechnical centrifuge. Chapter two: testing apparatus. In the first section of this chapter we describe all the procedures and facilities used to perform a test in the centrifuge. Then we explain the characteristics of the soil (Nevada sand), like the dry unit weight, water content, relative density, and its strength parameters (c,φ), which have been calculated in laboratory through the triaxial test. Chapter three: centrifuge tests. In this part of the document are presented all the results from the tests done in centrifuge. When we talk about results we refer to the acceleration at failure for each model tested and its failure surface. In our case study we tested models with the same soil and geometric characteristics but different angles. The angles tested in this research were: 60°, 75° and 90°. Chapter four: slope stability analysis. We introduce the features and the concept of the software: ReSSA (2.0). This software allows us to calculate the theoretical failure surfaces of the prototypes. Then we show in this section the comparisons between the experimental failure surfaces of the prototype, traced in the laboratory, and the one calculated by the software. Chapter five: conclusion. The conclusion of the research presents the results obtained in relation to the two main aims, mentioned above.

Riconoscimento automatico di attività attraverso i Google Play Services: una valutazione sperimentale

Tesi sperimentale per testare la nuova libreria di Activity Recognition per Android di Google inc.

Progettazione ed implementazione di un'applicazione Android basata sul riconoscimento automatico di attività

Progettazione ed implementazione di una semplice applicazione per smartphone Android al fine di dimostrare le funzionalità delle librerie per l'activity recognition messe a disposizione dai Google Play Services. Lo studio esplora il campo di ricerca in generale, mostrandone le modalità, le applicazioni e le problematiche, e introduce l'ambiente Android per poi analizzare l'applicazione progettata. In conclusione, vengono mostrati alcuni test svolti per verificare l'accuratezza del classificatore implementato da Google.

Progettazione di un applicativo di supporto al monitoraggio di team situati

Lo scopo della tesi e quello di giungere alla realizzazione di un prototipo, che si basi su tecnologie specifiche quali GPS, Android e Bluetooth, per l'infrastruttura di un sistema che può essere visto come l'unione di tre macro parti distinte, centrale di controllo, dispositivo mobile e pulsossimetro. Concentrandosi in particolare sugli ultimi due componenti citati e realizzando un sistema di comunicazione che si basa su un Web Service ispirato al modello REST, si giungerà, attraverso un attento processo logico dettato dai canoni dell'ingegneria del software, al prototipo finale. Il prototipo che sarà realizzato rappresenterà oltre che un primo sistema funzionante e operativo, un punto di partenza per estensioni future, con lo scopo di perfezionare le funzionalità già esistenti o di fornirne di aggiuntive.

Ruolo della gorgonia Paramuricea clavata (Risso 1826) nei popolamenti epibentonici coralligeni dell'Isola d'Elba

The red gorgonian Paramuricea clavata is considered a key species in Mediterranean coralligenous habitats and, when present at high density, its colonies may develop “gorgonian forests”, which are three-dimensional structures that increase habitat complexity. Mediterranean coralligenous habitats support high biodiversity but the structure and heterogeneity of communities can be strongly modified by several kinds of human-derived impacts. The global environmental change and human activity could threaten the coralligenous habitats and during the last few decades, Mediterranean suspension feeders have been involved in mass mortality events, in which P. clavata was one of the most affected. Without the complex three-dimensional structures formed by P. clavata, many benthic species may have not adequate sheltering and feeding opportunities. The aim of the present study was to investigate the ecological role of Paramuricea clavata in coralligenous habitats by comparing epibenthic assemblages established inside and outside gorgonian forests. This study was carried out in four sites randomly chosen, along the south-east coast of Elba island (north-western Mediterranean). The structure of the epibenthic assemblages were significantly different between area with and without gorgonians. Main differences concern very important taxa in the coralligenous bioconstruction processes like the encrusting red algae, Peyssonnelia spp. and Halimeda tuna, which were more abundant in presence of gorgonians. On the contrary, algal turf, mucilaginous algae and the invasive seaweed Caulerpa cylindracea were more abundant in areas without P. clavata. The presence of gorgonians may have a positive effect also on species diversity. The results of this study highlight the ecological role of P. clavata in the coralligenous habitats.

Observations and modeling of the Marginal Ice Zone

Global climate change in recent decades has strongly influenced the Arctic generating pronounced warming accompanied by significant reduction of sea ice in seasonally ice-covered seas and a dramatic increase of open water regions exposed to wind [Stephenson et al., 2011]. By strongly scattering the wave energy, thick multiyear ice prevents swell from penetrating deeply into the Arctic pack ice. However, with the recent changes affecting Arctic sea ice, waves gain more energy from the extended fetch and can therefore penetrate further into the pack ice. Arctic sea ice also appears weaker during melt season, extending the transition zone between thick multi-year ice and the open ocean. This region is called the Marginal Ice Zone (MIZ). In the Arctic, the MIZ is mainly encountered in the marginal seas, such as the Nordic Seas, the Barents Sea, the Beaufort Sea and the Labrador Sea. Formed by numerous blocks of sea ice of various diameters (floes) the MIZ, under certain conditions, allows maritime transportation stimulating dreams of industrial and touristic exploitation of these regions and possibly allowing, in the next future, a maritime connection between the Atlantic and the Pacific. With the increasing human presence in the Arctic, waves pose security and safety issues. As marginal seas are targeted for oil and gas exploitation, understanding and predicting ocean waves and their effects on sea ice become crucial for structure design and for real time safety of operations. The juxtaposition of waves and sea ice represents a risk for personnel and equipment deployed on ice, and may complicate critical operations such as platform evacuations. The risk is difficult to evaluate because there are no long-term observations of waves in ice, swell events are difficult to predict from local conditions, ice breakup can occur on very short time-scales and wave-ice interactions are beyond the scope of current forecasting models [Liu and Mollo-Christensen, 1988,Marko, 2003]. In this thesis, a newly developed Waves in Ice Model (WIM) [Williams et al., 2013a,Williams et al., 2013b] and its related Ocean and Sea Ice model (OSIM) will be used to study the MIZ and the improvements of wave modeling in ice infested waters. The following work has been conducted in collaboration with the Nansen Environmental and Remote Sensing Center and within the SWARP project which aims to extend operational services supporting human activity in the Arctic by including forecast of waves in ice-covered seas, forecast of sea-ice in the presence of waves and remote sensing of both waves and sea ice conditions. The WIM will be included in the downstream forecasting services provided by Copernicus marine environment monitoring service.

A framework for the environmental risk assessment of offshore carbon storage in CCS systems

Carbon capture and storage (CCS) represents an interesting climate mitigation option, however, as for any other human activity, there is the impelling need to assess and manage the associated risks. This study specifically addresses the marine environmental risk posed by CO2 leakages associated to CCS subsea engineering system, meant as offshore pipelines and injection / plugged and abandoned wells. The aim of this thesis work is to start approaching the development of a complete and standardized practical procedure to perform a quantified environmental risk assessment for CCS, with reference to the specific activities mentioned above. Such an effort would be of extreme relevance not only for companies willing to implement CCS, as a methodological guidance, but also, by uniformizing the ERA procedure, to begin changing people’s perception about CCS, that happens to be often discredited due to the evident lack of comprehensive and systematic methods to assess the impacts on the marine environment. The backbone structure of the framework developed consists on the integration of ERA’s main steps and those belonging to the quantified risk assessment (QRA), in the aim of quantitatively characterizing risk and describing it as a combination of magnitude of the consequences and their frequency. The framework developed by this work is, however, at a high level, as not every single aspect has been dealt with in the required detail. Thus, several alternative options are presented to be considered for use depending on the situation. Further specific studies should address their accuracy and efficiency and solve the knowledge gaps emerged, in order to establish and validate a final and complete procedure. Regardless of the knowledge gaps and uncertainties, that surely need to be addressed, this preliminary framework already finds some relevance in on field applications, as a non-stringent guidance to perform CCS ERA, and it constitutes the foundation of the final framework.

Analysis of human posterior EEG alpha frequency as a function of implicit learning of probabilistic lateralized stimuli in a visual detection task

Alpha oscillatory activity has long been associated with perceptual and cognitive processes related to attention control. The aim of this study is to explore the task-dependent role of alpha frequency in a lateralized visuo-spatial detection task. Specifically, the thesis focuses on consolidating the scientific literature's knowledge about the role of alpha frequency in perceptual accuracy, and deepening the understanding of what determines trial-by-trial fluctuations of alpha parameters and how these fluctuations influence overall task performance. The hypotheses, confirmed empirically, were that different implicit strategies are put in place based on the task context, in order to maximize performance with optimal resource distribution (namely alpha frequency, associated positively with performance): “Lateralization” of the attentive resources towards one hemifield should be associated with higher alpha frequency difference between contralateral and ipsilateral hemisphere; “Distribution” of the attentive resources across hemifields should be associated with lower alpha frequency difference between hemispheres; These strategies, used by the participants according to their brain capabilities, have proven themselves adaptive or maladaptive depending on the different tasks to which they have been set: "Distribution" of the attentive resources seemed to be the best strategy when the distribution probability between hemifields was balanced: i.e. the neutral condition task. "Lateralization" of the attentive resources seemed to be more effective when the distribution probability between hemifields was biased towards one hemifield: i.e., the biased condition task.