Discovery and recognition of motion primitives in human activities

We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the `motion flux', a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis

Dispositivo multifunzionale per la stimolazione di scaffold: protein electroadsorption su polimeri conduttivi nanostrutturati

La matrice extracellulare (extracellular matrix, ECM) è una struttura molto complessa che circonda e supporta le cellule dei tessuti; essa funge da sostegno strutturale per i tessuti e da elemento di trasmissione dei segnali in quanto regola lo sviluppo, la migrazione, la proliferazione, la forma e la funzione delle cellule a stretto contatto con essa. Questo aspetto merita una particolare attenzione in fase di progettazione di uno scaffold, che dovrebbe mimare più possibile la struttura della ECM naturale. L'ECM è costituita principalmente da proteine strutturali e proteine di adesione (fibronectina, laminina, tenascina e vitronectina). L'originalità di tale lavoro si basa sulla progettazione e realizzazione del dispositivo e sulla realizzazione di uno scaffold funzionale su cui il dispositivo è stato testato. Il dispositivo ha il vantaggio di essere costituito da elementi componibili. Tale sistema prevede l'inserimento di elettrodi per stimolare substrati conduttivi (contact electrode, che fungono da elettrodi di lavoro in caso di processi elettrochimici) o per stimolare le cellule attraverso campi elettrici (non contact electrode). In questo lavoro di tesi il dispositivo è stato testato per esperimenti di electroadsorbption che hanno avuto lo scopo di funzionalizzare con diverse proteine degli scaffold polimerici conduttivi micro- e nanocorrugati realizzati e caratterizzati opportunamente

Vision-based deep execution monitoring

Execution monitor of high-level robot actions can be effectively improved by visual monitoring the state of the world in terms of preconditions and postconditions that hold before and after the execution of an action. Furthermore a policy for searching where to look at, either for verifying the relations that specify the pre and postconditions or to refocus in case of a failure, can tremendously improve the robot execution in an uncharted environment. It is now possible to strongly rely on visual perception in order to make the assumption that the environment is observable, by the amazing results of deep learning. In this work we present visual execution monitoring for a robot executing tasks in an uncharted Lab environment. The execution monitor interacts with the environment via a visual stream that uses two DCNN for recognizing the objects the robot has to deal with and manipulate, and a non-parametric Bayes estimation to discover the relations out of the DCNN features. To recover from lack of focus and failures due to missed objects we resort to visual search policies via deep reinforcement learning

Rigid tool affordance matching points of regard

In this abstract we briefly introduce the analysis of simple rigid object affordance by experimentally establishing the relation between the point of regard of subjects before grasping an object and the finger tip points of contact once the object is grasped. The analysis show that there is a strong relation between these data, in so justifying the hypothesis that people figures out how objects are afforded according to their functionality

The Role of Expectations: An Application to Internal Migration

This paper examines the impact of unemployment on migration. In a theoretical model, we show that unemployment, per se, does not affect migration. Rather, migration only occurs when unemployment shocks force residents to update their expectations of the area\u27s unemployment rate. Once these expectations change, migration reallocates labor to bring the economy back to equilibrium. To test this theory, we devise an empirical strategy using state level data in the U.S. from 2000 to 2010, we find strong empirical evidence that unemployment shocks outside of expectations have a far greater impact on migration than unemployment shocks that are within expectations

Component-wise modeling of articulated objects

We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time

Etude de l’effet du Solacyl® sur le stress des porcelets au sevrage

Le sevrage est une période particulièrement stressante pour les porcelets, notamment liée aux intéractions avec de nouveaux congénères et à un nouvel environnement. Cette pratique a un impact important sur le bien-être et la productivité. Le but de cette étude est de déterminer quels paramètres comportementaux, biologiques et zootechniques peuvent être utilisés pour évaluer le stress suite à l’administration de salicylate de sodium, dans un système commercial intensif. L’étude a été réalisée sur 427 porcelets, de 3 semaines d’âge. 49 jours après le sevrage, il semble que les porcs du groupe traité, suivis individuellement, ont un GMQ plus élevé. A propos des critères biologiques, aucune conclusion n’a pu être formulée dû au manque de données. Aucune différence comportementale majeure entre les deux groupes n’a été mise en évidence. Il serait intéressant de suivre davantage de porcelets, au moins 21 par lot, afin d’obtenir des résultats plus probants dans les expérimentations ultérieure

Bayesian non-parametric inference for manifold based MoCap representation

We propose a novel approach to human action recognition, with motion capture data (MoCap), based on grouping sub-body parts. By representing configurations of actions as manifolds, joint positions are mapped on a subspace via principal geodesic analysis. The reduced space is still highly informative and allows for classification based on a non-parametric Bayesian approach, generating behaviors for each sub-body part. Having partitioned the set of joints, poses relative to a sub-body part are exchangeable, given a specified prior and can elicit, in principle, infinite behaviors. The generation of these behaviors is specified by a Dirichlet process mixture. We show with several experiments that the recognition gives very promising results, outperforming methods requiring temporal alignment