3LP: a linear 3D-walking model including torso and swing dynamics

In this paper, we present a new model of biped locomotion which is composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to double support, this model has different actuation possibilities in the swing hip and stance ankle which could be widely used to produce different walking gaits. Without the need for numerical time-integration, closed-form solutions help finding periodic gaits which could be simply scaled in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such property is coming from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract high level template space. The proposed model produces human-like torque and ground reaction force profiles and thus, compared to point-mass models, it is more promising for precise control of humanoid robots. Despite being linear and lacking many other features of human walking like CoM excursion, knee flexion and ground clearance, we show that the proposed model can predict one of the main optimality trends in human walking, i.e. nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis still remain for future works.Comment: Journal paper under revie

Functional Dynamics of PDZ Binding Domains: A Normal Mode Analysis

PDZ (Post-synaptic density-95/discs large/zonula occludens-1) domains are relatively small (80 to 120 residues) protein binding modules central in the organization of receptor clusters and in the association of cellular proteins. Their main function is to bind C-terminals of selected proteins that are recognized through specific amino-acids in their carboxyl end. Binding is associated with a deformation of the PDZ native structure and is responsible for dynamical changes in regions not in direct contact with the target. We investigate how this deformation is related to the harmonic dynamics of the PDZ structure and show that one low-frequency collective normal mode, characterized by the concerted movements of different secondary structures, is involved in the binding process. Our results suggest that even minimal structural changes are responsible of communication between distant regions of the protein, in agreement with recent Nuclear Magnetic Resonance (NMR) experiments. Thus PDZ domains are a very clear example of how collective normal modes are able to characterize the relation between function and dynamics of proteins, and to provide indications on the precursors of binding/unbonding events.Comment: 25 pages, 10 figures, submitted to Biophysical Journa

Research on robust salient object extraction in image

制度:新 ; 文部省報告番号:甲2641号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480

An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors

Event-Driven vision sensing is a new way of sensing visual reality in a frame-free manner. This is, the vision sensor (camera) is not capturing a sequence of still frames, as in conventional video and computer vision systems. In Event-Driven sensors each pixel autonomously and asynchronously decides when to send its address out. This way, the sensor output is a continuous stream of address events representing reality dynamically continuously and without constraining to frames. In this paper we present an Event-Driven Convolution Module for computing 2D convolutions on such event streams. The Convolution Module has been designed to assemble many of them for building modular and hierarchical Convolutional Neural Networks for robust shape and pose invariant object recognition. The Convolution Module has multi-kernel capability. This is, it will select the convolution kernel depending on the origin of the event. A proof-of-concept test prototype has been fabricated in a 0.35 m CMOS process and extensive experimental results are provided. The Convolution Processor has also been combined with an Event-Driven Dynamic Vision Sensor (DVS) for high-speed recognition examples. The chip can discriminate propellers rotating at 2 k revolutions per second, detect symbols on a 52 card deck when browsing all cards in 410 ms, or detect and follow the center of a phosphor oscilloscope trace rotating at 5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Bayesian modeling of biological motion perception in sport

La perception d’un mouvement biologique correspond à l’aptitude à recueillir des informations (comme par exemple, le type d’activité) issues d’un objet animé en mouvement à partir d’indices visuels restreints. Cette méthode a été élaborée et instaurée par Johansson en 1973, à l’aide de simples points lumineux placés sur des individus, à des endroits stratégiques de leurs articulations. Il a été démontré que la perception, ou reconnaissance, du mouvement biologique joue un rôle déterminant dans des activités cruciales pour la survie et la vie sociale des humains et des primates. Par conséquent, l’étude de l’analyse visuelle de l’action chez l’Homme a retenu l’attention des scientifiques pendant plusieurs décennies. Ces études sont essentiellement axées sur informations cinématiques en provenance de différents mouvements (comme le type d’activité ou les états émotionnels), le rôle moteur dans la perception des actions ainsi que les mécanismes sous-jacents et les substrats neurobiologiques associés. Ces derniers constituent le principal centre d’intérêt de la présente étude, dans laquelle nous proposons un nouveau modèle descriptif de simulation bayésienne avec minimisation du risque. Ce modèle est capable de distinguer la direction d’un ballon à partir d’un mouvement biologique complexe correspondant à un tir de soccer. Ce modèle de simulation est inspiré de précédents modèles, neurophysiologiquement possibles, de la perception du mouvement biologique ainsi que de récentes études. De ce fait, le modèle présenté ici ne s’intéresse qu’à la voie dorsale qui traite les informations visuelles relatives au mouvement, conformément à la théorie des deux voies visuelles. Les stimuli visuels utilisés, quant à eux, proviennent d’une précédente étude psychophysique menée dans notre laboratoire chez des athlètes. En utilisant les données psychophysiques de cette étude antérieure 3 et en ajustant une série de paramètres, le modèle proposé a été capable de simuler la fonction psychométrique ainsi que le temps de réaction moyen mesurés expérimentalement chez les athlètes. Bien qu’il ait été établi que le système visuel intègre de manière optimale l’ensemble des indices visuels pendant le processus de prise de décision, les résultats obtenus sont en lien avec l’hypothèse selon laquelle les indices de mouvement sont plus importants que la forme dynamique dans le traitement des informations relatives au mouvement. Les simulations étant concluantes, le présent modèle permet non seulement de mieux comprendre le sujet en question, mais s’avère également prometteur pour le secteur de l’industrie. Il permettrait, par exemple, de prédire l’impact des distorsions optiques, induites par la conception de verres progressifs, sur la prise de décision chez l’Homme. Mots-clés : Mouvement biologique, Bayésien, Voie dorsale, Modèle de simulation hiérarchique, Fonction psychométrique, Temps de réactionThe ability to recover information (e.g., identity or type of activity) about a moving living object from a sparse input is known as Biological Motion perception. This sparse input has been created and introduced by Johansson in 1973, using only light points placed on an individual's strategic joints. Biological motion perception/recognition proves to play a significant role in activities that are critical to the survival and social life of humans and primates. In this regard, the study of visual analysis of human action had the attention of scientists for decades. These studies are mainly focused on: kinematics information of the different movements (such as type of activity, emotional states), motor role in the perception of actions and underlying mechanisms, and associated neurobiological substrates. The latter being the main focus of the present study, a new descriptive risk-averse Bayesian simulation model, capable of discerning the ball’s direction from a set of complex biological motion soccer-kick stimuli is proposed. Inspired by the previous, neurophysiologically plausible, biological motion perception models and recent studies, the simulation model only represents the dorsal pathway as a motion information processing section of the visual system according to the two-stream theory, while the stimuli used have been obtained from a previous psychophysical study on athletes. Moreover, using the psychophysical data from the same study and tuning a set of parameters, the model could successfully simulate the psychometric function and average reaction time of the athlete participants of the aforementioned study. 5 Although it is established that the visual system optimally integrates all available visual cues in the decision-making process, the results conform to the speculations favouring motion cue importance over dynamic form by only depending on motion information processing. As a functioning simulator, the present simulation model not only introduces some insight into the subject at hand but also shows promise for industry use. For example, predicting the impact of the lens-induced distortions, caused by various lens designs, on human decision-making. Keywords: Biological motion, Bayesian, Dorsal pathway, Hierarchical simulation model, Psychometric function, Reaction tim