Incremental Principal Component Analysis Exact implementation and continuity corrections

This paper describes some applications of an incremental implementation of the principal component analysis (PCA). The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory. The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples. In particular we focus on the problem of the continuity of the PCs during an on-line analysis.Comment: accepted at http://www.icinco.org

Support Vector Machine Classification on a Biased Training Set: Multi-Jet Background Rejection at Hadron Colliders

This paper describes an innovative way to optimize a multivariate classifier, in particular a Support Vector Machine algorithm, on a problem characterized by a biased training sample. This is possible thanks to the feedback of a signal-background template fit performed on a validation sample and included both in the optimization process and in the input variable selection. The procedure is applied to a real case of interest at hadron collider experiments: the reduction and the estimate of the multi-jet background in the $W\to e \nu$ plus jets data sample collected by the CDF experiment. The training samples, partially derived from data and partially from simulation, are described in detail together with the input variables exploited for the classification. At present, the reached performance is superior to any other prescription applied to the same final state at hadron collider experiments.Comment: 24 pages, 8 figures, preprint of NIM pape

Distributed Bio-inspired Humanoid Posture Control

This paper presents an innovative distributed bio-inspired posture control strategy for a humanoid, employing a balance control system DEC (Disturbance Estimation and Compensation). Its inherently modular structure could potentially lead to conflicts among modules, as already shown in literature. A distributed control strategy is presented here, whose underlying idea is to let only one module at a time perform balancing, whilst the other joints are controlled to be at a fixed position. Modules agree, in a distributed fashion, on which module to enable, by iterating a max-consensus protocol. Simulations performed with a triple inverted pendulum model show that this approach limits the conflicts among modules while achieving the desired posture and allows for saving energy while performing the task. This comes at the cost of a higher rise time.Comment: 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC

Filtering Motion Data Through Piecewise Polynomial Approximation

In this work we propose a system to filter human movement data and store them into a compact representation. We are interested both in noise reduction and in segmentation. The method described in this paper relies on a iterative optimization and guarantee to converge to a local optimum: it proved anyway to produce stable results and to provide an accurate segmentation on the analyzed data . We analyze the Three ball cascade Juggling as case study: This provides us the challenge to represent both low-pass dynamics of human limbs and juggled balls and the discontinuities produced by contact forces

Design and Development of a Human Gesture Recognition System in Tridimensional Interactive Virtual Environment

This thesis describes the design and the development of a recognition system for human gestures. The main goal of this work is to demonstrate the possibility to extract enough information, both semantic and quantitative, from the human action, to perform complex tasks in a virtual environment. To manage the complexity and the variability adaptive systems are exploited, both in building a codebook (by unsupervised neural networks), and to recognize the sequence of symbols describing a gesture (by Hidden Markov models)

Human-Likeness Indicator for Robot Posture Control and Balance

Similarly to humans, humanoid robots require posture control and balance to walk and interact with the environment. In this work posture control in perturbed conditions is evaluated as a performance test for humanoid control. A specific performance indicator is proposed: the score is based on the comparison between the body sway of the tested humanoid standing on a moving surface and the sway produced by healthy subjects performing the same experiment. This approach is here oriented to the evaluation of a human-likeness. The measure is tested using a humanoid robot in order to demonstrate a typical usage of the proposed evaluation scheme and an example of how to improve robot control on the basis of such a performance indicator scoreComment: 16 pages, 5 Figures. arXiv admin note: substantial text overlap with arXiv:2110.1439

A Method for Digital Representation of Human Movements

In this work we present a method to produce a model of human motion based on an expansion in functions series. The model is thought to reproduce the learned movements generalizing them to different conditions. We will show, with an example, how the proposed method is capable to produce the model from a reduced set of examples preserving the relevant features of the demonstrations while guaranteeing constraints at boundaries