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522 results on '"Anguita, Davide"'

155. Worst case analysis of weight inaccuracy effects in multilayer perceptrons

Author

Anguita, Davide, Ridella, Sandro, and Rovetta, Stefano

Subjects
Perceptrons -- Research, Neural networks -- Research, Business, Computers, Electronics, Electronics and electrical industries
Abstract

We derive here a new method for the analysis of weight quantization effects in multilayer perceptrons based on the application of interval arithmetic. Differently from previous results, we find worst case bounds on the errors due to weight quantization, that are valid for every distribution of the input or weight values. Given a trained network, our method allows to easily compute the minimum number of bits needed to encode its weights. Index Terms - Interval arithmetic, multilayer perceptron, quantization, robustness.

Published
1999

169. Understanding Violin Players' Skill Level Based on Motion Capture: a Data-Driven Perspective.

Author

D'Amato, Vincenzo, Volta, Erica, Oneto, Luca, Volpe, Gualtiero, Camurri, Antonio, and Anguita, Davide

Abstract

Learning to play and perform a music instrument is a complex cognitive task, requiring high conscious control and coordination of an impressive number of cognitive and sensorimotor skills. For professional violinists, there exists a physical connection with the instrument allowing the player to continuously manage the sound through sophisticated bowing techniques and fine hand movements. Hence, it is not surprising that great importance in violin training is given to right hand techniques, responsible for most of the sound produced. In this paper, our aim is to understand which motion features can be used to efficiently and effectively distinguish a professional performance from that of a student without exploiting sound-based features. We collected and made freely available a dataset consisting of motion capture recordings of different violinists with different skills performing different exercises covering different pedagogical and technical aspects. We then engineered peculiar features and trained a data-driven classifier to distinguish among two different levels of violinist experience, namely beginners and experts. In accordance with the hierarchy present in the dataset, we study two different scenarios: extrapolation with respect to different exercises and violinists. Furthermore, we study which features are the most predictive ones of the quality of a violinist to corroborate the significance of the results. The results, both in terms of accuracy and insight on the cognitive problem, support the proposal and support the use of the proposed technique as a support tool for students to monitor and enhance their home study and practice. [ABSTRACT FROM AUTHOR]

Published
2020
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194. Transition-aware human activity recognition using smartphones

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma, Reyes Ortiz, Jorge Luis, Oneto, Luca, Samà Monsonís, Albert, Ghio, Alessandro, Llanas Parra, Xavier, Anguita, Davide, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma, Reyes Ortiz, Jorge Luis, Oneto, Luca, Samà Monsonís, Albert, Ghio, Alessandro, Llanas Parra, Xavier, and Anguita, Davide

Abstract

This work presents the Transition-Aware Human Activity Recognition (TAHAR) system architecture for the recognition of physical activities using smartphones. It targets real-time classification with a collection of inertial sensors while addressing issues regarding the occurrence of transitions between activities and unknown activities to the learning algorithm. We propose two implementations of the architecture which differ in their prediction technique as they deal with transitions either by directly learning them or by considering them as unknown activities. This is accomplished by combining the probabilistic output of consecutive activity predictions of a Support Vector Machine (SVM) with a heuristic filtering approach. The architecture is validated over three case studies that involve data from people performing a broad spectrum of activities (up to 33), while carrying smartphones or wearable sensors. Results show that TAHAR outperforms state-of-the-art baseline works and reveal the main advantages of the architecture., Postprint (author's final draft)

Published
2015

197. Human activity recognition on smartphones for mobile context awareness

Author

Anguita, Davide, Ghio, Alessandro, Oneto, Luca, Llanas Parra, Francesc Xavier, Reyes Ortiz, Jorge Luis, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, and Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement

Subjects
Entorns cognitius, Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC], Human-centered computing, Computació centrada en humans, Cognitive environments, Reconeixement de formes (Informàtica), Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], Telèfons intel·ligents -- Aplicacions
Abstract

Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic. In addition to the clear computational advantages of fixed-point arithmetic, it is easy to show the regularization effect of the number of bits and then the connections with the Statistical Learning Theory. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.

Published
2012

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