Prof. Xavier Parra-Llanas
Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain
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Academic Career Length
28Years (1999-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2016 in Neurocomputing
Journal Article
JRJorge-L. Reyes-Ortiz
Jorge-L. Reyes-OrtizLOLuca Oneto
Luca OnetoASAlbert Samà
Albert Samà
Xavier Parra-LlanasDADavide Anguita
Davide AnguitaThis 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
1 January 2012
Book Chapter
DADavide Anguita
Davide AnguitaAGAlessandro Ghio
Alessandro Ghio
LOLuca Oneto
Luca OnetoXavier Parra-LlanasJRJorge L. Reyes-Ortiz
Jorge L. Reyes-Ortiz
1 September 2003 in Neurocomputing
Journal Article
Cecilio AnguloXavier Parra-LlanasAMAndreu Català Mallofré
Andreu Català Mallofré1 January 2014
Book Chapter
JRJorge-Luis Reyes-Ortiz
Jorge-Luis Reyes-Ortiz
LOLuca Oneto
Luca OnetoAGAlessandro Ghio
Alessandro Ghio
ASAlbert Samà
Albert SamàDADavide Anguita
Davide AnguitaXavier Parra-Llanas1 August 2016 in Computer Vision and Image Understanding
Journal Article
We propose robust multi-dimensional motion features for human activity recognition from first-person videos. The proposed features encode information about motion magnitude, direction and variation, and combine them with virtual inertial data generated from the video itself. The use of grid flow representation, per-frame normalization and temporal feature accumulation enhances the robustness of our new representation. Results on multiple datasets demonstrate that the proposed feature representation outperforms existing motion features, and importantly it does so independently of the classifier. Moreover, the proposed multi-dimensional motion features are general enough to make them suitable for vision tasks beyond those related to wearable cameras. (C) 2015 The Authors. Published by Elsevier Inc.Peer ReviewedPostprint (published version
1 January 2013
Book Chapter
DADavide Anguita
Davide AnguitaAGAlessandro Ghio
Alessandro Ghio
LOLuca Oneto
Luca OnetoXavier Parra-LlanasJRJorge Luis Reyes-Ortiz
Jorge Luis Reyes-Ortiz
About Me
Xavier Parra received his B.S. and M.S. degrees in Computer Science from the Universitat Politècnica de Catalunya (UPC), Barcelona, in 1992, with a specialization in system engineering and industrial automation. He joined the UPC Department of Automatic Control in 1994 as Assistant Professor and completed his PhD in Computer Engineering in 2001, focusing on artificial neural networks and fault-tolerant learning algorithms. Since 2002, he has been Associate Professor with the Department of Automatic Control at UPC. He currently develops his research within the Community-Driven Techonology Research Group (TOC) as a senior member of the Technical Research Centre for Dependency Care and Autonomous Living (CETpD) in Vilanova i la Geltrú. He is also a member of the AgrotechUPC research centre. With more than 25 years of experience in the field, Prof. Parra's research interests include human activity recognition, assistive technologies, and wearable intelligent sensors. He has participated in numerous research projects related to robotics and control systems engineering and has authored many highly cited papers in these domains.