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Asma Bensalah, Alicia Fornes, Cristina Carmona_Duarte and Josep Llados. 2022. Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis. Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022.336–348. (LNCS.)
Abstract: Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.
Keywords: Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk
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Alicia Fornes and 11 others. 2022. The RPM3D Project: 3D Kinematics for Remote Patient Monitoring. Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022.217–226. (LNCS.)
Abstract: This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute (https://www.guttmann.com/en/) (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases.
Keywords: Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics
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Partha Pratim Roy, Josep Llados and Umapada Pal. 2007. Text/Graphics Separation in Color Maps. International Conference on Computing: Theory and Applications.545–551.
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Oriol Ramos Terrades, Salvatore Tabbone and Ernest Valveny. 2007. Optimal Linear Combination for Two-class Classifiers. Proceedings of the International Conference on Advances in Pattern Recognition.
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Alicia Fornes and Josep Llados. 2010. A Symbol-dependent Writer Identifcation Approach in Old Handwritten Music Scores. 12th International Conference on Frontiers in Handwriting Recognition.634–639.
Abstract: Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we introduce a symbol-dependent approach for identifying the writer of old music scores, which is based on two symbol recognition methods. The main idea is to use the Blurred Shape Model descriptor and a DTW-based method for detecting, recognizing and describing the music clefs and notes. The proposed approach has been evaluated in a database of old music scores, achieving very high writer identification rates.
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Hana Jarraya, Oriol Ramos Terrades and Josep Llados. 2017. Learning structural loss parameters on graph embedding applied on symbolic graphs. 12th IAPR International Workshop on Graphics Recognition.
Abstract: We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
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J. Chazalon and 9 others. 2017. SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode. 1st International Workshop on Open Services and Tools for Document Analysis.
Abstract: As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of
understanding, usage and improvement.
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Lluis Gomez, Marçal Rusiñol and Dimosthenis Karatzas. 2017. LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting. 14th International Conference on Document Analysis and Recognition.
Abstract: n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset.
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E. Royer, J. Chazalon, Marçal Rusiñol and F. Bouchara. 2017. Benchmarking Keypoint Filtering Approaches for Document Image Matching. 14th International Conference on Document Analysis and Recognition.
Abstract: Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy.
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David Aldavert, Marçal Rusiñol and Ricardo Toledo. 2017. Automatic Static/Variable Content Separation in Administrative Document Images. 14th International Conference on Document Analysis and Recognition.
Abstract: In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
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