Raymundo Forradellas

A reference framework following a proactive approach for Product Lifecycle Management

Product Lifecycle Management (PLM) has been identified as a key concept within manufacturing industries for improving product quality, time-to-market and costs. Previous works on this field are focused on processes, functions and information models, and those aimed at putting more intelligence on products are related to specific parts of the product lifecycle (e.g. supply chain management, shop floor control). Therefore, there is a lack of a holistic approach to PLM, putting more intelligence on products through the complete lifecycle. In this paper, a PLM framework supported by a proactive approach based on intelligent agents is proposed. The developed model aims at being a first step toward a reference framework for PLM, and complements past works on both product information and business process models (BPM), by putting proactivity on products behavior. An example of an instantiation of the reference framework is presented as a case study.

An artificial intelligence planning approach to manufacturing feature recognition

Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.

Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds

BACKGROUND Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. METHODS A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. RESULTS In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC - an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. CONCLUSIONS The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.

Arm Muscular Effort Estimation from Images Using Computer Vision and Machine Learning

A problem of great interest in disciplines like occupational medicine, ergonomics, and sports, is the measurement of biomechanical variables involved in human movement and balance such as internal muscle forces and joint torques. This problem is solved by a two-step process: data capturing using impractical, intrusive and expensive devices that is then used as input in complex models for obtaining the biomechanical variables of interest. In this work we present a first step towards capturing input data through a more automated, non-intrusive and economic process, specifically weight held by an arm subject to isometric contraction as a measure of muscular effort. We do so, by processing RGB images of the arm with computer vision (Local Binary Patterns and Color Histograms) and estimating the effort with machine learning algorithms (SVM and Random Forests). In the best case we obtained an FMeasure \(=70.68\,\%\), an Accuracy \(=71.66\,\%\) and a mean absolute error in the predicted weights of 554.16 grs (over 3 possible levels of effort). Considering the difficulty of the problem, it is enlightening to achieve over random results indicating that, despite the simplicity of the approach, it is possible to extract meaningful information for the predictive task. Moreover, the simplicity of the approach suggests many lines of further improvements: on the image capturing side with other kind of images; on the feature extraction side with more sophisticated algorithms and features; and on the knowledge extraction side with more sophisticated learning algorithms.