RECOGNITION OF HUMAN MOVEMENT PATTERNS

INTRODUCTION - Since a few years ago computer science means an important sup port to biomechanical analysis. Whenever a lot of calculations are to be made, and the value of different parameters like mechanical variables are needed, the advantages of using computers are clear. However a bottleneck is present in data acquisition process for cinematic analysis from video sequence. Traditionally this task is performed like a manual process: user of computer systems must mark, for each frame, some articular points (about 20-21 ) by means of an optical pencil or using a mouse on the computer display once image has been digitalized. This is a routinary task and takes a lot of time. For example, for three seconds of movement, we probably need to process about 150 frames and, for each of them, to perform a digitalizing process. Recently new approaches are used to allow an automatic recognition. These approaches are based on the use of body optical sensors. Recognition process is easier because we only see several single points in the screen (under special environment conditions). However, these approaches are no applicable in real situations (i.e. competitions) which are the most interesting moments for analysis. What we propose here is an attempt to make an automatic system for data acquisition process from video sequences in sport environments and, as a general rule, for the analysis of human movement. We must take into account that the complexity of recognition process is lowered if we are working into a very narrow domain, like cyclic movements with 2D analysis (i.e. path of legs in some kinds of running). Nevertheless, our approach can be transferred to movements with 3D analysis if tridimensional reconstruction from human shapes has been performed. The process that our system will perform on each frame includes: . Digital image pre-processing, Edge sharpening, contrast adjust and filling of areas of interest. . If results are on satisfactoy (e.g. incomplete shapes), an Artificial Neural Network is used in order to predict total pattern, using previous frames or information available in a sportmen customized database containing antrophometric data and cinematic pattern of movement. . Last step is an intelligent matching between articular segments included in database (invariables botn with a 3D analysis and with a 2D analysis under specific features mentioned above) and human shapes obtained from previous steps. RESULTS - First phase relating to image recognition in laboratory conditions (high contrast) has been performed and the difference between manual articular coordenates and automatic acquisition was about zero-two units. CONCLUSION - The system we propose can help to biomechanics to reduce a lot the time destinated to perform data acquisition. Several hours would be changed to some minutes without human assist. REFERENCES R. J. Schalkoff, (1988) Digital lmage Processing and Computer Vision. John Wiley and Sons Inc. L.J. Galbiati, (1988) Machine Vision and Digital lmage Processing Fundamentals. Prentice-Hall International. C.H. Chen, (1993) Handbook of Pattern Recognition and Computer Vision. World Scientific

Customer Orientation in Family Businesses

The purpose of this article is to investigate customer orientation of service employees (COSE) in family businesses. This study elaborates on the perception and importance of COSE in family-owned companies. The paper also proposes new consequences of COSE in the context of family business. The research is based on a qualitative study comprised of 13 interviews conducted on senior managers in family firms. The results are analysed using NVivo 11. This investigation confirms the relevance of the COSE construct in family businesses and the role of family influence over it. New consequences are elicited, including differentiation, customer experience, and customer well-being. The results show that practitioners consider COSE as a key element for success. This study sheds light on how COSE can be applied in a family business in order to enhance customer experience. This study expands on the potential of COSE with the use of family businesses for the first time and introduces new consequences from the original model

Precise OBS location at the sea bottom in active seismic profiles using the air gun shot records

The Norcaribe campaign, in November – December 2013, funded by Spanish Ministry of Innovation and Science (Norcaribe Project CGL2010-17715), was performed on board of the Spanish research vessel “Sarmiento de Gamboa” around the Hispaniola island, also with the participation of the Dominic Republic Navy patrol vessels and several Haiti and Dominic Republic institutions. During the campaign, a 200 km long, wide-amgle refraction seismic profile was carried out crossing the Beata ridge. The air gun signal (5100 ci) was recorded by 15 OBSs deployed along the seismic line in water depths between 2.300 meters and 4.320 meters. To obtain the section records, the OBS position is needed, usually the deployment location is used, but the OBS can drift while is sinking due to the deep oceanic currents. The recovery locations at surface could provide information about the drift, assuming a constant sea current since the deployment to the recovery, but it is imprecise. In this work we show a method to obtain a precise location of the OBS at the sea bottom using a high-resolution bathymetry and the OBS record of the closest air gun shots of the profile. Also, the preliminary results for the Norcaribe campaign (Beata ridge profile) are shown.Peer Reviewe

Solving Large Scale Instances of the Distribution Design Problem Using Data Mining

In this paper we approach the solution of large instances of the distribution design problem. The traditional approaches do not consider that the instance size can significantly reduce the efficiency of the solution process. We propose a new approach that includes compression methods to transform the original instance into a new one using data mining techniques. The goal of the transformation is to condense the operation access pattern of the original instance to reduce the amount of resources needed to solve the original instance, without significantly reducing the quality of its solution. In order to validate the approach, we tested it proposing two instance compression methods on a new model of the replicated version of the distribution design problem that incorporates generalized database objects. The experimental results show that our approach permits to reduce the computational resources needed for solving large instances by at least 65%, without significantly reducing the quality of its solution. Given the encouraging results, at the moment we are working on the design and implementation of efficient instance compression methods using other data mining techniques

Wearable Postural Control System for Low Back Pain Therapy

© 2021 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/10.1109/TIM.2021.3057935[Abstract]: Treatment of low back pain usually includes exercise, analgesics, prostheses, and, in severe cases, surgery. Early treatments based on postural control are essential to prevent low back pain and mitigate permanent damage. We present a wearable device, with an estimated cost below U.S. $100, which uses inertial units with triaxial accelerometers, gyroscopes, and magnetometers to measure the orientation of three sections of the spine. The device integrates the absolute and relative orientation from the sensors to estimate the posture of the back in real-time and uses a fuzzy system to control a vibration unit that indicates the user when to correct the posture of the back. We validated the device in controlled conditions, obtaining an rms deviation ≤1.24° and conducted a preliminary clinical pilot study with patients afflicted by lumbar hyperlordosis or lumbar hypolordosis. We observed an improved postural control and a reduction of low back pain in all cases. These results show a promising potential of the device to reduce pain, improve postural therapies, and raise postural awareness in patients affected with low back pain

Tendencias, factores desencadenantes y tipo de plagio en las tareas académicas: un caso de estudio en la universidad española

The aim of the paper is to deepen the knowledge of plagiarism among college students in a Spanish University and to propose measures leading to its reduction. An analytical framework was developed to compare students’ perception of the plagiarism that they perform with data provided by anti-plagiarism software, which provided objective information about student’s real plagiarism behaviour. The data´s comparison revealed a general lack of knowledge about plagiarism; 37 % of the students declared ignorance of what it is. They were able to recognise cheating activities as plagiarism only when severe. Thus, cheating attitudes were perceived as morally acceptable in the most common plagiarism situations such as the reproduction of works from the internet. In policy terms, the implementation of training measures is advisable to provide students with conceptual tools to help them avoid and reject plagiarism. Improving compliance and the articulation of gradual and adapted punishments to the cheaters will be relevant to reinforce the educational system and reduce dishonest attitudesEl objetivo de este trabajo es profundizar en el conocimiento sobre plagio entre las actividades académicas de estudiantes de una universidad española y proponer medidas encaminadas a su reducción. Para ello, hemos desarrollado un marco analítico que compara la percepción de los estudiantes sobre el plagio que realizan, con datos objetivos ofrecidos por un software antiplagio sobre el comportamiento plagiador de ese mismo grupo de estudiantes. La comparación reveló una falta generalizada de conocimiento sobre qué constituye plagio. En ese sentido, el 37% de los estudiantes consultados declararon desconocer que actividades son consideradas como plagio. Así, algunas de las actividades de plagio más comunes, como la reproducción de obras de Internet, fueron percibidas como moralmente aceptables entre los estudiantes. Para minimizar el plagio, recomendamos la implementación de medidas formativas entre los estudiantes que les proporcionen herramientas conceptuales para evitar y rechazar el plagio. Mejorar el cumplimiento entre los estudiantes y la articulación de castigos graduales y adaptados a los diferentes tipos de plagiadores, también será relevante para reforzar el sistema educativo y reducir las actitudes deshonesta

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research