This paper introduces a mobile application that aims to improve exercise posture analysis in gym environments using machine learning and computer vision. The solution processes user-uploaded videos to detect posture errors, utilizing Long Short-Term Memory (LSTM) networks and MediaPipe for precise pose estimation. The trained model achieved high accuracy in classifying exercise postures, demonstrating reliable performance across different user scenarios. Traditional posture correction methods, such as personal trainers and wearable devices, often lack accessibility and precision. In contrast, our application offers a scalable, user-friendly tool that delivers actionable feedback, helping users optimize their workouts and reduce injury risks. The study highlights the potential of combining machine learning with mobile technology to enhance exercise safety and performance, setting a foundation for future improvements.

Every year, the increase in human-computer interaction is noticeable. This brings with it the evolution of computer vision to improve this interaction to make it more efficient and effective. This paper presents a CNN-based emotion face recognition model capable to be executed on mobile devices, in real time and with high accuracy. Different models implemented in other research are usually of large sizes, and although they obtained high accuracy, they fail to make predictions in an optimal time, which prevents a fluid interaction with the computer. To improve these, we have implemented a lightweight CNN model trained with the FER2013 dataset to obtain the prediction of seven basic emotions. Experimentation shows that our model achieves an accuracy of 66.52% in validation, can be stored in a 13.23MB file and achieves an average processing time of 14.39ms and 16.06ms, on a tablet and a phone, respectively..

Leishmaniasis is part of a group of diseases called Neglected Tropical Diseases (NTDs) that affects poor and forgotten communities and reports more than 5,000 cases in regions like Brazil, Peru, and Colombia being categorized as endemic in these. In this study, we present a machine-learning model (Random Forest) to predict cases in the future and predict possible outbreaks using meteorological and epidemiological data of the province of la Convencion (Cusco - Peru). Understanding how climate variables affect leishmaniasis outbreaks is an important problem to help people to perform prevention systems. We used several techniques to obtain better metrics and improve our model performance such as synthetic data and hyperparameter optimization. Results showed two important climate factors to analyze and no outbreaks.

Abstract Deep learning models have shown that it is possible to train neural networks to dispense, to a lesser or greater extent, with the need for human intervention for the task of image animation, which helps to reduce not only the production time of these audiovisual pieces, but also presents benefits with respect to the economic investment they require to be made. However, these models suffer from two common problems: the animations they generate are of very low resolution and they require large amounts of training data to generate good results. To deal with these issues, this article introduces the architectural modification of a state‐of‐the‐art image animation model integrated with a video super‐resolution model to make the generated videos more visually pleasing to viewers. Although it is possible to train the animation models with higher resolution images, the time it would take to train them would be much longer, which does not necessarily benefit the quality of the animation, so it is more efficient to complement it with another model focused on improving the animation resolution of the generated video as we demonstrate in our results. We present the design and implementation of a convolutional neural network based on an state‐of‐art model focused on the image animation task, which is trained with a set of facial data from videos extracted from the YouTube platform. To determine which of all the modifications to the selected state‐of‐the‐art model architecture is better, the results are compared with different metrics that evaluate the performance in image animation and video quality enhancement tasks. The results show that modifying the architecture of the model focused on the detection of characteristic points significantly helps to generate more anatomically and visually attractive videos. In addition, perceptual testing with users shows that using a super‐resolution video model as a plugin helps generate more visually appealing videos.

Teaching robot movements has always been considered a complex topic in which there is much interest, since the slightest change in the robot’s programming can generate a high downtime that can last for months. In this work, we carried out a study of human movements to implement a new method of Robot Programming by Demonstration (RPbD) using neural networks. Current methods require specialists with high mathematical and logical knowledge to teach robot movements. Using a famous pose estimation algorithm called OpenPose and a 3D lifting method we obtain the estimated pose of the person arm in a simnlated 3D space. Then, we use various classification tools to translate it to the robot. The results show that it is feasible to make robot programming more accessible using pose estimation.

We introduce the notion of Optimal Patterns (OPs), defined as the best patterns according to a given user preference, and show that OPs encompass many data mining problems. Then, we propose a generic method based on a Dynamic Constraint Satisfaction Problem to mine OPs, and we show that any OP is characterized by a basic constraint and a set of constraints to be dynamically added. Finally, we perform an experimental study comparing our approach vs adhoc methods on several types of OPs.

We introduce skypattern cubes and propose an efficient bottom-up approach to compute them. Our approach relies on derivation rules collecting skypatterns of a parent node from its child nodes without any dominance test. Non-derivable skypatterns are computed on the fly thanks to Dynamic CSP. The bottom-up principle enables to provide a concise representation of the cube based on skypattern equivalence classes without any supplementary effort. Experiments show the effectiveness of our proposal.

Sequential pattern mining under various constraints is a challenging data mining task. The paper provides a generic framework based on constraint programming to discover sequence patterns defined by constraints on local patterns (e.g., Gap, regular expressions) or constraints on patterns involving combination of local patterns such as relevant subgroups and top-k patterns. This framework enables the user to mine in a declarative way both kinds of patterns. The solving step is done by exploiting the machinery of Constraint Programming. For complex patterns involving combination of local patterns, we improve the mining step by using dynamic CSP. Finally, we present two case studies in biomedical information extraction and stylistic analysis in linguistics.

We propose an effective method to compute the sky pattern cubes thanks to a relaxation strategy in the pattern mining process. Our approach is based on the fact that each node of the cube can be approximated by the set of edge-sky patterns (a relaxed form of sky patterns) w.r.t. The whole set of measures M. Then we transform the problem into a skyline cube mining in M dimensions. The set of edge-sky patterns can be efficiently mined by using either a dynamic CSP method or an extended version of a static method based on the theoretical relationships between patterns and condensed representations of sky patterns. Experiments conducted on UCI datasets and on a real-life dataset (Mutagen city) show the relevance and performance of our approach.