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In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network's classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study. ; This work was supported by the Spanish Government PID2019-104818RB-I00 grant, supported with Feder funds.

In this paper, we present a novel deep learning-based architecture, which is under the scope of expert and intelligent systems, to perform accurate real-time tridimensional hand pose estimation using a single RGB frame as an input, so there is no need to use multiple cameras or points of view, or RGB-D devices. The proposed pipeline is composed of two convolutional neural network architectures. The first one is in charge of detecting the hand in the image. The second one is able to accurately infer the tridimensional position of the joints retrieving, thus, the full hand pose. To do this, we captured our own large-scale dataset composed of images of hands and the corresponding 3D joints annotations. The proposal achieved a 3D hand pose mean error of below 5 mm on both the proposed dataset and Stereo Hand Pose Tracking Benchmark, which is a public dataset. Our method also outperforms the state-of-the-art methods. We also demonstrate in this paper the application of the proposal to perform a robotic hand teleoperation with high success. ; This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243

In recent years, we have seen a large growth in the number of applications which use deep learning-based object detectors. Autonomous driving assistance systems (ADAS) are one of the areas where they have the most impact. This work presents a novel study evaluating a state-of-the-art technique for urban object detection and localization. In particular, we investigated the performance of the Faster R-CNN method to detect and localize urban objects in a variety of outdoor urban videos involving pedestrians, cars, bicycles and other objects moving in the scene (urban driving). We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted on a vehicle. Furthermore, some of the data is weakly annotated so it can be used for testing weakly supervised learning techniques. There already exist urban object datasets, but none of them include all the essential urban objects. We carried out extensive experiments demonstrating the effectiveness of the baseline approach. Additionally, we propose an R-CNN plus tracking technique to accelerate the process of real-time urban object detection. ; This work has been partially funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by the University of Alicante project GRE16-19.

Accurate visual hand pose estimation at joint level has several applications for human-robot interaction, natural user interfaces and virtual/augmented reality applications. However, it is still an open problem being addressed by the computer vision community. Recent novel deep learning techniques may help circumvent the limitations of standard approaches. However, they require large amounts of accurate annotated data. Hand pose datasets that have been released so far present issues such as limited number of samples, inaccurate data or high-level annotations. Moreover, most of them are focused on depth-based approaches, providing only depth information (missing RGB data). In this work, we present a novel multiview hand pose dataset in which we provide hand color images and different kind of annotations for each sample, i.e. the bounding box and the 2D and 3D location on the joints in the hand. Furthermore, we introduce a simple yet accurate deep learning architecture for real-time robust 2D hand pose estimation. Then, we conduct experiments that show how the use of the proposed dataset in the training stage produces accurate results for 2D hand pose estimation using a single color camera. ; This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by the Generalitat Valenciana project GV/2018/22 and by a Spanish grant for PhD studies ACIF/2017/243.

Pedestrian movement direction recognition is an important factor in autonomous driver assistance and security surveillance systems. Pedestrians are the most crucial and fragile moving objects in streets, roads, and events, where thousands of people may gather on a regular basis. People flow analysis on zebra crossings and in shopping centers or events such as demonstrations are a key element to improve safety and to enable autonomous cars to drive in real life environments. This paper focuses on deep learning techniques such as convolutional neural networks (CNN) to achieve a reliable detection of pedestrians moving in a particular direction. We propose a CNN-based technique that leverages current pedestrian detection techniques (histograms of oriented gradients-linSVM) to generate a sum of subtracted frames (flow estimation around the detected pedestrian), which are used as an input for the proposed modified versions of various state-of-the-art CNN networks, such as AlexNet, GoogleNet, and ResNet. Moreover, we have also created a new data set for this purpose, and analyzed the importance of training in a known data set for the neural networks to achieve reliable results. ; This work was supported by the Feder funds, Spanish Government through the COMBAHO Project, under Grant TIN2016-76515-R, and in part by the University of Alicante Project under Grant GRE16-19.

Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures. We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. The proposed approach obtained a 99.90% training accuracy and 99.75% validation accuracy. We also evaluated the proposed system on a test set (new subjects) obtaining an accuracy of 77.85%. In addition, we showed the test prediction results for each gesture separately and analyzed which gestures for the Myo armband with our suggested network can be difficult to distinguish accurately. Moreover, we studied for first time the gated recurrent unit network capability in gesture recognition approaches. Finally, we integrated our method in a system that is able to classify live hand gestures. ; This work was supported by the Spanish Government TIN2016-76515R grant, supported with Feder funds. It has also been funded by the University of Alicante project GRE16-19, by the Valencian Government project GV/2018/022, and by a Spanish grant for PhD studies ACIF/2017/243.

The use of 3D data in mobile robotics applications provides valuable information about the robot's environment but usually the huge amount of 3D information is unmanageable by the robot storage and computing capabilities. A data compression is necessary to store and manage this information but preserving as much information as possible. In this paper, we propose a 3D lossy compression system based on plane extraction which represent the points of each scene plane as a Delaunay triangulation and a set of points/area information. The compression system can be customized to achieve different data compression or accuracy ratios. It also supports a color segmentation stage to preserve original scene color information and provides a realistic scene reconstruction. The design of the method provides a fast scene reconstruction useful for further visualization or processing tasks. ; This work has been supported by the Spanish Government DPI2013-40534-R grant.

In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work. ; This work was supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. It was funded by the University of Alicante project GRE16-19, and by the Valencian Government project GV/2018/022. Edmanuel Cruz is funded by a Panamanian grant for PhD studies IFARHU & SENACYT 270-2016-207. This work was also supported by a Spanish grant for PhD studies ACIF/2017/243. Thanks also to NVIDIA for the generous donation of a Titan Xp and a Quadro P6000.

Custom shoes manufacturing is one of the major challenges facing the footwear industry today. A shoe for everyone: it is a change in the production model in which each individual's foot is the main focus, replacing traditional size systems based on population means. This paradigm shift represents a major effort for the industry, for which the design and not production becomes the main bottleneck. It is therefore necessary to accelerate the design process by improving the accuracy of current methods. The starting point for making a shoe that fits the client's foot anatomy is scanning the surface of the foot. Automated foot model reconstruction is accomplished through the use of the self-organising growing neural gas (GNG) network, which is able to topographically map the low dimension of the network to the high dimension of the manifold of the scanner acquisitions without requiring a priori knowledge of the structure of the input space. The GNG obtains a surface representation adapted to the topology of the foot, is accurate, tolerant to noise, and eliminates outliers. It also improves the reconstruction in "dark" areas where the scanner does not obtain information: the heel and toe areas. The method reconstructs the foot surface 4 times more accurately than other well-known methods. The method is generic and easily extensible to other industrial objects that need to be digitized and reconstructed with accuracy and efficiency requirements. ; This work was partially funded by the Spanish Government DPI2013-40534-R grant, supported with Feder funds, NILS Mobility Project 012-ABEL-CM-2014A, and Fundación Séneca 18946/JLI/13.

As estimated by the World Health Organization, there are millions of people who lives with some form of vision impairment. As a consequence, some of them present mobility problems in outdoor environments. With the aim of helping them, we propose in this work a system which is capable of delivering the position of potential obstacles in outdoor scenarios. Our approach is based on non-intrusive wearable devices and focuses also on being low-cost. First, a depth map of the scene is estimated from a color image, which provides 3D information of the environment. Then, an urban object detector is in charge of detecting the semantics of the objects in the scene. Finally, the three-dimensional and semantic data is summarized in a simpler representation of the potential obstacles the users have in front of them. This information is transmitted to the user through spoken or haptic feedback. Our system is able to run at about 3.8 fps and achieved a 87.99% mean accuracy in obstacle presence detection. Finally, we deployed our system in a pilot test which involved an actual person with vision impairment, who validated the effectiveness of our proposal for improving its navigation capabilities in outdoors. ; This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds, the University of Alicante project GRE16-19, and by the Valencian Government project GV/2018/022. Edmanuel Cruz is funded by a Panamenian grant for PhD studies IFARHU & SENACYT 270-2016-207. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243. Thanks also to Nvidia for the generous donation of a Titan Xp and a Quadro P6000.

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of ~ 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability. ; This work has been funded by the Spanish Government with Feder funds (TIN2016-76515-R and DPI2015-68087-R), by two grants for PhD studies (FPU15/04516 and BES-2016-07829), by regional projects (GV/2018/022 and GRE16-19) and by the European Commission (COMMANDIA SOE2/P1/F0638), action supported by Interreg-V Sudoe.

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of approximately 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability. ; This work has been funded by the Spanish Government with Feder funds (TIN2016-76515-R and DPI2015-68087-R), by two grants for PhD studies (FPU15/04516 and BES-2016-07829), by regional projects (GV/2018/022 and GRE16-19) and by the European Commission (COMMANDIA SOE2/P1/F0638), action supported by Interreg-V Sudoe.

The use of 3D data in mobile robotics applications provides valuable information about the robot's environment. However usually the huge amount of 3D information is difficult to manage due to the fact that the robot storage system and computing capabilities are insufficient. Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible. A few methods have been proposed to compress 3D information. Nevertheless, there does not exist a consistent public benchmark for comparing the results (compression level, distance reconstructed error, etc.) obtained with different methods. In this paper, we propose a dataset composed of a set of 3D point clouds with different structure and texture variability to evaluate the results obtained from 3D data compression methods. We also provide useful tools for comparing compression methods, using as a baseline the results obtained by existing relevant compression methods. ; This work was partially supported by grant DPI2013-40534-R of the Ministerio of Economia y Competitividad of the Spanish Government, supported with Feder funds, and Valencian's Government project GV/2014/097.

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid. ; This work was partially funded by the Spanish Government DPI2013-40534-R grant and Valencian Government GV/2013/005 grant.

The first hybrid CPU-GPU based method for estimating a point light source position in a scene recorded by an RGB-D camera is presented. The image and depth information from the Kinect is enough to estimate a light position in a scene, which allows for the rendering of synthetic objects into a scene that appears realistic enough for augmented reality purposes. This method does not require a light probe or other physical device. To make this method suitable for augmented reality, we developed a hybrid implementation that performs light estimation in under 1second. This is sufficient for most augmented reality scenarios because both the position of the light source and the position of the Kinect are typically fixed. The method is able to estimate the angle of the light source with an average error of 20°. By rendering synthetic objects into the recorded scene, we illustrate that this accuracy is good enough for the rendered objects to look realistic. ; This work is partially supported by the Fish4Knowledge project, which is funded by the European Union 7th Framework Programme [FP7/2007-2013], by the HiPEAC Network of Excellence, by the Valencian Government grant BEFPI/2012/056 and by EPSRC (EP/P504902/1, EP/H012338/1).