Train Station Pedestrian Monitoring Pilot Study Using an Artificial Intelligence Approach.

Pedestrian monitoring in crowded areas like train stations has an important impact in the overall operation and management of those public spaces. An organized distribution of the different elements located inside a station will contribute not only to the safety of all passengers but will also allow for a more efficient process of the regular activities including entering/leaving the station, boarding/alighting from trains, and waiting. This improved distribution only comes by obtaining sufficiently accurate information on passengers' positions, and their derivatives like speeds, densities, traffic flow. The work described here addresses this need by using an artificial intelligence approach based on computational vision and convolutional neural networks. From the available videos taken regularly at subways stations, two methods are tested. One is based on tracking each person's bounding box from which filtered 3D kinematics are derived, including position, velocity and density. Another infers the pose and activity that a person has by analyzing its main body key points. Measurements of these quantities would enable a sensible and efficient design of inner spaces in places like railway and subway stations.

Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic.

Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand and control the nuclear fusion process. A very interesting diagnostic called Thomson scattering (TS) is performed in the Spanish fusion device TJ-II. This diagnostic takes images to measure the temperature and density profiles of the plasma, which is heated to very high temperatures to produce fusion plasma. Each image captures spectra of laser light scattered by the plasma under different conditions. Unfortunately, some images are corrupted by noise called stray light that affects the measurement of the profiles. In this work, we propose the use of deep learning models to reduce the stray light that appears in the diagnostic. The proposed approach utilizes a Pix2Pix neural network, which is an image-to-image translation based on a generative adversarial network (GAN). This network learns to translateimages affected by stray light to images without stray light. This allows for the effective removal of the noise that affects the measurements of the TS diagnostic, avoiding the need for manual image processing adjustments. The proposed method shows a better performance, reducing the noise up to 98% inimages, which surpassesprevious works that obtained 85% for the validation dataset.

BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN.

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.

Development and Control of a Real Spherical Robot.

This paper presents the design and implementation of a spherical robot with an internal mechanism based on a pendulum. The design is based on significant improvements made, including an electronics upgrade, to a previous robot prototype developed in our laboratory. Such modifications do not significantly impact its corresponding simulation model previously developed in CoppeliaSim, so it can be used with minor modifications. The robot is incorporated into a real test platform designed and built for this purpose. As part of the incorporation of the robot into the platform, software codes are made to detect its position and orientation, using the system SwisTrack, to control its position and speed. This implementation allows successful testing of control algorithms previously developed by the authors for other robots such as Villela, the Integral Proportional Controller, and Reinforcement Learning.

Biomarkers for dementia in Latin American countries: Gaps and opportunities.

Limited knowledge on dementia biomarkers in Latin American and Caribbean (LAC) countries remains a serious barrier. Here, we reported a survey to explore the ongoing work, needs, interests, potential barriers, and opportunities for future studies related to biomarkers. The results show that neuroimaging is the most used biomarker (73%), followed by genetic studies (40%), peripheral fluids biomarkers (31%), and cerebrospinal fluid biomarkers (29%). Regarding barriers in LAC, lack of funding appears to undermine the implementation of biomarkers in clinical or research settings, followed by insufficient infrastructure and training. The survey revealed that despite the above barriers, the region holds a great potential to advance dementia biomarkers research. Considering the unique contributions that LAC could make to this growing field, we highlight the urgent need to expand biomarker research. These insights allowed us to propose an action plan that addresses the recommendations for a biomarker framework recently proposed by regional experts.

Human Activity Recognition by Sequences of Skeleton Features.

In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person's body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people's activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.

Detection of Human Gait Phases Using Textile Pressure Sensors: A Low Cost and Pervasive Approach.

Human gait analysis is a standard method used for detecting and diagnosing diseases associated with gait disorders. Wearable technologies, due to their low costs and high portability, are increasingly being used in gait and other medical analyses. This paper evaluates the use of low-cost homemade textile pressure sensors to recognize gait phases. Ten sensors were integrated into stretch pants, achieving an inexpensive and pervasive solution. Nevertheless, such a simple fabrication process leads to significant sensitivity variability among sensors, hindering their adoption in precision-demanding medical applications. To tackle this issue, we evaluated the textile sensors for the classification of gait phases over three machine learning algorithms for time-series signals, namely, random forest (RF), time series forest (TSF), and multi-representation sequence learner (Mr-SEQL). Training and testing signals were generated from participants wearing the sensing pants in a test run under laboratory conditions and from an inertial sensor attached to the same pants for comparison purposes. Moreover, a new annotation method to facilitate the creation of such datasets using an ordinary webcam and a pose detection model is presented, which uses predefined rules for label generation. The results show that textile sensors successfully detect the gait phases with an average precision of 91.2% and 90.5% for RF and TSF, respectively, only 0.8% and 2.3% lower than the same values obtained from the IMU. This situation changes for Mr-SEQL, which achieved a precision of 79% for the textile sensors and 36.8% for the IMU. The overall results show the feasibility of using textile pressure sensors for human gait recognition.

Estimation of Soluble Solids for Stone Fruit Varieties Based on Near-Infrared Spectra Using Machine Learning Techniques.

The quality control for fruit maturity inspection is a key issue in fruit packaging and international trade. The quantification of Soluble Solids (SS) in fruits gives a good approximation of the total sugar concentration at the ripe stage, and on the other hand, SS alone or in combination with acidity is highly related to the acceptability of the fruit by consumers. The non-destructive analysis based on Visible (VIS) and Near-Infrared (NIR) spectroscopy has become a popular technique for the assessment of fruit quality. To improve the accuracy of fruit maturity inspection, VIS−NIR spectra models based on machine learning techniques are proposed for the non-destructive evaluation of soluble solids in considering a range of variations associated with varieties of stones fruit species (peach, nectarine, and plum). In this work, we propose a novel approach based on a Convolutional Neural Network (CNN) for the classification of the fruits into species and then a Feedforward Neural Network (FNN) to extract the information of VIS−NIR spectra to estimate the SS content of the fruit associated to several varieties. A classification accuracy of 98.9% was obtained for the CNN classification model and a correlation coefficient of Rc>0.7109 for the SS estimation of the FNN models was obtained. The results reported show the potential of this method for a fast and on-line classification of fruits and estimation of SS concentration.

Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator.

This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind of experience. The model was tested under several scenarios and control goals (i.e., position control, path-following and formation control) with control strategies such as reinforcement learning, and Villela and IPC algorithms. The results of these approaches were compared using performance indexes to analyze the performance of the model under different scenarios. The model and examples with different control scenarios are available online.

Dementia in Latin America: Paving the way toward a regional action plan.

Across Latin American and Caribbean countries (LACs), the fight against dementia faces pressing challenges, such as heterogeneity, diversity, political instability, and socioeconomic disparities. These can be addressed more effectively in a collaborative setting that fosters open exchange of knowledge. In this work, the Latin American and Caribbean Consortium on Dementia (LAC-CD) proposes an agenda for integration to deliver a Knowledge to Action Framework (KtAF). First, we summarize evidence-based strategies (epidemiology, genetics, biomarkers, clinical trials, nonpharmacological interventions, networking, and translational research) and align them to current global strategies to translate regional knowledge into transformative actions. Then we characterize key sources of complexity (genetic isolates, admixture in populations, environmental factors, and barriers to effective interventions), map them to the above challenges, and provide the basic mosaics of knowledge toward a KtAF. Finally, we describe strategies supporting the knowledge creation stage that underpins the translational impact of KtAF.

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots.

This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data.

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems.

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.

Cochlear dysfunction as an early biomarker of cognitive decline in normal hearing and mild hearing loss.

INTRODUCTION: Age-related hearing loss is an important risk factor for cognitive decline. However, audiogram thresholds are not good estimators of dementia risk in subjects with normal hearing or mild hearing loss. Here we propose to use distortion product otoacoustic emissions (DPOAEs) as an objective and sensitive tool to estimate the risk of cognitive decline in older adults with normal hearing or mild hearing loss. METHODS: We assessed neuropsychological, brain magnetic resonance imaging, and auditory analyses on 94 subjects > 64 years of age. RESULTS: We found that cochlear dysfunction, measured by DPOAEs-and not by conventional audiometry-was associated with Clinical Dementia Rating Sum of Boxes (CDR-SoB) classification and brain atrophy in the group with mild hearing loss (25 to 40 dB) and normal hearing (<25 dB). DISCUSSION: Our findings suggest that DPOAEs may be a non-invasive tool for detecting neurodegeneration and cognitive decline in the older adults, potentially allowing for early intervention.

A mobile robots experimental environment with event-based wireless communication.

An experimental platform to communicate between a set of mobile robots through a wireless network has been developed. The mobile robots get their position through a camera which performs as sensor. The video images are processed in a PC and a Waspmote card sends the corresponding position to each robot using the ZigBee standard. A distributed control algorithm based on event-triggered communications has been designed and implemented to bring the robots into the desired formation. Each robot communicates to its neighbors only at event times. Furthermore, a simulation tool has been developed to design and perform experiments with the system. An example of usage is presented.

The limitations and challenges in the assessment of executive dysfunction associated with real-world functioning: The opportunity of serious games.

Nowadays, there is a broad range of methods for detecting and evaluating executive dysfunction ranging from clinical interview to neuropsychological evaluation. Nevertheless, a critical issue of these assessments is the lack of correspondence of the neuropsychological test's results with real-world functioning. This paper proposes serious games as a new framework to improve the neuropsychological assessment of real-world functioning. We briefly discuss the contribution and limitations of current methods of evaluation of executive dysfunction (paper-and-pencil tests, naturalistic observation methods, and Information and Communications Technologies) to inform on daily life functioning. Then, we analyze what are the limitations of these methods to predict real-world performance: (1) A lack of appropriate instruments to investigate the complexity of real-world functioning, (2) the vast majority of neuropsychological tests assess well-structured tasks, and (3) measurement of behaviors are based on simplistic data collection and statistical analysis. This work shows how serious games offer an opportunity to develop more efficient tools to detect executive dysfunction in everyday life contexts. Serious games provide meaningful narrative stories and virtual or real environments that immerse the user in natural and social environments with social interactions. In those highly interactive game environments, the player needs to adapt his/her behavioral performance to novel and ill-structured tasks which are suited for collecting user interaction evidence. Serious games offer a novel opportunity to develop better tools to improve diagnosis of the executive dysfunction in everyday life contexts. However, more research is still needed to implement serious games in everyday clinical practice.

Deep learning exoplanets detection by combining real and synthetic data.

Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope light curves. The approach is based on related work from the literature and enhanced to validation with real light curves. A CNN classification model is trained from a mixture of real and synthetic data. The model is then validated only with unknown real data. The best ratio of synthetic data is determined by the performance of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

Multi-feature computational framework for combined signatures of dementia in underrepresented settings.

Objective.The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings.Approach. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat).Main results. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens).Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data.Significance. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.

New Frontiers in the Prevention, Diagnosis, and Treatment of Alzheimer's Disease.

One of the major puzzles in medical research and public health systems worldwide is Alzheimer's disease (AD), reaching nowadays a prevalence near 50 million people. This is a multifactorial brain disorder characterized by progressive cognitive impairment, apathy, and mood and neuropsychiatric disorders. The main risk of AD is aging; a normal biological process associated with a continuum dynamic involving a gradual loss of people's physical capacities, but with a sound experienced view of life. Studies suggest that AD is a break from normal aging with changes in the powerful functional capacities of neurons as well as in the mechanisms of neuronal protection. In this context, an important path has been opened toward AD prevention considering that there are elements of nutrition, daily exercise, avoidance of toxic substances and drugs, an active social life, meditation, and control of stress, to achieve healthy aging. Here, we analyze the involvement of such factors and how to control environmental risk factors for a better quality of life. Prevention as well as innovative screening programs for early detection of the disease using reliable biomarkers are becoming critical to control the disease. In addition, the failure of traditional pharmacological treatments and search for new drugs has stimulated the emergence of nutraceutical compounds in the context of a "multitarget" therapy, as well as mindfulness approaches shown to be effective in the aging, and applied to the control of AD. An integrated approach involving all these preventive factors combined with novel pharmacological approaches should pave the way for the future control of the disease.

Interventional Study to Evaluate the Clinical Effects and Safety of the Nutraceutical Compound BrainUp-10® in a Cohort of Patients with Alzheimer's Disease: A Multicenter, Randomized, Double-Blind, and Placebo-Controlled Trial.

BACKGROUND: Clinically-evaluated nutraceuticals are candidates for Alzheimer's disease (AD) prevention and treatment. Phase I studies showed biological safety of the nutraceutical BrainUp-10®, while a pilot trial demonstrated efficacy for treatment. Cell studies demonstrated neuroprotection. BrainUp-10® blocks tau self-assembly. Apathy is the most common of behavioral alterations. OBJECTIVE: The aim was to explore efficacy of BrainUp-10® in mitigating cognitive and behavioral symptoms and in providing life quality, in a cohort of Chilean patients with mild to moderate AD. METHODS: The was a multicenter, randomized, double blind, placebo-controlled phase II clinical study in mild to moderate AD patients treated with BrainUp-10® daily, while controls received a placebo. Primary endpoint was Apathy (AES scale), while secondary endpoints included Mini-Mental State Examination (MMSE), Trail Making Test (TMT A and TMT B), and Neuropsychiatry Index (NPI). AD blood biomarkers were analyzed. Laboratory tests were applied to all subjects. RESULTS: 82 patients were enrolled. The MMSE score improved significantly at week 24 compared to baseline with tendency to increase, which met the pre-defined superiority criteria. NPI scores improved, the same for caregiver distress at 12th week (p = 0.0557), and the alimentary response (p = 0.0333). Apathy tests showed a statistically significant decrease in group treated with BrainUp-10®, with p = 0.0321 at week 4 and p = 0.0480 at week 12 treatment. A marked decrease in homocysteine was shown with BrainUp-10® (p = 0.0222). CONCLUSION: Data show that BrainUp-10® produces a statistically significant improvement in apathy, ameliorating neuropsychiatric distress of patients. There were no compound-related adverse events. BrainUp-10® technology may enable patients to receive the benefits for their cognitive and behavioral problems.