Learning on real robots from experience and simple user feedback

In this article we describe a novel algorithm that allows fast and continuous learning on a physical robot working in a real environment. The learning process is never stopped and new knowledge gained from robot-environment interactions can be incorporated into the controller at any time. Our algorithm lets a human observer control the reward given to the robot, hence avoiding the burden of defining a reward function. Despite the highly-non-deterministic reinforcement, through the experimental results described in this paper, we will see how the learning processes are never stopped and are able to achieve fast robot adaptation to the diversity of different situations the robot encounters while it is moving in several environments.This work was supported by the research grant TIN2009-07737 of the Spanish Ministerio de Economía y Competitividad, and María Barbeito program of the Xunta de Galicia

Walking Recognition in Mobile Devices

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposalsThis research has received financial support from AEI/FEDER (European Union) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, reference competitive group ED431C2018/29, and grant ED431F2018/02), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Educación, Cultura y Deporte of Spain in the FPU 2017 program (FPU17/04154), and the Ministerio de Economía, Industria y Competitividad in the Industrial PhD 2014 program (DI-14-06920)S

Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposalThis work was supported by the research projects TIN2009-07737, INCITE08PXIB262202PR, and TIN2012-32262, the grant BES-2010-040813 FPI-MICINN, and by the grant “Consolidation of Competitive Research Groups, Xunta de Galicia ref. 2010/6”S

Episodix: a serious game to detect cognitive impairment in senior adults. A psychometric study

Introduction Assessment of episodic memory is traditionally used to evaluate potential cognitive impairments in senior adults. The present article discusses the capabilities of Episodix, a game to assess the aforementioned cognitive area, as a valid tool to discriminate among mild cognitive impairment (MCI), Alzheimer’s disease (AD) and healthy individuals (HC); that is, it studies the game’s psychometric validity study to assess cognitive impairment. Materials and Methods After a preliminary study, a new pilot study, statistically significant for the Galician population, was carried out from a cross-sectional sample of senior adults as target users. A total of 64 individuals (28 HC, 16 MCI, 20 AD) completed the experiment from an initial sample of 74. Participants were administered a collection of classical pen-and-paper tests and interacted with the games developed. A total of six machine learning classification techniques were applied and four relevant performance metrics were computed to assess the classification power of the tool according to participants’ cognitive status. Results According to the classification performance metrics computed, the best classification result is obtained using the Extra Trees Classifier (F1 = 0.97 and Cohen’s kappa coefficient = 0.97). Precision and recall values are also high, above 0.9 for all cognitive groups. Moreover, according to the standard interpretation of Cohen’s kappa index, classification is almost perfect (i.e., 0.81–1.00) for the complete dataset for all algorithms. Limitations Weaknesses (e.g., accessibility, sample size or speed of stimuli) detected during the preliminary study were addressed and solved. Nevertheless, additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. Conclusion Promising results obtained about psychometric validity of Episodix, represent a relevant step ahead towards the introduction of serious games and machine learning in regular clinical practice for detecting MCI or AD. However, more research is needed to explore the introduction of item response theory in this game and to obtain the required normative data for clinical validityThis research was supported by the University of VigoS

A methodology to measure sight-hidden dips'parameters.

Highway design standards specify several requirements on available sight distance. Usually, compliance with these standards is ensured during the design phase of a new road. This is made through geometric calculations that take into account the terrain and the road. In this paper, a procedure for measuring distances in an existing road from georeferenced photographs is proposed. In addition, an estimation of the error committed when using this procedure is made. Distances measured using this procedure arecompared with the ones measured using a Global Navigation Satellite System (GNSS). Distances error is within the error estimation and is low enough for using it in traffic safety studies. In addition, the procedure is applied to measure several parameters of a sight-hidden dip. This procedure does not need a terrain model to measure these parameters. This is an advantage compared with other existing procedures for estimating the parameters of sight-hidden dips

Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults

Introduction Assessment of episodic memory has been traditionally used to evaluate potential cognitive impairments in senior adults. Typically, episodic memory evaluation is based on personal interviews and pen-and-paper tests. This article presents the design, development and a preliminary validation of a novel digital game to assess episodic memory intended to overcome the limitations of traditional methods, such as the cost of its administration, its intrusive character, the lack of early detection capabilities, the lack of ecological validity, the learning effect and the existence of confounding factors. Materials and Methods Our proposal is based on the gamification of the California Verbal Learning Test (CVLT) and it has been designed to comply with the psychometric characteristics of reliability and validity. Two qualitative focus groups and a first pilot experiment were carried out to validate the proposal. Results A more ecological, non-intrusive and better administrable tool to perform cognitive assessment was developed. Initial evidence from the focus groups and pilot experiment confirmed the developed game’s usability and offered promising results insofar its psychometric validity is concerned. Moreover, the potential of this game for the cognitive classification of senior adults was confirmed, and administration time is dramatically reduced with respect to pen-and-paper tests. Limitations Additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. Conclusion Initial evidence show that serious games can be used as an instrument to assess the cognitive status of senior adults, and even to predict the onset of mild cognitive impairments or Alzheimer’s diseaseThe present work has been funded by the government of Galicia (Xunta de Galicia, Spain) to cover the travel expenses to participants’ homes during the pilot experiment (Ref.: 2016/236)S

Ensemble and continual federated learning for classifcation tasks

Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real world problems, it is common to have a continual data stream, which may be non stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphonesOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has received financial support from AEI/FEDER (European Union) Grant Number PID2020-119367RB-I00, as well as the Consellería de Cultura, Educación e Universitade of Galicia (accreditation ED431G-2019/04, ED431G2019/01, and ED431C2018/29), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S

Dataset bias exposed in face verification

This is the peer reviewed version of the following article: López‐López, E., Pardo, X.M., Regueiro, C.V., Iglesias, R. and Casado, F.E. (2019), Dataset bias exposed in face verification. IET Biom., 8: 249-258, which has been published in final form at https://doi.org/10.1049/iet-bmt.2018.5224. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsMost facial verification methods assume that training and testing sets contain independent and identically distributed samples, although, in many real applications, this assumption does not hold. Whenever gathering a representative dataset in the target domain is unfeasible, it is necessary to choose one of the already available (source domain) datasets. Here, a study was performed over the differences among six public datasets, and how this impacts on the performance of the learned methods. In the considered scenario of mobile devices, the individual of interest is enrolled using a few facial images taken in the operational domain, while training impostors are drawn from one of the public available datasets. This work tried to shed light on the inherent differences among the datasets, and potential harms that should be considered when they are combined for training and testing. Results indicate that a drop in performance occurs whenever training and testing are done on different datasets compared to the case of using the same dataset in both phases. However, the decay strongly depends on the kind of features. Besides, the representation of samples in the feature space reveals insights into what extent bias is an endogenous or an exogenous factorThis work has received financial support from the Xunta de Galicia, Consellería de Cultura, Educación e Ordenación Universitaria (Accreditation 2016–2019, EDG431G/01 and ED431G/08, and reference competitive group 2014–2017, GRC2014/030), the European Union: European Social Fund (ESF), European Regional Development Fund (ERDF) and FEDER funds and (AEI/FEDER, UE) grant number TIN2017‐90135‐R. Eric López had received financial support from the Xunta de Galicia and the European Union (European Social Fund ‐ ESF)S

Synthesis and Electrocatalytic Properties of H2SO4-Induced (100) Pt Nanoparticles Prepared in Water-in-Oil Microemulsion

The increasing number of applications for shape-controlled metal nanoparticles (NPs) has led to the need for easy, cheap, and scalable methodologies. We report the synthesis of (100) preferentially oriented Pt NPs, with a particle size of 9 nm, by using a water-in-oil microemulsion method. The specific surface structure of the NPs is induced by the presence of H2SO4 in the water phase of the microemulsion. Interestingly, the results reported herein show how increasing amounts of H2SO4 lead to the formation of Pt NPs containing a larger amount of (100) sites on their surface. This preferential surface orientation is confirmed electrochemically by using the so-called hydrogen adsorption/desorption process. In addition, transmission electron microscopy measurements confirm the presence of cubic-like Pt NPs. Finally, the electrocatalytic properties of the Pt NPs are evaluated towards ammonia and CO electro-oxidations, which are (100) structure-sensitive reactions.This work has been financially supported by the MCINN-FEDER (Spain) (project CTQ 2010-16271), Generalitat Valenciana (project PROMETEO/2009/045) and in part by NASA-URC Grant No. NNX10AQ17A and NSF-NSEC Center for Hierarchical Manufactur-ing Grant No. CHM-CMMI-0531171. R. M-R is grateful to the Becas Iberoamérica, Santander Universidades-España 2012 and PR-LSAMP Bridge to Doctorate Fellowship programs