Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga

A decision support system for Rey-Osterrieth complex figure evaluation

Objective: The Rey Osterrieth complex figure (ROCF) is one of the most used neuropsychological tests for the assessment of mild cognitive impairment (MCI) and dementia. In the copy test, the patient has to draw a replica of a 18-pattern image and the outcome is a score based on the accuracy of the overall drawing. The standard scoring system however have limitations related to its subjective nature and its inability to evaluate other cognitive domains than constructional abilities. Previous works addressed those problems by proposing tablet-based automated evaluation systems. Even promising, such methods are still far away from clinical validation and translation. In this work, we developed a decision support system (DSS) for the evaluation of the ROCF copy test in the common practice using retrospective information from previously performed drawings. The goal of our system was to support the professionals providing a qualitative judgement for each of the 18 patterns, estimating the most probable diagnosis for the patient, and identifying the main signs associated to the obtained diagnosis. Methods: A total of 250 human evaluated ROCF copies were scanned from 57 healthy subjects, 131 individuals with MCI, and 62 individuals with dementia. The images were pre-processed and analysed using both computer vision and deep learning techniques to assign a qualitative label to the 18 patterns. Then, the 18 labels were used as features in 3 binary (healthy VS MCI, healthy VS dementia, MCI VS dementia) and a 3-class classifications with model explanation (SHAP).Results: Very good to excellent performance were obtained in all the diagnosis classification tasks. Indeed, an accuracy of about 85%, 91%, and 83% was obtained in discriminating healthy subjects from MCI, healthy subjects from dementia and MCI from dementia respectively. An accuracy of 73% was achieved in the 3-class classification. The model explanation showed which patterns are responsible for each prediction and how the importance of some patterns changes according to the severity of the cognitive decline. Significance: The proposed DSS enriches the standard evaluation and interpretation of the ROCF copy test. Being trained with retrospective knowledge, the performance of the DSS can be further enhanced by extending the dataset with existing ROCF copies

Pankkialan operatiiviset riskit tulevaisuudessa

Opinnäytetyön tarkoitus oli kartoittaa operatiivisten riskien tulevaisuuden näkymiä pankkitoiminnassa Suomessa. Opinnäytetyön tutkimuksellisena tavoitteena oli asiantuntijoiden avustuksella selvittää tulevaisuuden näkymiä riskimaailmassa. Kehittämistavoitteena oli tuottaa konkreettisia suuntaviivoja tulevaisuuden riskien näkökulmaan ja niiden hallinnointiin. Tietoperustassa tarkasteltiin finanssialaa pankkialan näkökulmasta. Riskin, riskienhallinnan ja operatiivisen riskin määritelmiä sekä tulevaisuuden ennakointia. Tutkimuksellisessa osiossa tarkoituksena oli tuottaa tutkittavasta aiheesta sellaista tietoa ja ymmärrystä, jota voidaan hyödyntää tulevaisuudessa operatiivisten riskien tunnistamisessa ja hallinnassa. Pääaineiston keruu toteutettiin Delfoi-menetelmällä antamalla viisi tulevaisuuden väittämää panelistien arvioitavaksi. Tutkimustulokset osoittivat, että tulevaisuuden ennakoinnin ja riskienhallinnan yhdistäminen on edelleen haastavaa. Digitalisaatio tuo omat haasteet riskienhallinnalle, henkilöstön erikoisosaaminen nousee kilpailuvaltiksi, vastuullisuuteen kiinnitetään entistä enemmän huomiota asiakkaiden näkökulmasta sekä pankkitoiminta koetaan edelleen ihmisten väliseksi luottamuskaupaksi eikä sitä suhdetta voi digitalisoida. Johtopäätöksenä todetaan, että esille nousee selkeästi neljä teemaa, joihin tulee riskienhallinnan tulevaisuuden näkökulmasta reagoida. Nämä ovat digitalisaatio ja palveluiden siirtyminen verkkoon tuo haasteita verkkoturvallisuuden kannalta, yhteistyön vahvistaminen eri toimijoiden välillä noussee kilpailuvaltiksi, riskienhallinnan fokusointi vaatii robotiikan ja osaavan henkilöstön yhdistelmää sekä vastuullinen yritystoiminta vahvistuu. Opinnäytetyön tuotoksena tunnistettiin neljä mahdollista tulevaisuuden riskiä ja niiden hallintakeinot. Tutkimus antaa kokonaiskuvan tulevaisuuden näkökulmista operatiivisten riskien tunnistamisessa ja hallinnoinnissa. Opinnäytetyö on hyödynnettävissä yleisesti organisaatioissa riskienhallinnan kehittämisen tukena.The purpose of this study is to examine the future of the banking industry in Finland from the perspective of operational risks. The aim of this research is to examine with the help of experts the future views in the risk industry and to create a better insight of how the future looks and how operational risks can be managed. The theory section focuses on the financial industry from the perspective of banking, future forecasts and the definition of risk, risk management and operational risk. In the research section the aim was to produce information about the topic that can be used for identification and manage-ment of operational risks in the future. The main research data acquisition was conducted using the Delfoi-method by providing five future propositions to be evaluated by chosen panelists. The results of the study showed that combining the future perspective and risk management is still challenging. Digitalization brings its own challenges to risk management, special expertise of the employees will become a competitive asset, customers will pay increasing attention to sustainability and banking will still be considered as a confidential relationship between people, which therefore cannot be digitalized. The conclusion was that there are four clear themes that must be responded from the future perspective of risk management. These themes are digitalization and services moving online, which brings challenges from the perspective of online security, open cooperation and sharing of information will become a competitive asset, focusing of risk management will require a combination of robotics and humans, and sustainable entrepreneurship will become stronger. As an outcome of this study, a concept of possible future operational risks was gained, as well as risk management methods. The study provides an overview into the perspectives of the future of operational risk identification and management, and is also publicly available to give support to be utilized in the development of risk management in organizations

Digitalized Cognitive Assessment mediated by a Virtual Caregiver

The ageing of the population deeply impacts on the social costs relative to health care. The use of modern technologies is one of the most promising approaches, under current study, to reduce such impact. In this demonstration, we propose a framework that can be employed for at-home assessment of Mild Cognitive Impairment (MCI). It is composed by a set of digitalized cognitive tests, developed from their paper-and-pencil counterparts, and by a Virtual Caregiver, which oversees the test execution and provides instructions.</jats:p

Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment

The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact in our aging society, by means of digitalized cognitive tests. In this work, we present a system where a specific set of cognitive tests is selected, digitalized, and integrated with a robotic assistant, whose task is the guidance and supervision of the users during the completion of such tests. The system is then evaluated by means of an experimental study involving potential future users, in order to assess its acceptability and identify key directions for technical improvements

A Multi-Actor Framework Centered around an Assistive Mobile Robot for Elderly People Living Alone

In a social and economic context characterized by a constantly aging population, the research for new technologies able to assist elderly people is getting more and more attention. In this extended abstract we illustrate the main components of the European project MoveCare, a multi-actor framework designed to assist pre-frail elders living alone. The proposed system is centered around an assistive mobile robot that provides the user with a set of functionalities to support cognitive and social stimulation, assistance, and transparent monitoring

Completing robot maps by predicting the layout of rooms behind closed doors

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly limiting the activities robots can perform. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building