HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics

The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers’ support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers’ well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker’s models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers’ health information towards a successful risk management strategy for safe industrial Cobot environments.This work is also based upon work from COST Actions CA18106 supported by COST (European Cooperation in Science and Technology) and the Basque Government grants, IT1489-22, ELKARTEK21/109 and EUSK22/17

Assessment of exposure determinants and exposure levels by using stationary concentration measurements and a probabilistic near-field/far-field exposure model

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO 2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95 th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m 3 room, no local ventilation, and NF ventilation of 1.6 m 3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the exposure level was 12.5% of the OELV

Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management

Essential tremor (ET) is a highly prevalent neurological disorder characterized by action-induced tremors involving the hand, voice, head, and/or face. Importantly, hand tremor is present in nearly all forms of ET, resulting in impaired fine motor skills and diminished quality of life. To advance early diagnostic approaches for ET, automated handwriting tasks and magnetic resonance imaging (MRI) offer an opportunity to develop early essential clinical biomarkers. In this study, we present a novel approach for the early clinical diagnosis and monitoring of ET based on integrating handwriting and neuroimaging analysis. We demonstrate how the analysis of fine motor skills, as measured by an automated Archimedes’ spiral task, is correlated with neuroimaging biomarkers for ET. Together, we present a novel modeling approach that can serve as a complementary and promising support tool for the clinical diagnosis of ET and a large range of tremors.This work was supported in part by the Universidad del País Vasco/Euskal Herriko Unibertsitatea, the University of Cambridge, PPG 17/51 and GIU 092/19, the Basque government (Saiotek SA-2010/00028, ELEKIN, Engineering and Society and Bioengineering Research Groups, GIC18/136, and ELKARTEK 18/99, 20/81), ‘‘Ministerio de Ciencia e Innovación’’ (SAF201677758R), FEDER funds, DomusVi Foundation (FP18/76), and the government of Gipuzkoa (HELENA, SABRINA, DG18/14-23, DG19/29, DG20/25 projects). This work is also based upon the work from COST Actions CA18106 and CA15225, supported by COST (European Cooperation in Science and Technology)

Designing a human computation framework to enhance citizen-government interaction

Human computation or Human-based computation (HBC) is a paradigm that considers the design and analysis of information processing systems in which humans participate as computational agents performing small tasks and being orchestrated by a computer system. In particular, humans perform small pieces of work and a computer system is in charge of orchestrating their results. In this work, we want to exploit this potential to improve the take-up of e-service usage by citizens interacting with governments. To that end, we propose Citizenpedia, a human computation framework aimed at fostering citizen\u2019s involvement in the public administration. Cit-izenpedia is presented as a web application with two main components: the Question Answering Engine, where citizens and civil servants can post and solve doubts about e-services and public administration, and the Collaborative Procedure Designer, where citizens can collaborate with civil servants in the definition and improvement of new administrative procedures and e-services. In this work, we present the design and prototype of Citizenpedia and two evaluation studies conducted: the first one, a set of on-line surveys about the component\u2019s design, and the second one, a face-to-face user evaluation of the prototype. These evaluations showed us that the participants of the tests found the platform attractive, and pointed out several improvement suggestions regarding user experience of e-services

Citizenpedia: Simplifying citizens interaction with public administration

Governments are facing increasing expectations from citizens to deliver more innovative and responsive services. Digital technologies offers opportunities for more collaborative and participatory relationships across stakeholders to actively collaborate in the design of public services and participate in their delivery. In this work we present Citizenpedia, a software framework under development within the H2020 SIMPATICO project, that aims to involve citizens in improving e-services provided by public administration and simplifying their usage

Language Resources for a Bilingual Automatic Index System of Broadcast News in Basque and Spanish

Abstract: Automatic Indexing of Broadcast News is a developing research area of great recent interest [1]. This paper describes the development steps for designing an automatic index system of broadcast news for both Basque and Spanish. This application requires of appropriate Language Resources to design all the components of the system. Nowadays, large and well-defined resources can be found in most widely used languages, but there is a lot of work to do with respect to minority languages. Even if Spanish has much more resources than Basque, this work has parallel efforts for both languages. These two languages have been chosen because they are evenly official in the Basque Autonomous Community and they are used in many mass media of the Community including the Basque Public Radio and Television EITB [2]. 1

Development of Resources for a Bilingual Automatic Index System of Broadcast News in Basque and Spanish

The development of an automatic index system of broadcast news requires appropriate Video and Language Resources (LR) to design all the components of the system. Nowadays, large and well-defined resources can be found in most widely used languages (Informedia), but there is a lot of work to do with respect to minority languages. The main goal of this work is the design of resources in Basque and Spanish for the transcription of broadcast news. These two languages have been chosen because they are both official in the Basque Autonomous Community and they are used in the Basque Public Radio and Television EITB (EITB)

Location-based Services In Ubiquitous Computing Environments

This paper presents a framework for providing dynamically deployable services in ubiquitous computing settings. The framework attaches physical entities and spaces with application-specific services to support and annotate them. By using RFID-based tracking systems, it detects the locations of physical entities, such as people or things, and deploys services bound to the entities at proper computing devices near the locations of the entities. It enables locationbased services to be implemented as mobile agents and operated at stationary or mobile computing devices, which are at appropriate locations, even if the services do not have any location-information. The paper also describes a prototype implementation of the framework and several practical applications

An Extensible Architecture for the Integration of Remote and Virtual Laboratories in Public Learning Tools

Remote laboratories are software and hardware tools that allow students to remotely access real equipment located in universities. The integration of remote laboratories in learning tools (learning management systems, content man- agement systems, or personal learning environments) has been achieved to integrate remote laboratories as part of the learning curricula. A cross-institutional initiative called gateway4labs has been created to perform this integration extensible to multiple remote laboratories in multiple learning tools. This contribution focuses on describing this initiative and, in particular, how opening it to public systems (where users do not need to be registered) produces new technical and organizational challenges due to the public availability of labs. In addition, this contribution shows integrations of systems that were not previously addressed in this initiative, such as PhET or ViSH, as well as a new approach for integrating supported laboratories in external specifications such as the smart device one through OpenSocial