Human-centered User Interfaces for Automated Driving – (Un-)exploited Potentials

Designing user interfaces for (highly) automated driving is a complex task since users vary considerably regarding their needs and preferences. Therefore, a one-size-fits-all approach will not be sufficient for designing these interfaces. Thus, in this paper we aim to identify unexploited potentials in this area. We do so by performing a systematic literature review. Our contributions are 1) a systematization of human-centered user interface design for automated driving in four key aspects, 2) the research intensity per aspect, 3) the unexploited potential within each aspect and 4) the potentials of the relations between them. Concretely, current research lacks frameworks supporting the customization of the named interfaces based on user characteristics. Among others, personalization of displayed information shows unexploited potentials for acceptance and usability. Thus, we recommend future research to focus on human-centricity accounting for individual needs instead of the interface itself

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Artificial intelligence (AI)-enabled voice assistants (VAs) such as Amazon Alexa increasingly assist shopping decisions and exhibit empathic behavior. The advancement of empathic AI raises concerns about machines nudging consumers into purchasing undesired or unnecessary products. Yet, it is unclear how the machine’s empathic behavior affects consumer responses and decision-making outcomes during voice-enabled shopping. This article draws from the service robot acceptance model (sRAM) and social response theory (SRT) and presents an individual-session experiment where families (vs. individuals) complete actual shopping tasks using an ad-hoc Alexa app featuring high (vs. standard) empathic capabilities. We apply the experimental conditions as moderators to the structural model, bridging selected functional, social-emotional, and relational variables. Our framework collocates affective empathy, explicates the bases of consumers’ beliefs, and predicts behavioral outcomes. Findings demonstrate (i) an increase in consumers’ perceptions, beliefs, and adoption intentions with empathic Alexa, (ii) a positive response to empathic Alexa holding constant in family settings, and (iii) an interaction effect only on the functional model dimensions whereby families show greater responses to empathic Alexa while individuals to standard Alexa

Lean manufacturing e ergonomia na industria metalúrgica: uma abordagem integrada para a melhoria de desempenho

Due to an increasingly competive market, most companies can only survive through continuous improvement, by increasing their productivity and reducing costs. The Lean Production System (SPL) is more and more often used for this purpose.However, the workers' well-being is often neglected, leading to musculoskeletal problems and other occupational diseases. Several authors have identified a gap in the literature regarding the identification of the best practices in the integration of the prevention of musculoskeletal diseases in an SPL. The main objective of this thesis is to clarify the relationship between Ergonomics and LPS and provide the necessary tools for practitioners to implement an ergonomic LPS in their production areas. To achieve this objective, a systematic review was performed and case studies were conducted in four production areas in a metallurgical company using Lean concepts, ergonomic analysis and simulation. From the results found in the literature, which were validated by the four case studies, we can conclude that the integration of Ergonomics during an SPL implementation has the potential to result in gains in productivity and simultaneously improve working conditions. To potentiate these results, several components must be taken into account, namely: the integration of ergonomics in the design of the workstation, the tools for monitoring and evaluation, training and the automation of the manual tasks. Beyond the results obtained and the lessons learned from the case studies, two important tools were developed and validated which were a great support to the implementation of future studies in different areas or sectors: the methodology flowchart and ErgoSafeCI (a tool to evaluate and monitor the LPS implementation while taking into account the ergonomic and safety aspects of a production area). This work offers a valuable contribution for researchers and professionals because it demonstrates how the integration of ergonomics into an SPL increases productivity by providing the necessary tools which make it possible to replicate the procedure in other production areas or sectors.Atualmente, devido ao mercado cada vez mais competitivo, a maioria das empresas só sobrevive através da melhoria contínua, aumentando a produtividade e diminuindo os custos. O Sistema de Produção Lean (SPL) é cada vez mais usado com esse objetivo. No entanto, o bem estar dos trabalhadores é muitas vezes negligenciado, levando a problemas músculoesqueléticos e a outras doenças profissionais. Diversos autores identificam uma falha na literatura quanto à identificação das melhores práticas na integração da prevenção das doenças músculoesqueléticas num SPL. O objetivo principal desta tese é clarificar a relação entre a Ergonomia e um SPL e desenvolver as ferramentas necessárias para ajudar os profissionais na implementação de um SPL ergonómico nas suas áreas produtivas. Para atingir esse objetivo foi realizada uma revisão sistemática à literatura e foram desenvolvidos casos de estudo em quatro áreas produtivas numa empresa metalúrgica onde foram usados vários conceitos Lean, análises ergonómicas e a simulação. Através dos resultados encontrados na literatura e validados nos casos de estudo, concluímos que a integração da ergonomia durante a implementação de um SPL resulta em ganhos de produtividade e simultaneamente melhora as condições de trabalho. Para potenciar estes resultados, diversos fatores devem ser considerados, nomeadamente: a integração da ergonomia no desenho do posto trabalho, nas ferramentas de monitorização e avaliação, na formação e a automatização das tarefas manuais. Para além dos resultados obtidos através dos casos de estudo, e da identificação de algumas “best practices” através das lições aprendidas ao longo deste trabalho, foram ainda desenvolvidas e validadas duas ferramentas importantes no apoio à implementação de futuros estudos em diferentes áreas produtivas e setores: a ErgoSafeCI (ferramenta para avaliar e monitorizar a implementação de um SPL considerando os aspetos ergonómicos e de segurança numa área produtiva) e uma proposta de metodologia geral para abordar a questão da integração das práticas Lean com as práticas de ergonomia. Este trabalho apresenta um contributo, que se espera valioso, para investigadores e profissionais por demonstrar como a integração da ergonomia num SPL potencia a produtividade fornecendo as ferramentas necessárias para a replicação da metodologia proposta noutras áreas produtivas.Programa Doutoral em Engenharia e Gestão Industria

Continuous and automated data collection in migraine research - Extending the data collection capabilities of the Empatica E4

Migraine is a recurrent headache disorder that afflicts significant portions of the global population. There is no current cure and migraines are mainly managed through symptomatic medical treatments and manual biofeedback routines. Automated data collection and prediction of migraine attacks through machine learning could be viable approaches for helping migraineurs and for reducing the impact of migraines, both on a societal and an individual level. However, machine learning approaches require access to large amounts of high-quality real-time data for facilitating prompt and reliable prediction under everyday conditions and within useful timeframes. The Empatica E4 is an unobtrusive wearable sensor device that can satisfy these data collection needs, although not without flaws and shortcomings. Several studies have reported issues with E4 data collection, most regarding participant involvement and the logistical aspects of the collection process. On top of this, the native systems provided by Empatica for storing, retrieving, and utilizing collected data do not properly facilitate real-time data analysis or machine learning approaches. This project creates a flexible data collection solution based on the E4 for facilitating real-time prediction of migraine attacks. It incorporates features and elements for increasing user involvement and for maximizing the data collection potential of the E4. Additionally, the solution is integrated with the mSpider data storage platform, facilitating reliable and flexible data storage and retrieval options. The prototype system was tested on three potential end-users under everyday conditions over the course of 20 days. After the data collection period, each user attended a semi-structured interview. Testing and interview results show that the data collection capabilities of the prototype system are on-par with other similar systems, it offers stable data collection under everyday conditions, and it can store data in the mSpider system. However, the added features for increasing participant involvement had little discernible effect on the data collection process or the amount of collected data. This was probably caused by the low intensity of the added features or the short duration of the testing period. Additionally, the testing process found that the high technical proficiency requirements and the necessary daily maintenance of the E4 makes it unsuited for continuous migraine treatment purposes, although it is a good tool for migraine research. Future prototype iterations should increase the intensity of the participant involvement features and greatly increase the length of testing periods

Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2013 Annual Conference:Human Factors: sustainable life and mobility

On the occasion of the 2013 Meeting in Torino, Ital