1,721,030 research outputs found
Smart retrofitting for human factors: a face recognition-based system proposal
Full text linkIndustry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety
A Multi-Criteria Analysis Method in Algorithm-Driven Design
No full textThe study presents a new method based on generative design and multi-criteria analysis to select the best design option accounting for engineering performance, economic feasibility and other design goals (e.g. novelty, compliance). A comparison between topology optimization and generative design is proposed and discussed. The method is applied to the design of a rocker for racing cars
Evaluation of Deep Convolutional Neural Network Achitecture for Emotion Recognition in the Wild
No full textThis paper presents a software based on an innovative Convolutional Neural Network model to recognize the six Ekman's universal emotions from the photos of human faces captured in the wild. The CNN was trained using three different datasets already labeled and merged after making them homogeneous. A comparison among different types of CNN architectures using the Keras framework for Python language is proposed and the evaluation results are presente
The Role of Haptic Feedback and gamification in virtual museum systems
Full text linkThis paper reports the results of a research, aimed to evaluate the ability of a haptic interface to improve the user experience with virtual museum systems. In particular, two user studies have been carried out in order to: (1) determine similarities between visual and tactile experiences during manipulation of a 3D printed replica of an artefact with a pen like stylus and of a 3D reconstructed artefact using the considered haptic application and (2) compare the user’s perceived usability and user experience during the interaction with the haptic application interface, both gamified and not gamified, and with a mouse-based interface, based on the SUS scale and the AttrakDiff2 questionnaire. A total of 65 people were involved. The considered haptic application is based on the haptic device Omega 6 produced by Force Dimension and it is a permanent attraction of the “Museo Archeologico Nazionale delle Marche”. Results suggest that the proposed haptic interface is suitable for use by people familiar with mouse-based computer interaction, but without previous experience with haptic systems, and provide some insights useful to better understand the role of haptic feedback and gamification in enhancing user experience with Virtual Museums (VM), and to guide the development of other similar applications in the futur
Nudges-Based Design Method for Adaptive HMI to Improve Driving Safety
No full textThis study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people’s behavior and support people’s choices, to improve safety using emotional regulation techniques, through the management of environmental characteristics (e.g., temperature and illumination), according to an approach based on the nudging concept within a design thinking process. The proposed approach focuses on research in the field of behavioral psychology that has studied the correlations between human emotions and driving behavior, pushing towards the elicitation of those emotions judged to be most suitable for safe driving. The main objective is to support the ideation of scenarios and/or design features for adaptive HMIs to implement a nudging strategy to increase driving safety. At the end, the results from a collaborative workshop, organized as a case study to collect concept ideas in the context of sports cars, will be shown and evaluated to highlight the validity of the proposed methodology, but also the limitations due to the requirement of prototypes to evaluate the actual effectiveness of the presented nudging strategies
Synthesis of scheelite nanoparticles by mechanically assisted solid-state reaction of wolframite and calcium carbonate
No full textNanostructured scheelite (CaWO4) was synthesized by calcination in air of enriched wolframite (Fe1-xMnxWO4) ore and calcium carbonate (CaCO3). The effects of process parameters such as milling conditions of the solid reactants, calcination in flowing or static air, and use of stoichiometric excess of calcium carbonate on wolframite conversion into scheelite were studied by X-Ray Diffraction (XRD) and field emission gun scanning electron microscopy (FEG SEM). The intimate mixing and associated decrease in the diffusion path by high-energy planetary ball milling (PBM) were responsible for the conversion of most of wolframite into nanostructured scheelite after 2 h at 600 °C, with no need of calcium carbonate stoichiometric excess. Complete conversion of PBM wolframite:CaCO3 mixtures into nanosized scheelite, iron oxide and carbon dioxide was accomplished after 2 h at 700 °C. The nanostructured scheelite obtained from wolframite is expected to be significantly more reactive in subsequent treatments (e.g., leaching) for tungsten extraction
Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments
Full text linkThis study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further
The Effect of Immersive Audio Rendering on Listeners' Emotional State
No full textImmersive audio rendering techniques allow for generating a 3D scenario where the listener can perceive the sound from all directions. An important aspect of these approaches is the subjective perception of the listener and how these types of systems are perceived from the emotional point of view and how they can influence the listener's mood. In this context, a deep investigation of immersive sound perception considering subjective perception in terms of flowing emotion is performed. Starting from a 4-channels immersive audio system and an emotion-aware system based on the analysis of the user's facial expressions, several experiments have been performed to investigate a correlation between immersive perception and the listener's emotions
A toolkit for the automatic analysis of human behavior in HCI applications in the wild
Full text linkNowadays, smartphones and laptops equipped with cameras have become an integral part of our daily lives. The pervasive use of cameras enables the collection of an enormous amount of data, which can be easily extracted through video images processing. This opens up the possibility of using technologies that until now had been restricted to laboratories, such as eye-tracking and emotion analysis systems, to analyze users’ behavior in the wild, during the interaction with websites. In this context, this paper introduces a toolkit that takes advantage of deep learning algorithms to monitor user’s behavior and emotions, through the acquisition of facial expression and eye gaze from the video captured by the webcam of the device used to navigate the web, in compliance with the EU General data protection regulation (GDPR). Collected data are potentially useful to support user experience assessment of web-based applications in the wild and to improve the effectiveness of e-commerce recommendation systems
Emotion Recognition and Affective Computing
No full textThis chapter explores the challenging topic of emotion recognition by affective computing. The importance of considering and understanding people’s emotions in interaction design is discussed, focusing on the role of human emotions in the entire life cycle of human–system interaction as a means to innovate products and services. The measurement of emotions is also analyzed, including the classification of human emotions and recognition methods, as well as current techniques for measuring emotional responses. An emotional-based approach and related technologies are considered in managing the entire life cycle of human–system interaction as an innovation driver. This chapter also presents how to use affective computing in cross-transversal applications, concentrating on potential applications and different case studies. This chapter concludes with a look towards a world of emotional intelligence, where affective computing plays a crucial role in collecting and analyzing emotional data to support innovative product and service experiences
- …
