Publications
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A model-driven approach for prospective ergonomics: Application to ikigai robotics
Prospective Ergonomics requires building a vision of the future, which can be achieved empirically (e.g. analysing unmet needs) and/or creatively (e.g. creating future needs). We develop an alternative way of imagining the future, through a model-driven approach. Based on several developmental models, we provide a global picture of possible future(s) emphasising higher-ordered motivations and values […]
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Recovery and Restructuring of Fine and Coarse Soil Fractions as Earthen Construction Materials
Abstract: Excessive consumption of natural resources to meet the growing demands of building and infrastructure projects has put enormous stress on these resources. On the other hand, a significant quantity of soil is excavated for development activities across the globe and is usually treated as waste material. This study explores the potential of excavated soils […]
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Multi Objective Optimization of Human-Robot Collaboration: A Case Study in Aerospace Assembly Line
Collaborative robotics is becoming increasingly prevalent in industry 5.0, leading to a growing need to improve interactions and collaborations between humans and robots. However, the current approach to defining the sharing of responsibilities between humans and robots is empirical and uses the robot as an active fixture of parts, which is a sub-optimal method for […]
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An improved 3D skeletons UP-Fall dataset : enhancing data quality for efficient impact fall detection
Detecting impact where an individual makes contact with the ground within a fall event is crucial in fall detection systems, particularly for elderly care where prompt intervention can prevent serious injuries. The UP-Fall dataset, a key resource in fall detection research, has proven valuable but suffers from limitations in data accuracy and comprehensiveness. These limitations […]
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Improving Pain Classification using Spatio-Temporal Deep Learning Approaches with Facial Expressions
Pain management and severity detection are crucial for effective treatment, yet traditional self-reporting methods are subjective and may be unsuitable for non-verbal individuals (people with limited speaking skills). To address this limitation, we explore automated pain detection using facial expressions. Our study leverages deep learning techniques to improve pain assessment by analyzing facial images from […]
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Educational digital twins (EDT) to a sustainable future? Between promises and reality
The Sustainable Development Goals set by the United Nations in 2015 underline the urgency of sustainability in all sectors, starting with the industrial sector. The ambition of Industry 5.0 is to go beyond Industry 4.0, striving for a sustainable technological revolution centered on people (Barcellini, 2019; Julien & Martin, 2021). So-called “disruptive” industrial innovations are […]
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Proteus effect: how avatars influence the way we behave
Poster présenté au RJC en IHM 2024 sur le projet de thèse d’Anna Martin Coesel
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GSK-C2F Graph Skeleton Modelization for Action Segmentation and Recognition using a Coarse-to-Fine strategy
Locating the temporal boundaries of performing actions, especially in industry 5.0 context, poses significant challenges due to several factors. These include the complex industrial environment, the presence of similarities between inter-class actions, the significant variation in the execution of intra-class actions arising from the expertise levels of operators, and the under or over-representation of particular […]
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Designing Secure and Smart Supply Chains: A Roadmap
The supply chain (SC) comprises all the vital stages a product goes through to its final destination, forming a value chain. In this article, our primary focus is on integrating Internet of Things, artificial intelligence, and blockchain technology to design secure and smart SC systems.
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Contribution to the distribution supply chain’s performance through the use of digital technologies Case study: cold logistics chain
The development of the Internet of Things (IOT) has made it easier to obtain real-time data related to the supply chain for food distribution’s traceability management. In this study, we suggest digitally transforming the supply chain for seafood distribution. In order to provide better visibility of both traceability data and the parameters that need to […]
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Remaining useful life prediction with uncertainty quantification using evidential deep learning
Predictive Maintenance presents an important and challenging task in Industry 4.0. It aims to prevent premature failures and reduce costs by avoiding unnecessary maintenance tasks. This involves estimating the Remaining Useful Life (RUL), which provides critical information for decision makers and planners of future maintenance activities. However, RUL prediction is not simple due to the […]
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Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station
The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The […]
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