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RICCARDO KARIM KHAMAISI

Assegnista di ricerca
INTERMECH Centro Interd. per la Ricerca Applicata e i Servizi nel settore della Meccanica Avanzata e della Motoristica
Docente a contratto
Dipartimento di Ingegneria "Enzo Ferrari"


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Pubblicazioni

2021 - A Reference Framework to Combine Model-Based Design and AR to Improve Social Sustainability [Articolo su rivista]
Grandi, Fabio; Khamaisi, Riccardo Karim; Peruzzini, Margherita; Raffaeli, Roberto; Pellicciari, Marcello
abstract

Product and process digitalization is pervading numerous areas in the industry to improve quality and reduce costs. In particular, digital models enable virtual simulations to predict product and process performances, as well as to generate digital contents to improve the general workflow. Digital models can also contain additional contents (e.g., model-based design (MBD)) to provide online and on-time information about process operations and management, as well as to support operator activities. The recent developments in augmented reality (AR) offer new specific interfaces to promote the great diffusion of digital contents into industrial processes, thanks to flexible and robust applications, as well as cost-effective devices. However, the impact of AR applications on sustainability is still poorly explored in research. In this direction, this paper proposed an innovative approach to exploit MBD and introduce AR interfaces in the industry to support human intensive processes. Indeed, in those processes, the human contribution is still crucial to guaranteeing the expected product quality (e.g., quality inspection). The paper also analyzed how this new concept can benefit sustainability and define a set of metrics to assess the positive impact on sustainability, focusing on social aspects.


2021 - Preliminary validation of a low-cost motion analysis system based on rgb cameras to support the evaluation of postural risk assessment [Articolo su rivista]
Agostinelli, T.; Generosi, A.; Ceccacci, S.; Khamaisi, R. K.; Peruzzini, M.; Mengoni, M.
abstract

This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and per-formed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool’s usefulness for ergonomist.


2021 - Ux in ar-supported industrial human–robot collaborative tasks: A systematic review [Articolo su rivista]
Khamaisi, R. K.; Prati, E.; Peruzzini, M.; Raffaeli, R.; Pellicciari, M.
abstract

The fourth industrial revolution is promoting the Operator 4.0 paradigm, originating from a renovated attention towards human factors, growingly involved in the design of modern, human-centered processes. New technologies, such as augmented reality or collaborative robotics are thus increasingly studied and progressively applied to solve the modern operators’ needs. Human-centered design approaches can help to identify user’s needs and functional requirements, solving usability issues, or reducing cognitive or physical stress. The paper reviews the recent literature on augmented reality-supported collaborative robotics from a human-centered perspective. To this end, the study analyzed 21 papers selected after a quality assessment procedure and remarks the poor adoption of user-centered approaches and methodologies to drive the development of human-centered augmented reality applications to promote an efficient collaboration between humans and robots. To remedy this deficiency, the paper ultimately proposes a structured framework driven by User eXperience approaches to design augmented reality interfaces by encompassing previous research works. Future developments are discussed, stimulating fruitful reflections and a decisive standardization process.