Massimiliano Zecca, Ph.D.

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New office, new adventures! 🤠

on April 11, 2017

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All is packed up now, as you can see in the picture. Just a bit over 3 years since I joined Loughborough University in 2014, it's time to leave my old W2.61 office and move to a new, exciting location! 😀 Starting from tomorrow (well, technically today, as it's just past 2AM now) these are my new contact info: Michael Pearson East, LP1.1.05 Sports Technology Institute Wolfson School of Mechanical, Electrical and Manufacturing Engineering Loughborough University Loughborough, Leics LE11 3TU, UK Tel: +44 (0)1509 227122 Sports tech, here I come! 😀

📝 Towards semiotically driven empirical studies of ballet as a communicative form 📝

This paper explores the use of dance as a way to communicate through movement, using classical ballet as an example. It examines how different approaches, such as linguistics, communication studies, and social semiotics, can be used to understand dance as a form of communication. The paper also discusses the use of technology, specifically motion capture technology, to track and study the physical movements of dance. It suggests that this approach can be used to better understand the relationship between movement and the meaning of dance, and can inform future research on the subject. The paper presents a specific method for studying ballet in this way and suggests how it can be applied to other forms of dance as well. You can read the paper on nature.com , and watch a couple of related videos on vimeo. Real-time FGD Concept video

📝 Classifying the skill level of laparoscopic surgeons on resource-constrained systems 📝

Great news today! Our paper " Novel low memory footprint DNN models for edge classification of surgeons’ postures " is being published on IEEE Embedded Systems Letters thanks to the joint efforts of our wonderful team: Alex, Terry, Marco, Maria Celvisia, Esther, and Luciano from Loughborough University, STMicroelectronics, University of Leicester, and University of Cambridge! This scientific study looks at ways to improve training for laparoscopic surgery and decrease injuries related to these procedures. The researchers used advanced computer programs called deep neural networks to help with this. However, these programs often require a lot of computer power, which can be a problem. The study introduces two new computer models that use less power and can be used on smaller devices, like those found in laparoscopic surgery. These models were tested on small processors and the results showed that they were effective in classifying surgical skill levels while also being

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