INVITED SPEAKER

Xianyi Zeng is a full professor (exceptional class – grade 2) at ENSAIT Textile Engineer School – University of Lille, France, and Director of the GEMTEX National Laboratory.

He has been an IEEE senior member since 2011 and led the Theme on Human-Machine Systems in GRAISyHM (regional association of Researchers on Automation) since 2013. He has had the French National Knight's title in the Order of the Academic Palms since 2019 and was the holder of the Innovation R&D Award from the France-China Committee, and the EU Key Innovation Team Award from the EU Innovation Radar, both in 2021. He was awarded Top-10 Innovation Leader of Overseas Chinese in Europe in 2022. In ENSAIT, he has been a leader of the Department of Fashion and Service Engineering since 2009.

Xianyi Zeng has published more than 160 papers in peer-reviewed international journals, presented more than 260 papers at international conferences, and supervised more than 40 PhD students. In addition, as a principal investigator, he has led three European projects (Asia-Link: 2004-2008), SMDTex – European Joint Doctorate Program on Textile Sustainable Design and Management (Erasmus Mundus Program: 2013 – 2021), FBD_BModel – Fashion big data and business model (H2020 Program: 2017-2021) and several national and regional research projects such as IOTFetMov (ANR Program: 2015 – 2019), Camille 3D (FUI Program: 2012 – 2015), SUCRE (ARCIR Program: 2013 – 2017) and industrial projects in France and Europe.

Title: Intelligent clothing for online monitoring of human health and well-being. If it is put to the medical sector

Abstract: 

In this presentation, we propose a series of principles for designing intelligent and connected garments for online human health monitoring, including textile/garment design, electronic devices integration, local decision support system development. These principles will permit to enhance product autonomy and intelligence level and fully integrate devices into textiles. The proposed garment design process can be more adapted to customized body shapes of the target population and is capable of selecting the most relevant fabrics and garment patterns for minimizing signal attenuation and improving wearer’s comfort. Also, the integrated physiological sensors are connected to a centralized microcontroller, on which an intelligent algorithm is implemented for filtering noises, extracting relevant features from measured signals and intelligently interacting with the cloud platform. Several specific applications (i.e. fetal movement monitoring, long COVID-19 symptom evaluation and general human health and fatigue evaluation) have been proposed to show how intelligent garments are integrated into the patient’s lifestyle for long-term continuous health care.