ParcoursSenior addresses the issues of how to make outdoor walking of the elderly safer with personalized walking trails that best fit his/her walking capability in order to promote regular outdoor walking. The importance of mobility has triggered development of sensing technologies for walking monitoring including remote sensing and wearable systems. ParcoursSenior aims to fill the gap between collected information, walking capacity and walks to propose. Our goal is to develop a personalized digital twin that observes the outdoor walking effort of the elderly over time, learns his/her walking capacity and proposes safe and diverse trails that adapt to personal conditions. Specifically, ParcoursSenior aims at developing an integrated numerical chain of sensing-analytics-decision for outdoor walking monitoring and trail decision-aid. It relies on wearable sensors for monitoring key signals, machine learning techniques to turn sensor signals into the elderly’s walking capacity on different grounds, and decision-making tools to determine trails suitable for the health conditions with an appropriate combination of difficulties. The research is decomposed into four axes: - Pre-trail evaluation for developing a smart sensing system for pre-trail evaluation of the health condition and walking capability by wearable sensors, test protocols and machine learning algorithms; - Trail analytics to develop a smart sensing and analytics system to learn the capability of the elderly to walk different routes; - Trail decision-aid on multicriteria optimization of walking trails that are safe, diversified and adapted to the physical conditions of the elderly; - Utility and acceptance studies on users’ perceived utility & acceptance of the project results and potential improvements. The consortium has partial results on physical condition evaluation, mobility monitoring with WellBeNet, accompanying seniors on outdoor walks and a community-mapping tool for trail selection.

In the context of IoT (Internet-of-Things) where multiple objects are collecting and exchanging data using wireless technologies, we need to simultaneously ensure communication reliability in complex environments, ultra-low eco-energetic footprint, and sustainable operation to limit the impact of energy storage. OPV4COM is aiming at validating printed organic photovoltaic (OPV) solar modules as both energy harvesters from indoor light and data receivers for optical wireless communication (OWC), providing the next generation of IoT nodes efficient and sustainable operation. While the concept was proposed in 2015, crucial bottlenecks have to be addressed to validate the technology towards green IoT, as only ideal configurations based on short distances (<1m) have been explored. Yet, real indoor environments (smart home, smart factory, etc.) involve multiples obstacles and noise sources potentially associated with mobility scenarii. Our main objectives are therefore to validate the real performance of large-area inkjet-printed OPV modules through innovative technologic solutions to reach the optimal energy harvesting/data reception trade-off. Self-powered and mobile portable/wearable IoT devices will be considered as a relevant application area considering the flexibility of OPV devices. By systematically investigating the steady-state and dynamic responses of OPV cells and modules, by specifically modeling them and simulating their performance in complex IoT scenes, and by experimentally validating their operation in OWC channels, the project aims at filling the actual gaps towards realistic applications. The project, involving two academic laboratories (XLIM and IM2NP) and a technology transfer center (CISTEME), has already raised the interest of a major industrial actor of printed OPV modules for indoor IoT (Dracula Technologies, Valence, France), who will provide on-demand flexible OPV cells and modules to the partners towards the validation of the technology.

ENDOSIM is a research project in the field of predictive simulation and computer-aided medical interventions (CAMI). It focuses on the treatment of aortic aneurisms and valvular stenoses. In a previous ANR TecSan project (ANGIOVISION, ended in February 2013), the partners of the ENDOSIM project have developed operative assistance tools using augmented angio-navigation for the treatment of abdominal aortic aneurysms (AAA). The results demonstrated, on more than 20 patients, the accuracy of the patient-specific simulation approach. Based on these developments and results, the team aims to move forward and tackle the problem of predictive planning, in order to maximize the accuracy and reliability of two complex endovascular procedures: • the implantation of fenestrated stent-grafts for the treatment of thoraco-abdominal aneurysms, • the endovascular implantation of cardiac valves for the treatment of aortic stenoses. For these two minimally invasive procedures, atheromatous plaques are sources of numerous, unsolved so far, difficulties among which: navigability issues in the vicinity of the lesions, risk of plaque rupture due to ancillary contacts, complexity for positioning the device on the lesion site, brittleness of the vasculature, crushing of the native valves… These issues currently constitute a major obstacle for a more massive use of endovascular techniques. The goal of ENDOSIM is to develop the first predictive endovascular surgery planning software in the world. This will lead to optimize the pre-operative planning and to secure per-operative navigation, through the following points: • tool navigability estimation from the patient’s imaging data, • improvement of the pre-operative device sizing reliability, • pre-operative prediction of the device positioning and per-operative visualization, • decision-making help for patient eligibility and device selection. In order to reach these objectives, the novel approach featured in ENDOSIM relies upon the joint use of image analysis techniques and biomechanical numerical simulation techniques, both being patient-specific and predictive. The scientific breakthroughs of ENDOSIM comprise mainly accurate and predictive patient-specific simulations of the endovascular ancillary insertion and device deployment. These simulations will be based on pre-operative imaging data and validated using per- and post-operative data on a group of atheromatous patients. The prediction of the risk of surgery-induced injury at the atheromatous sites is also very original. The numerical simulations developed through the project will be systematically enhanced and validated thanks to 3D imaging data obtained on real patients with the per-operative multi-incidence equipment of the TherA-Image platform. From a clinical point of view, the benefits of the ENDOSIM project will relate to securing the surgical planning thanks to simulations based on pre-operative data and improved positioning accuracy thanks augmented navigation tools. This should allow a more massive use of endovascular treatments and hence make the most of these minimally invasive procedures for the patients. From the industrial point of view, ENDOSIM will lead Therenva® (French leader in endovascular surgery software) to market the first predictive endovascular planning software solution. This will also be complemented by a visualization system for per-operative assistance. The close partnership with Ansys® (worldwide leader in numerical simulation) will promote a widespread adoption of Therenva® software solutions by endovascular device companies, as a first step, and by the worldwide clinical community as a second step.

The rupture of an aortic aneurysm, which is often lethal, is a biomechanical phenomenon that occurs when the wall stress state exceeds the local strength of the tissue. Current understanding of arterial rupture mechanisms is poor, as the physics taking place at the microscopic scale in collagenous structures remains an open area of research. The interaction of the arterial constituents in the situation of mechanical load bearing, their impact on the mechanical properties and their role in arterial rupture are still open questions. This represents a significant limit for the use of numerical modelling approaches as a help for decision to the surgeons, as well as for the development of potential preventive treatments. _x000D_ Understanding, modelling, and quantifying the micro-mechanisms which drive the mechanical response of such tissue and locally trigger rupture represents the most challenging and promising pathway towards predictive diagnosis and personalized care of aortic aneurysm._x000D_ To develop a more detailed and comprehensive understanding of aneurysmal rupture, the PI's group developed an original in vitro experiment enabling to detect, in advance, at the macro-scale, rupture-prone areas in bulging ascending thoracic aortic aneurysmal tissues. These state-of-the-art results indicate that rupture occurs at a localized strain concentration. Accordingly, the next step is to examine the details of the arterial microstructure in order to elucidate the underlying mechanisms controlling the rupture response._x000D_ Through the achievements of AADaRP, an original experimental approach will be developed using multi-photon confocal microscopy combined with in situ bulging test of these aneurysmal tissues. This will enable to observe the tissue’s microstructure up to rupture. Then the local mechanical state of the fibrous microstructure of the tissue, especially close to its rupture state, will be numerically reconstructed to establish quantitative micro-scale rupture criteria. Based on these investigations and collected data, AADaRP will then address micro-macro modelling to build clinically-relevant biomechanical risk assessment models. _x000D_ The entire project will be completed through collaboration with medical doctors and engineers, experts in all required fields for the success of AADaRP, and used to working together in Saint-Etienne._x000D_ AADaRP is expected to open longed-for pathways for research in soft tissue mechanobiology which focuses on cell environment and to enable essential clinical applications for the quantitative assessment of AA rupture risk. It will significantly contribute to understanding fatal vascular events and improving cardiovascular treatments. It will provide a tremendous source of data and inspiration for subsequent applications and research by answering most fundamental questions on aortic aneurysm rupture behavior, enabling ground-breaking clinical changes to take place.

The role of Platelet Concentrate (PCs) in such adverse reactions (ARs) and low transfusion yield could be related to the inflammatory function of platelets. Their contribution to the haemostatic response, platelets are innate immunity cells that lead to pro-inflammatory events. This study aims to further understanding of PC proper use and misuse. This project consist to follow-up of the characteristics of the CPs, recipients, their associated pathology, and parameters related to the effectiveness of platelet transfusion with several approach: in vitro, in vivo and will be added with the EFS and clinical University Hospital Center of Saint-Etienne database. Methods of "machine learning" will be used in order to propose a PC delivery decision tree for a personalized transfusion. These optimization algorithms will allow to best matching patients and available platelet products in order to reduce the AR risk and increase platelet transfusion yield by taking into account features of both patients and platelet products. In fine, the objectives are to develop an automated blood unit selection model for the automated matching of personalized transfusion and improved demand prediction models to simultaneously improve inventory management practices and minimize wastage.