FeetMe est une jeune startup du domaine médical qui cherche à résoudre le problème des troubles de la mobilité́. Fort de notre expérience dans l’analyse de la marche, nous développons des solutions technologiques (objet connecté et applications) pour accompagner les patients dans le suivi et la gestion de leur maladie chronique avec des bio marqueurs numériques et des outils de suivi des études en vie réelle pour les patients, les médecins et les groupes pharmaceutiques.
FeetMe connait une forte croissance. Un premier produit d’aide au diagnostic pour les professionnels de santé a déjà̀ été́ commercialisé et FeetMe prépare le lancement de la V2 à destination des patients. L’équipe a remporté de nombreux concours et aides publiques (Concours Mondial de l’Innovation, Création Développement, FUI notamment) et a réalisé une levée de fonds lui permettant d’assurer son développement.
FeetMeFeetMe is a digital health company developing innovative connected technologies and services to improve patient outcomes in functional care, track disease evolution, and optimize medication utilization.
The innovative technology from FeetMe allows gait and posture analysis in real-time and real-life conditions. The technology combines pressure sensors, motion sensors and learning algorithms to analyse patients’ functional capacity,as well as empower rehabilitation among sufferers of gait disorders.
FeetMe is growing quickly, its first product FeetMe Evaluation for diagnostics assistance for health professionals and for clinical research within pharma is on the market already. The company is preparing the launch of FeetMe Rehabilitation, a solution intended for home-based rehabilitation of patients suffering from walking difficulties.
It has formalized a collaboration with Novartis and AMGEN laboratories to improve themanagement of multiple sclerosis and osteoporosis.
ProductFeetMe Evaluation is a solution for ambulatory gait assessment that combines miniaturized pressure sensors, motion sensors and an embedded calculation power to allow real time gait parameters assessment
FeetMeRehabilitation is a solution for home-based rehabilitation. It combines a choice of clinically validated exercises a pair connected insoles for real type movement measurement and an application that provides patients real time feedback.
The functional and motor skills of patients with neuropathologies are assessedusing gait parameters. They are key indicators for determining the progression ofneurodegenerative disease. These walking parameters are essential in thetherapeutic decision making process. Currently, they are measured in movementanalysis laboratories and do not allow patient follow-up over time. There istherefore an interest in evaluating them in real life. FeetMe insoles meet this firstneed. However, all devices on the market based on inertial units fixed to the feetonly return parameters associated with the referential frame on its own foot. Estimating the relative position between both feet when embedded in insoles isnowadays a major technical challenge because technical constraints are very important. Walking parameters associated with the relative positions of both feetare key features to evaluate the highest level of motor impairments of patient. These metrics also have an important impact in preventing the risk of falls linkedto many neuropathology’s.
The overall project is divided into two parts: the measurement of distance from RF signals and the integration of this new measurement into an interlacedKalman filter that takes into account the relative position of the two feet. For the first part of the project, with support from the embedded system team, the student will develop models that links the RF signal to the distance measurement. Several estimation approaches will be studied for distance measurement: power measurement and arrival time measurement. One objective of this first part will also be to caracterize the requirements in terms of sensors accuracy on simulated data in order to evaluate the feasibility of integrating such solution in a real system. On the basis of this distance measurement, the second part of the project will be based on the fusion of the two Kalman filters (modelled by Markov chains and linear operators) with the integration of this new distance measurement. This fusion will also have to take into account: the Kalman updating time, the parameters and the probability density associated with each of the parameters. In the literature, several approaches to system fusion are proposed. However, the walk has a kinematics of flight time and stopping time that allows an optimization in calculation for data fusion and information sharing between insoles. These specificities will have to be taken into account in the development phase. In order to identify the most efficient fusion moments, a machine learning approach will also be proposed to the student according to the progress made.
The student will be in the environment of a startup and will interact a lot with thepeople who develop the embedded system.
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We are seeking a candidate with the following skills:
• Master’s Level Degree
• Strong interest and rigor in R&D
• Prior experience with a few of the following models: Logistic Regression, Linear Regression, , Hidden Markov Models, Conditional Random Fields
• Knowledge in RF signals or a strong interest to learn on that topic
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Strong analytical and quantitative problem-solving skills.
• Proficient in one or more programming languages such as Python, MATLAB, Java, R, C++
• A drive to learn and master new technologies and techniques.
• Good oral and written communications skills to interact with other developmentand applications engineers daily.