Keynote Speakers

MICROSCOPE is a French space mission aiming at testing the equivalence principle developed by CNES, the Observatory of Cote d’Azur and ONERA from 1999. At the foundation of the Einstein’s general relativity, the equivalence principle (EP) stipulates that all bodies are falling at the same rate in a uniform gravity field regardless of their mass or composition. Testing general relativity to its foundations is nothing more than finding the Grail of physics: the ultimate unification theory.

MICROSCOPE objective was to improve by two orders of magnitude the best current laboratory tests reaching barely 10-13. Launch in 2016, MICROSCOPE has delivered useful information for two and a half years. At the heart of the satellite, the scientific instrument developed by ONERA measured signals with a resolution better than 10-14m/s² at the particular frequency of the EP test. Apart from the scientific role, the instrument was also to feed the Drag-Free and Attitude Control System of the satellite to limit the environment accelerations to 10-13m/s² and the angular accelerations to 10-12rd/s². A first publication in 2017 confirmed at 10-14 the Einstein’s principle of equivalence with only 7% of the data available at that time. All the relevant data has been analysed and now provides the most accurate test of the equivalence principle: probably setting the test limit for the next decade.

Pending final results, the presentation will focus on the outstanding preliminary results of the MICROSCOPE mission, the first test in space of EP and will present the implications of these results for physics.

Indoor positioning and navigation have become a prerequisite for many applications and services. Currently available solutions are mainly based on radio beacon networks deployed in the infrastructure or a-priori mapping of signal characteristics. The first approach is costly and requires numerous calibrations, and the second assumes that the signals are stable over time, which is rarely true.

Given these limitations, the use of inertial signals offers the promise of a solution that requires no infrastructure deployment, no knowledge of the map, is functional without mobile cellular networks coverage and therefore accessible to a larger population. It has become the backbone of pedestrian localization systems but must be fused with other measures to compensate for its measurement errors. The inertial sensors embedded in wearable devices are first of low quality and they require continuous calibration “tricks”. Second, they measure many human motions, which are not those we are trying to estimate to track the human gait. In this context, what accuracy is achieved by the recent inertial signals processing methods? Can we measure complex human movements even when the wearable device is worn by the pedestrian on different parts of the body? Is the hybridization of inertial signals with other data still needed to ensure high accuracy? What is the best strategy to label time series of inertial data for learning human gait features with artificial intelligence: physics or human interpretation?

I will talk about recent developments in indoor pedestrian localization with inertial measurements, including artificial intelligence approaches, whether for professional applications where a 6.8m after 2’500m 3D accuracy on 3 indoor levels (ANR/DGA MALIN competition) was recently achieved or for the general public with wearables where learning issues to gain robustness in everyday life will be presented.”

Since the first acceleration sensors in the mid-80s, the market and technology of MEMS-based inertial sensors have developed enormously in just a few decades. Starting from first applications for automotive airbag systems, MEMS and especially inertial sensors have become key components in almost all aspects of modern life.

Safety and comfort functions in our cars, modern user interfaces in our cell phones, intelligent power management in wireless headphones, independent recognition of gymnastic exercises in fitness trackers, or the various monitor functions in robots, smart homes, or industry – all functions that would be inconceivable without MEMS inertial sensors.

This progress has been made possible by an impressively high speed of innovation – even compared to Moore´s Law for semiconductors.

The combined figure of merit (cFOM) of MEMS inertial sensors has steadily improved by several orders of magnitude in just a few years, illustrating that performance of those sensors is increasing drastically despite power consumption and footprint being significantly reduced.

Cross-domain innovation is a key success factor here. New approaches in manufacturing, design or the integration of intelligent software / AI enable leaps in innovation and continually enabling completely new use-cases.

The presentation gives an overview of the development of MEMS inertial sensors from the beginning until today and explains the progress in their development with recent examples.