P. D. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 2013.

, PNI offers dead reckoning processor | EETE Analog, p.1, 2018.

A. Norrdine, Z. Kasmi, and J. Blankenbach, Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet, IEEE Sens. J, vol.16, issue.17, pp.6766-6773, 2016.

F. Chollet and H. Liu, A (not so) short introduction to Micro Electro Mechanical Systems, 2013.

M. Andreja?ic, Mems accelerometers, University of Ljubljana. Faculty for mathematics and physics, Department of physics, 2008.

P. D. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Earth Surface and Gravity Models, 2013.

B. Velay, Département Mesures physiques, Institut universitaire de technologie (IUT) de Saint Nazaire, 2010.

V. Apostolyuk, Theory and design of micromechanical vibratory gyroscopes, MEMS/NEMS, pp.173-195, 2006.

A. A. Trusov, Overview of MEMS Gyroscopes: History, Principles of Operations, Types of Measurements, 2011.

V. Renaudin, M. H. Afzal, and G. Lachapelle, New Method for Magnetometers Based Orientation Estimation, IEEE/ION Position, Location and Navigation Symposium, pp.348-356, 2010.

S. Chable and V. Renaudin, Couplage de mesures GPS et inertielles pour de la navigation pédestre dans les bâtiments, XYZ, vol.146, pp.27-32, 2016.

X. Zhang, Y. Li, P. Mumford, and C. Rizos, Allan Variance Analysis on Error Characters of MEMS Inertial Sensors for an FPGA-based GPS / INS System, Int. Symp. GPS/GNSS, pp.127-133, 2008.

P. D. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Chap. 6: Dead Reckoning, Attitude, and Height Measurement, 2013.

V. Renaudin and C. Combettes, Magnetic, acceleration fields and gyroscope quaternion (MAGYQ)-based attitude estimation with smartphone sensors for indoor pedestrian navigation, Sensors (Switzerland), vol.14, issue.12, pp.22864-22890, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01470333

J. W. Kim, H. J. Jang, D. Hwang, and C. Park, A step, stride and heading determination for the pedestrian navigation system, Journal of Global Positioning systems, vol.3, issue.2, pp.273-279, 2004.

A. R. Pratama and R. Hidayat, Smartphone-based pedestrian dead reckoning as an indoor positioning system, 2012 International Conference on System Engineering and Technology (ICSET), pp.1-6, 2012.

A. Brajdic and R. Harle, Walk detection and step counting on unconstrained smartphones, Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp.225-234, 2013.

M. Susi, V. Renaudin, and G. Lachapelle, Motion mode recognition and step detection algorithms for mobile phone users, Sensors (Switzerland), vol.13, issue.2, pp.1539-1562, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00878547

K. Lan and W. Shih, Using Simple Harmonic Motion to Estimate Walking Distance for Waist-mounted PDR, 2012 IEEE Wireless Communications and Networking Conference (WCNC), pp.2445-2450, 2012.

H. Wang, H. Lenz, A. Szabo, J. Bamberger, and U. D. Hanebeck, WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors, Navigation and Communication, pp.1-7, 2007.

I. Bylemans, M. Weyn, and M. Klepal, Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems, 2009 Third International Conference on Mobile Ubiquitous Computing, pp.113-118, 2009.

V. Renaudin, M. Susi, and G. Lachapelle, Step Length Estimation Using Handheld Inertial Sensors, Sensors, vol.12, issue.7, pp.8507-8525, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00878552

W. Zijlstra, Assessment of spatio-temporal parameters during unconstrained walking, European Journal of Applied Physiology, vol.92, issue.1, pp.39-44, 2004.

D. Alvarez, R. C. González, A. López, and J. C. Alvarez, Comparison of step length estimators from weareable accelerometer devices, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5964-5967, 2006.

J. Rose and J. G. Gamble, Human Walking, pp.1-75, 2006.

S. H. Shin, C. G. Park, J. W. Kim, H. S. Hong, and J. M. Lee, Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors, IEEE Sensors Applications Symposium, pp.1-5, 2007.

V. Renaudin, V. Demeule, and M. Ortiz, Adaptative pedestrian displacement estimation with a smartphone, International Conference on Indoor Positioning and Indoor Navigation, pp.1-9, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01470447

V. Renaudin, C. Combettes, and F. Peyret, Quaternion Based Heading Estimation with Handheld MEMS in Indoor Environments, pp.645-656, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01470469

S. Durand and F. Peyret, Localisation précise en temps réel par mesures de phases des signaux GNSS, Laboratoire Central des Ponts et Chaussées, p.103, 2004.

S. Durand and F. Peyret, Localisation précise en temps réel par mesures de phases des signaux GNSS, Laboratoire Central des Ponts et Chaussées, pp.21-26, 2004.

L. Morel and S. Durand, Cours de géodésie, Ecole Supérieure des Géomètres et Topographes, Chap, vol.8, 2013.

S. Bandi and D. Thalmann, Path finding for human motion in virtual environments, Computational Geometry, vol.15, pp.103-127, 2000.

N. Victor, Evaluation des déplacements piétons quotidiens : Application à la ville de Luxembourg, 2016.

S. Kammoun, F. Dramas, B. Oriolaand, and C. Jouffrais, Route selection algorithm for Blind pedestrian, ICCAS 2010, pp.2223-2228, 2010.

A. Helal, S. E. Moore, and B. Ramachandran, Drishti: an integrated navigation system for visually impaired and disabled, Proceedings Fifth International Symposium on Wearable Computers, pp.149-156, 2001.

T. S. Yusof, S. F. Toha, and H. M. Yusof, Path Planning for Visually Impaired People in an Unfamiliar Environment Using Particle Swarm Optimization, Procedia Computer Science, vol.76, pp.80-86, 2015.

S. Izumi, G. Kobayashi, and T. Yoshida, Route navigation method for disabled access GIS in consideration of abilities and psychologies, 2nd International Conference on Digital Information Management, pp.519-525, 2007.

R. W. Saaty, The analytic hierarchy process-what it is and how it is used, Mathematical Modelling, vol.9, pp.161-176, 1987.

J. Balata, Z. Mikovec, P. Bures, and E. Mulickova, Automatically generated landmark-enhanced navigation instructions for blind pedestrians, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp.1605-1612, 2016.

R. G. Golledge, J. R. Marston, J. M. Loomis, and R. L. Klatzky, Stated Preferences for Components of a Personal Guidance System for Nonvisual Navigation, Journal of visual impairment and blindness, vol.98, pp.135-147, 2004.

H. Holone, G. Misund, and H. Holmstedt, Users Are Doing It for Themselves: Pedestrian Navigation with User Generated Content, The 2007 International Conference on Next Generation Mobile Applications, Services and Technologies, pp.91-99, 2007.

J. Girres, Modèle d'estimation de l'imprécision des mesures géométriques de données géographiques, 2012.

, Géoportail, p.1, 2019.

, QGIS, p.12, 2019.

, Map Features -OpenStreetMap Wiki

J. Girres and G. Touya, Quality Assessment of the French OpenStreetMap Dataset, Transactions in GIS, vol.14, issue.4, pp.435-459, 2010.

, Sidewalks -OpenStreetMap Wiki

O. Petit, P. Billon, and J. Follin, Évaluation de la qualité des données OpenStreetMap sur la Sarthe et réflexion sur le processus de contribution, XYZ, vol.2, issue.131, pp.37-47, 2012.

, AutoCAD pour Mac et AutoCAD pour Windows Logiciel de CAO Autodesk

, OSM goes indoors: usages, tools and prospects

, Google Maps Platform | Tarifs et forfaits

C. S. Jensen, H. Lu, and B. Yang, Graph Model Based Indoor Tracking, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp.122-131, 2009.

I. Afyouni, C. Ray, and C. Claramunt, Spatial models for context-aware indoor navigation systems: A survey, Journal of Spatial Information Science, pp.85-123, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00731055

P. Gilliéron and B. Merminod, Personal navigation system for indoor applications, 11th IAIN world congress, pp.1-15, 2003.

J. Lategahn, T. Ax, and C. Röhrig, Multi hypothesis Kalman Filter for indoor pedestrian navigation based on topological maps, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp.607-612, 2016.

C. Gaisbauer and A. U. Frank, Wayfinding Model for Pedestrian Navigation, 11th AGILE International Conference on Geographic Information Science, pp.1-9, 2008.

W. G. Van-toll, A. F. Cook, and R. Geraerts, A navigation mesh for dynamic environments, Comput. Animat. Virtual Worlds, vol.23, issue.6, pp.535-546, 2012.

M. Kallmann and M. Kapadia, Navigation meshes and real-time dynamic planning for virtual worlds, ACM SIGGRAPH 2014 Courses on -SIGGRAPH '14, pp.1-81, 2014.

I. Afyouni, C. Ray, and C. Claramunt, Spatial models for context-aware indoor navigation systems: A survey, Journal of Spatial Information Science, issue.4, pp.85-123, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00731055

M. R. Mendonça, H. S. Bernardino, and R. F. Neto, Stealthy Path Planning Using Navigation Meshes, 2015 Brazilian Conference on Intelligent Systems (BRACIS), pp.31-36, 2015.

I. Borovikov, Navigation Graph Generation, 2011.

P. Hafner, T. Moder, M. Wieser, and T. Bernoulli, Evaluation of smartphone-based indoor positioning using different Bayes filters, International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1-10, 2013.

W. Van-toll, A. F. Cook, I. V. , and R. Geraerts, Navigation meshes for realistic multi-layered environments, IEEE International Conference on Intelligent Robots and Systems, pp.3526-3532, 2011.

W. Xu, L. Liu, S. Zlatanova, W. Penard, and Q. Xiong, A pedestrian tracking algorithm using gridbased indoor model, Automation in Construction, vol.92, pp.173-187, 2018.

T. Fetzer, F. Ebner, F. Deinzer, L. Köping, and M. Grzegorzek, On Monte Carlo smoothing in multi sensor indoor localisation, 2016 International Conference on Indoor Positioning and Indoor Navigation, pp.1-8, 2016.

T. Moder, K. Wisiol, P. Hafner, and M. Wieser, Smartphone-based Indoor Positioning Utilizing Motion Recognition, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1-8, 2015.

S. Kaiser and C. Lang, Detecting elevators and escalators in 3D pedestrian indoor navigation, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, pp.1-6, 2016.

J. Wang, A. Hu, X. Li, and Y. Wang, An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter, ISPRS Int. J. Geo-Information, vol.4, issue.4, pp.2638-2659, 2015.

S. Lee and K. Mase, Activity and location recognition using wearable sensors, IEEE Pervasive Computing, vol.1, pp.24-32, 2002.

I. Spassov, Algorithms for map-aided autonomous indoor pedestrian positioning and navigation, 2007.

N. Kroll, J. Michael, and S. Sebastian, Context-aware Indoor-Outdoor Detection for Seamless Smartphone Positioning, GEOProcessing 2016: The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services, pp.106-108, 2016.

P. Zhou, Y. Zheng, Z. Li, M. Li, and G. Shen, IODetector, Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems -SenSys '12, pp.1-14, 2012.

W. Wang, Q. Chang, Q. Li, Z. Shi, and W. Chen, Indoor-outdoor detection using a smart phone sensor, Sensors (Switzerland), vol.16, issue.10, pp.1-15, 2016.

H. Gao and P. D. Groves, Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone, Navigation, Journal of the Institute of Navigation, vol.65, issue.1, pp.99-116, 2018.

H. Jia, S. Su, W. Kong, H. Luo, and G. Shang, MobiIO: Push the limit of indoor/outdoor detection through human's mobility traces, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.197-202, 2014.

T. Martire, P. Nazemzadeh, A. Cristiano, A. Sanna, and D. Trojaniello, Indoor-Outdoor Detection using Head-mounted Color Light Sensors, GNB 2018 -Sixth National Congress of Bioingeneering, pp.1-4, 2018.

R. Sung, S. Jung, and D. Han, Sound based indoor and outdoor environment detection for seamless positioning handover, ICT Express, vol.1, issue.3, pp.106-109, 2015.

J. Krumm and R. Hariharan, Tempio: inside/outside classification with temperature, Second International Workshop on Man-Machine Symbiotic Systems, pp.241-250, 2004.

G. Shtar, B. Shapira, and L. Rokach, Clustering Wi-Fi fingerprints for indoor--outdoor detection, Wireless Networks, pp.1-19, 2018.

M. Wu, P. H. Pathak, and P. Mohapatra, Monitoring building door events using barometer sensor in smartphones, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing -UbiComp '15, pp.319-323, 2015.

V. Radu, P. Katsikouli, R. Sarkar, and M. K. Marina, A semi-supervised learning approach for robust indoor-outdoor detection with smartphones, Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems -SenSys '14, pp.1-15, 2014.

G. Louppe, Understanding Random Forests: From Theory to Practice, 2014.

, Understanding the Bias-Variance Tradeoff, p.11, 2018.

H. He and E. A. Garcia, Learning from imbalanced data, IEEE Trans. Knowl. Data Eng, vol.21, issue.9, pp.1263-1284, 2009.

J. Jenness and J. J. Wynne, Cohen's Kappa and classification table metrics 2.0: An ArcView 3. x extension for accuracy assessment of spatially explicit models, USGS Numbered Series, pp.1-86, 1363.

M. Bekkar, H. K. Djemaa, and T. A. Alitouche, Evaluation measures for models' assessment over imbalanced data sets, Journal of Information Engineering and Applications, vol.3, issue.10, pp.27-38, 2013.

R. Alejo, J. A. Antonio, R. M. Valdovinos, and J. H. Pacheco-sánchez, Assessments Metrics for Multi-class Imbalance Learning: A Preliminary Study, Pattern Recognition. MCPR 2013, vol.7914, pp.335-343, 2013.

J. A. Cox, C. D. James, and J. B. Aimone, A Signal Processing Approach for Cyber Data Classification with Deep Neural Networks, Procedia Computer Science, vol.61, pp.349-354, 2015.

A. L. Blum and P. Langley, Selection of relevant features and examples in machine learning, Artificial Intelligence, vol.97, issue.1-2, pp.245-271, 1997.

P. Preux, Fouille de données : Notes De Cours, 2011.

J. Tang, S. Alelyani, and H. Liu, Feature selection for classification: A review, Data Classification: Algorithms and Applications, pp.37-64, 2014.

T. N. Lal, O. Chapelle, J. Weston, and A. Elisseeff, Embedded Methods, Feature Extraction: Foundations and Applications, pp.137-165, 2006.

C. Duby and S. Robin, Institut national de recherche agronomique -Paris Grignon, pp.1-54, 2006.

S. M. Holland, Principal Components Analysis (PCA), pp.1-11, 2008.

P. W. Mausel, W. J. Kramber, and J. K. Lee, Optimum band selection for supervised classification of multispectral data, Photogramm. Eng. Remote Sensing, vol.56, issue.1, pp.55-60, 1990.

M. Morchid, R. Dufour, P. M. Bousquet, G. Linarès, and J. M. Torres-moreno, Feature selection using Principal Component Analysis for massive retweet detection, Pattern Recognit. Lett, vol.49, pp.33-39, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01319767

Q. Gu, Z. Li, and J. Han, Generalized Fisher Score for Feature Selection, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, pp.1-8, 2012.

J. R. Vergara and P. A. Estévez, A review of feature selection methods based on mutual information, Neural Computing and Applications, vol.24, issue.1, pp.175-186, 2014.

M. Elhoushi, S. Member, J. Georgy, A. Noureldin, S. Member et al., Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices for Indoor Pedestrian Navigation Using Portable Devices, IEEE Transactions on Instrumentation and Measurement, vol.65, issue.1, pp.208-221, 2016.

F. T. Alaoui, V. Renaudin, and D. Betaille, Points of interest detection for map-aided PDR in combined outdoor-indoor spaces, 2017 International Conference on Indoor Positioning and Indoor Navigation, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01741665

P. Senin, Literature Review on Time Series Indexing, pp.1-33, 2009.

, Signal envelope -MATLAB envelope -MathWorks France, p.6, 2018.

, Envelope Detection -MATLAB

A. Mueen and E. Keogh, Extracting Optimal Performance from Dynamic Time Warping, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining -KDD '16, pp.2129-2130, 2016.

H. Skutkova, M. Vitek, P. Babula, R. Kizek, and I. Provaznik, Classification of genomic signals using dynamic time warping, BMC Bioinformatics, vol.14, pp.1-7, 2013.

T. M. Rath and R. Manmatha, Lower-Bounding of Dynamic Time Warping Distances for Multivariate Time Series, pp.1-4, 2003.

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, pp.174-188, 2002.
DOI : 10.1109/78.978374
URL : http://www.cs.ubc.ca/~murphyk/Software/Kalman/ParticleFilterTutorial.pdf

J. Brossier and P. Amblard, Particulaire et communications numériques, SEE, École Nationale Supérieure d'Ingénieurs Electriciens de Grenoble (LIS/ENSIEG), 2004.

L. Bazzani, D. Bloisi, and V. Murino, A Comparison of Multi Hypothesis Kalman Filter and Particle Filter for Multi-target Tracking, Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp.1-8, 2009.

P. Bonnifait, J. Laneurit, C. Fouque, and G. Dherbomez, Multi-hypothesis map-matching using particle filtering, 16th World Congress for ITS Systems and Services, pp.1-8, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00445673

T. Willemsen, F. Keller, and H. Sternberg, A topological approach with MEMS in smartphones based on routing-graph, Indoor Positioning and Indoor Navigation (IPIN), pp.1-6, 2015.
DOI : 10.1109/ipin.2015.7346952

F. T. Alaoui, D. Betaille, and V. Renaudin, A multi-hypothesis particle filtering approach for pedestrian dead reckoning, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, pp.1-8, 2016.
DOI : 10.1109/ipin.2016.7743614
URL : https://hal.archives-ouvertes.fr/hal-01451509

P. Hafner, T. Moder, M. Wieser, and T. Bernoulli, Evaluation of Smartphone-based Indoor Positioning Using Different Bayes Filters, International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1-10, 2013.
DOI : 10.1109/ipin.2013.6817876

A. Selloum, Localisation multi-capteurs d'un véhicule routier sous contraintes cartographiques : Mise en oeuvre d'un filtre particulaire et d'une modélisation multi-voies de la route par des clothoïdes, Auscultation et Calcul Scientifique, 2010.

M. Widyawan, S. Klepal, and . Beauregard, A Backtracking Particle Filter for fusing building plans with PDR displacement estimates, 5th Workshop on Positioning, Navigation and Communication, pp.207-212, 2008.
DOI : 10.1109/wpnc.2008.4510376

H. Bao and W. Wong, An indoor dead-reckoning algorithm with map matching, 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp.1534-1539, 2013.
DOI : 10.1109/iwcmc.2013.6583784

, factoextra package Readme | R Documentation

, discretize function | R Documentation

M. Susi, Gait Analysis for Pedestrian Navigation Using MEMS Handheld Devices, 2012.

, RTKLIB: An Open Source Program Package for GNSS Positioning, p.13, 2019.

U. M. Fayyad and K. B. Irani, Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning, the Thirteenth International Joint Conference on Artificial Intelligence, pp.1022-1027, 1993.

D. Steinberg, Chapter 10: CART: Classification and Regression Trees, pp.179-201, 2009.

S. L. Crawford, Extensions to the CART algorithm, International Journal of Man-Machine Studies, vol.31, issue.2, pp.197-217, 1989.
DOI : 10.1016/0020-7373(89)90027-8

, Data Science Basics: An Introduction to Ensemble Learners, p.6, 2018.

W. Buntine, Decision Tree Induction Systems: A Bayesian Analysis, UAI'87 Proceedings of the Third Conference on Uncertainty in Artificial Intelligence, pp.109-127, 1989.

L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

A. Ben-hur and J. Weston, Data Mining Techniques for the Life Sciences, Methods in Molecular Biology (Methods and Protocols), vol.609, pp.223-239, 2010.

. Svm-|-mr and . Opengate, AI -Ch17 -Support vector machine, p.13, 2019.