A. , N. Pan, W. Ip, C. Khayal, I. Et et al., Social fmri : Investigating and shaping social mechanisms in the real world, Pervasive and Mobile Computing, vol.7, issue.6, pp.643-659, 2011.

A. , B. E. Haskell, W. L. Herrmann, S. D. Meckes, N. Bassett-jr et al., 2011 compendium of physical activities : a second update of codes and met values. Medicine and science in sports and exercise, pp.431575-1581, 2011.

A. , A. Bosch, S. Marin-perianu, M. Marin-perianu, R. Et et al., Activity recognition using inertial sensing for healthcare, wellbeing and sports applications : A survey, Architecture of computing systems (ARCS), 2010 23rd international conference on, pp.1-10, 2010.

A. , M. Constandache, I. , R. Choudhury, and R. , Surroundsense : mobile phone localization via ambience fingerprinting, Proceedings of the 15th annual international conference on Mobile computing and networking, pp.261-272, 2009.

B. , L. Et, I. , and S. S. , Activity recognition from user-annotated acceleration data, Pervasive computing, pp.1-17, 2004.

Y. Bengio, Learning Deep Architectures for AI, Machine Learning, pp.1-127, 2009.
DOI : 10.1561/2200000006

B. , D. Co¸skunco¸-co¸skun, D. Et, P. , and F. , On-line context aware physical activity recognition from the accelerometer and audio sensors of smartphones, Ambient Intelligence, pp.205-220, 2014.

B. , D. Portet, F. Besacier, L. Et, T. et al., Recordme : A smartphone application for experimental collections of large amount of data respecting volunteer's privacy, Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services, pp.345-348, 2014.

B. , C. V. Koekkoek, K. T. Verduin, M. Kodde, R. Et et al., A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity, Biomedical Engineering IEEE Transactions on, issue.3, pp.44136-147, 1997.

P. J. Brown, J. D. Bovey, C. Et, and X. , Context-aware applications: from the laboratory to the marketplace, IEEE Personal Communications, vol.4, issue.5, pp.58-64, 1997.
DOI : 10.1109/98.626984

B. Bibliographie, W. Sundt, M. Dell, N. Chaudhri, R. Breit et al., Open data kit 2.0 : expanding and refining information services for developing regions, Proceedings of the 14th Workshop on Mobile Computing Systems and Applications, p.10, 2013.

C. , A. Et, H. , and G. , The systems hacker's guide to the galaxy energy usage in a modern smartphone, Proceedings of the 4th Asia-Pacific Workshop on Systems, p.5, 2013.

C. Aguilar, P. A. Boudy, J. Istrate, D. Dorizzi, B. Et et al., A Dynamic Evidential Network for Fall Detection, IEEE Journal of Biomedical and Health Informatics, vol.18, issue.4, pp.1103-1113, 2014.
DOI : 10.1109/JBHI.2013.2283055

URL : https://hal.archives-ouvertes.fr/hal-01285503

C. , G. Kotz, and D. , A survey of context-aware mobile computing research, 2000.

C. , B. Sawhney, N. Et, P. , and A. , Auditory context awareness via wearable computing, Energy, issue.600, p.40020, 1998.

C. , P. Della-mea, V. Di-gaspero, L. Menegon, D. Mischis et al., The context-aware browser, Intelligent Systems, issue.1, pp.2538-2585, 2010.

C. , J. Crowley, J. L. Dobson, S. Et, G. et al., Context is key, Communications of the ACM, vol.48, issue.3, pp.49-53, 2005.

C. , J. L. Coutaz, J. Rey, G. Et, R. et al., Perceptual components for context aware computing, UbiComp 2002 : Ubiquitous Computing, pp.117-134, 2002.

C. Cvetkovi´c, B. Kalu?a, B. Mili´cmili´-mili´c, R. Et, L. et al., Towards human energy expenditure estimation using smart phone inertial sensors, Ambient Intelligence, pp.94-108, 2013.

D. , M. Et, L. , and H. , Feature selection for classification, Intelligent Data Analysis, vol.1, pp.131-156, 1997.

D. , S. Das, B. Krishnan, N. C. Thomas, B. L. Et et al., Simple and complex activity recognition through smart phones, Intelligent Environments (IE), 2012 8th International Conference on, pp.214-221, 2012.

D. , A. K. Abowd, G. D. Et, S. , and D. , A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-computer interaction, pp.97-166, 2001.

D. , I. Reinhardt, A. Englert, F. Christin, D. Et et al., Do you hear what i hear ? using acoustic probing to detect smartphone locations, Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on, pp.1-9, 2014.

E. , N. Et, P. , and A. , Reality mining : sensing complex social systems. Personal and ubiquitous computing, pp.255-268, 2006.

F. , D. Et, F. , and J. , All gadget and no representation makes jack a dull environment, Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments, pp.155-160, 1998.

H. , M. A. Et, H. , and G. , Benchmarking attribute selection techniques for discrete class data mining. Knowledge and Data Engineering, IEEE Transactions on, vol.15, issue.6, pp.1437-1447, 2003.

H. , A. R. Et, R. , and E. M. , Visions : A computer system for interpreting scenes, Computer vision systems, vol.78, pp.303-334, 1978.

H. , C. Lerer, A. Anokwa, Y. Tseng, C. Brunette et al., Open data kit : tools to build information services for developing regions, Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, p.18, 2010.

H. , G. Deng, L. , Y. , D. Dahl et al., Deep neural networks for acoustic modeling in speech recognition : The shared views of four research groups, Signal Processing Magazine IEEE, issue.6, pp.2982-97, 2012.

H. , X. Nugent, C. Mulvenna, M. Mcclean, S. Scotney et al., Evidential fusion of sensor data for activity recognition in smart homes, Pervasive and Mobile Computing, vol.5, issue.3, pp.236-252, 2009.

H. , S. A. Gluhak, A. Et, T. , and R. , A survey on smartphonebased systems for opportunistic user context recognition, ACM Computing Surveys (CSUR), issue.3, p.4527, 2013.

I. , O. D. Kose, M. Et, E. , and C. , A review and taxonomy of activity recognition on mobile phones, BioNanoScience, vol.3, issue.2, pp.145-171, 2013.

J. , C. Bellik, Y. Et, B. , and Y. , A context-aware locomotion assistance device for the blind, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00261503

K. , N. Schiele, B. Schmidt, and A. , Recognizing context for annotating a live life recording. Personal and Ubiquitous Computing, pp.251-263, 2007.

K. , N. Blom, J. Dousse, O. Gatica-perez, D. Et et al., Towards rich mobile phone datasets : Lausanne data collection campaign, Proc. ICPS, 2010.

K. , J. R. Weiss, G. M. Et, M. , and S. A. , Activity recognition using cell phone accelerometers, ACM SigKDD Explorations Newsletter, vol.12, issue.2, pp.74-82, 2011.

L. , N. D. Miluzzo, E. Lu, H. Peebles, D. Choudhury et al., A survey of mobile phone sensing, Communications Magazine, issue.9, pp.48140-150, 2010.

L. Bibliographie, A. Bonastre, J. Fauve, B. G. Lee, K. Lévy et al., Alize 3.0-open source toolkit for state-of-the-art speaker recognition, INTERSPEECH, pp.2768-2772, 2013.

L. , V. B. Mella, O. Fohr, and D. , Speaker diarization using normalized cross likelihood ratio, INTERSPEECH, pp.1869-1872, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00163855

L. , J. Choudhury, T. Borriello, and G. , A practical approach to recognizing physical activities, Pervasive Computing, pp.1-16, 2006.

L. , H. Et, Y. , and L. , Toward integrating feature selection algorithms for classification and clustering. Knowledge and Data Engineering, IEEE Transactions on, vol.17, issue.4, pp.491-502, 2005.

L. , H. , Y. , J. Liu, Z. Lane et al., The jigsaw continuous sensing engine for mobile phone applications, Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp.71-84, 2010.

M. , L. Smith, D. Et, M. , and B. P. , Context awareness using environmental noise classification, INTERSPEECH, 2003.

M. , N. Et, S. , and C. , Location-aware information delivery withcommotion, Handheld and Ubiquitous Computing, pp.157-171, 2000.

M. , S. Et, S. , and B. , Using evidence theory for the integration of distributed databases, International Journal of Intelligent Systems, vol.12, issue.10, pp.763-776, 1997.

M. , E. Lane, N. D. Fodor, K. Peterson, R. Lu et al., Sensing meets mobile social networks : the design, implementation and evaluation of the cenceme application, Proceedings of the 6th ACM conference on Embedded network sensor systems, pp.337-350, 2008.

M. , E. Papandrea, M. Lane, N. D. Lu, H. Et et al., Pocket, bag, hand, etc.-automatically detecting phone context through discovery, Proc. PhoneSense 2010, pp.21-25, 2010.

P. , J. Patel, A. Curtis, D. Teller, S. Et et al., Online pose classification and walking speed estimation using handheld devices, Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp.113-122, 2012.

P. , V. Tuomi, J. Klapuri, A. Huopaniemi, J. Et et al., Computational auditory scene recognition, Acoustics, Speech, and Signal Processing (ICASSP) IEEE International Conference on, p.1941, 2002.

R. , N. Scott, J. Han, L. Et, I. et al., Context-aware battery management for mobile phones, Pervasive Computing and Communications Sixth Annual IEEE International Conference on, pp.224-233, 2008.

R. , S. Mun, M. Burke, J. Estrin, D. Hansen et al., Using mobile phones to determine transportation modes, ACM Transactions on Sensor Networks (TOSN), vol.6, issue.2, p.13, 2010.

S. , R. Et, A. , and R. , Scripts, plans, goals and understanding, 1977.

S. , B. Adams, N. Et, W. , and R. , Context-aware computing applications, Mobile Computing Systems and Applications, pp.85-90, 1994.

S. , M. Bosch, S. Incel, O. D. Scholten, H. Et et al., Fusion of smartphone motion sensors for physical activity recognition, Sensors, issue.6, pp.1410146-10176, 2014.

S. , P. Et, R. , and J. , Ready-to-use activity recognition for smartphones, Computational Intelligence and Data Mining (CIDM), 2013 IEEE Symposium on, pp.59-64, 2013.

S. , T. Varshavsky, A. Lamarca, A. Chen, M. Y. Choudhury et al., Mobility detection using everyday gsm traces, UbiComp 2006 : Ubiquitous Computing, pp.212-224, 2006.

V. , A. Pantic, M. Bourlard, and H. , Social signal processing : Survey of an emerging domain, Image and Vision Computing, vol.27, issue.12, pp.1743-1759, 2009.

W. , D. T. Rice, A. Beresford, and A. R. , Device analyzer : Understanding smartphone usage, Mobile and Ubiquitous Systems : Computing, Networking, and Services, pp.195-208, 2014.

W. , A. Jones, A. Hopper, and A. , A new location technique for the active office, Personal Communications IEEE, issue.5, pp.42-47, 1997.

W. , I. H. Et, F. , and E. , Data Mining : Practical machine learning tools and techniques, 2005.

Y. , Z. Chakraborty, D. Misra, A. Jeung, H. Et et al., Semantic activity classification using locomotive signatures from mobile phones, 2012.